diff toolboxes/MIRtoolbox1.3.2/somtoolbox/som_make.m @ 0:e9a9cd732c1e tip

first hg version after svn

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/toolboxes/MIRtoolbox1.3.2/somtoolbox/som_make.m	Tue Feb 10 15:05:51 2015 +0000
@@ -0,0 +1,339 @@
+function sMap = som_make(D, varargin)
+
+%SOM_MAKE Create, initialize and train Self-Organizing Map.
+%
+% sMap = som_make(D, [[argID,] value, ...])
+%
+%  sMap = som_make(D);
+%  sMap = som_make(D, 'munits', 20);
+%  sMap = som_make(D, 'munits', 20, 'hexa', 'sheet');
+%  sMap = som_make(D, 'msize', [4 6 7], 'lattice', 'rect');
+%
+%  Input and output arguments ([]'s are optional): 
+%   D        (matrix) training data, size dlen x dim
+%            (struct) data struct
+%   [argID,  (string) See below. The values which are unambiguous can 
+%    value]  (varies) be given without the preceeding argID.
+%
+%   sMap     (struct) map struct
+%
+% Here are the valid argument IDs and corresponding values. The values 
+% which are unambiguous (marked with '*') can be given without the
+% preceeding argID.
+%   'init'       *(string) initialization: 'randinit' or 'lininit' (default)
+%   'algorithm'  *(string) training: 'seq' or 'batch' (default) or 'sompak'
+%   'munits'      (scalar) the preferred number of map units
+%   'msize'       (vector) map grid size
+%   'mapsize'    *(string) do you want a 'small', 'normal' or 'big' map
+%                          Any explicit settings of munits or msize override this.
+%   'lattice'    *(string) map lattice, 'hexa' or 'rect'
+%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'
+%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',
+%                          'ep' or 'bubble'
+%   'topol'      *(struct) topology struct
+%   'som_topol','sTopol' = 'topol'
+%   'mask'        (vector) BMU search mask, size dim x 1
+%   'name'        (string) map name
+%   'comp_names'  (string array / cellstr) component names, size dim x 1
+%   'tracking'    (scalar) how much to report, default = 1
+%   'training'    (string) 'short', 'default', 'long'
+%                 (vector) size 1 x 2, first length of rough training in epochs, 
+%                          and then length of finetuning in epochs
+%
+% For more help, try 'type som_make' or check out online documentation.
+% See also SOM_MAP_STRUCT, SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT,
+%          SOM_RANDINIT, SOM_LININIT, SOM_SEQTRAIN, SOM_BATCHTRAIN.          
+
+%%%%%%%%%%%%% DETAILED DESCRIPTION %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+% som_make
+%
+% PURPOSE
+%
+% Creates, initializes and trains a SOM using default parameters.
+%
+% SYNTAX
+%
+%  sMap = som_make(D);
+%  sMap = som_make(...,'argID',value,...);
+%  sMap = som_make(...,value,...);
+%
+% DESCRIPTION
+%
+% Creates, initializes and trains a SOM with default parameters. Uses functions
+% SOM_TOPOL_STRUCT, SOM_TRAIN_STRUCT, SOM_DATA_STRUCT and SOM_MAP_STRUCT to come
+% up with the default values.
+%
+% First, the number of map units is determined. Unless they are
+% explicitly defined, function SOM_TOPOL_STRUCT is used to determine this.
+% It uses a heuristic formula of 'munits = 5*dlen^0.54321'. The 'mapsize'
+% argument influences the final number of map units: a 'big' map has 
+% x4 the default number of map units and a 'small' map has x0.25 the
+% default number of map units. 
+%
+% After the number of map units has been determined, the map size is 
+% determined. Basically, the two biggest eigenvalues of the training
+% data are calculated and the ratio between sidelengths of the map grid
+% is set to this ratio. The actual sidelengths are then set so that 
+% their product is as close to the desired number of map units as
+% possible.
+%
+% Then the SOM is initialized. First, linear initialization along two
+% greatest eigenvectors is tried, but if this can't be done (the
+% eigenvectors cannot be calculated), random initialization is used
+% instead.  After initialization, the SOM is trained in two phases:
+% first rough training and then fine-tuning. If the 'tracking'
+% argument is greater than zero, the average quantization error and
+% topographic error of the final map are calculated.
+%
+% REQUIRED INPUT ARGUMENTS
+%
+%  D           The data to use in the training.
+%     (struct) A data struct. If a struct is given, '.comp_names' field as 
+%              well as '.comp_norm' field is copied to the map struct.
+%     (matrix) A data matrix, size dlen x dim. The data matrix may
+%              contain unknown values, indicated by NaNs. 
+%  
+% OPTIONAL INPUT ARGUMENTS 
+%
+%  argID (string) Argument identifier string (see below).
+%  value (varies) Value for the argument (see below).
+%
+% Here are the valid argument IDs and corresponding values. The values 
+% which are unambiguous (marked with '*') can be given without the
+% preceeding argID.
+%   'init'       *(string) initialization: 'randinit' or 'lininit' (default)
+%   'algorithm'  *(string) training: 'seq' or 'batch' (default) or 'sompak'
+%   'munits'      (scalar) the preferred number of map units
+%   'msize'       (vector) map grid size
+%   'mapsize'    *(string) do you want a 'small', 'normal' or 'big' map
+%                          Any explicit settings of munits or msize override this.
+%   'lattice'    *(string) map lattice, 'hexa' or 'rect'
+%   'shape'      *(string) map shape, 'sheet', 'cyl' or 'toroid'
+%   'neigh'      *(string) neighborhood function, 'gaussian', 'cutgauss',
+%                          'ep' or 'bubble'
+%   'topol'      *(struct) topology struct
+%   'som_topol','sTopol' = 'topol'
+%   'mask'        (vector) BMU search mask, size dim x 1
+%   'name'        (string) map name
+%   'comp_names'  (string array / cellstr) component names, size dim x 1
+%   'tracking'    (scalar) how much to report, default = 1
+%   'training'    (string) 'short', 'default' or 'long'
+%                 (vector) size 1 x 2, first length of rough training in epochs, 
+%                          and then length of finetuning in epochs
+%
+% OUTPUT ARGUMENTS
+% 
+%  sMap (struct) the trained map struct
+%
+% EXAMPLES
+%
+%  To simply train a map with default parameters: 
+%
+%   sMap = som_make(D); 
+%  
+%  With the optional arguments, the initialization and training can be
+%  influenced. To change map size, use 'msize', 'munits' or 'mapsize'
+%  arguments:  
+%
+%   sMap = som_make(D,'mapsize','big'); or sMap=som_make(D,'big');
+%   sMap = som_make(D,'munits', 100);
+%   sMap = som_make(D,'msize', [20 10]); 
+%
+%  Argument 'algorithm' can be used to switch between 'seq' and 'batch'
+%  algorithms. 'batch' is the default, so to use 'seq' algorithm: 
+%
+%   sMap = som_make(D,'algorithm','seq'); or sMap = som_make(D,'seq'); 
+%
+%  The 'tracking' argument can be used to control the amout of reporting
+%  during training. The argument is used in this function, and it is
+%  passed to the training functions. To make the function work silently
+%  set it to 0.
+%
+%   sMap = som_make(D,'tracking',0); 
+%
+% SEE ALSO
+% 
+%  som_map_struct   Create a map struct.
+%  som_topol_struct Default values for SOM topology.
+%  som_train_struct Default values for SOM training parameters.
+%  som_randinint    Random initialization algorithm.
+%  som_lininit      Linear initialization algorithm.
+%  som_seqtrain     Sequential training algorithm.
+%  som_batchtrain   Batch training algorithm.
+
+% Copyright (c) 1999-2000 by the SOM toolbox programming team.
+% http://www.cis.hut.fi/projects/somtoolbox/
+
+% Version 2.0beta juuso 111199
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% check arguments
+
+% D
+if isstruct(D) 
+  data_name = D.name; 
+  comp_names = D.comp_names;
+  comp_norm = D.comp_norm;
+  D = D.data;
+else 
+  data_name = inputname(1);
+  sDummy = som_data_struct(D(1,:)); 
+  comp_names = sDummy.comp_names;
+  comp_norm = sDummy.comp_norm;
+end
+[dlen dim] = size(D);
+
+% defaults
+mapsize = '';
+sM = som_map_struct(dim); 
+sTopol = sM.topol;
+munits = prod(sTopol.msize); % should be zero
+mask = sM.mask; 
+name = sM.name; 
+neigh = sM.neigh; 
+tracking = 1;
+algorithm = 'batch'; 
+initalg = 'lininit';
+training = 'default'; 
+
+% varargin
+i=1; 
+while i<=length(varargin), 
+  argok = 1; 
+  if ischar(varargin{i}), 
+    switch varargin{i}, 
+      % argument IDs
+     case 'mask',       i=i+1; mask = varargin{i}; 
+     case 'munits',     i=i+1; munits = varargin{i}; 
+     case 'msize',      i=i+1; sTopol.msize = varargin{i}; 
+                        munits = prod(sTopol.msize); 
+     case 'mapsize',    i=i+1; mapsize = varargin{i}; 
+     case 'name',       i=i+1; name = varargin{i};
+     case 'comp_names', i=i+1; comp_names = varargin{i}; 
+     case 'lattice',    i=i+1; sTopol.lattice = varargin{i};
+     case 'shape',      i=i+1; sTopol.shape = varargin{i}; 
+     case {'topol','som_topol','sTopol'}, 
+                        i=i+1; sTopol = varargin{i}; munits = prod(sTopol.msize); 
+     case 'neigh',      i=i+1; neigh = varargin{i};
+     case 'tracking',   i=i+1; tracking = varargin{i};
+     case 'algorithm',  i=i+1; algorithm = varargin{i}; 
+     case 'init',       i=i+1; initalg = varargin{i};
+     case 'training',   i=i+1; training = varargin{i}; 
+      % unambiguous values
+     case {'hexa','rect'}, sTopol.lattice = varargin{i};
+     case {'sheet','cyl','toroid'}, sTopol.shape = varargin{i}; 
+     case {'gaussian','cutgauss','ep','bubble'}, neigh = varargin{i};
+     case {'seq','batch','sompak'}, algorithm = varargin{i}; 
+     case {'small','normal','big'}, mapsize = varargin{i}; 
+     case {'randinit','lininit'}, initalg = varargin{i};
+     case {'short','default','long'}, training = varargin{i}; 
+     otherwise argok=0; 
+    end
+  elseif isstruct(varargin{i}) & isfield(varargin{i},'type'), 
+    switch varargin{i}(1).type, 
+     case 'som_topol', sTopol = varargin{i}; 
+     otherwise argok=0; 
+    end
+  else
+    argok = 0; 
+  end
+  if ~argok, 
+    disp(['(som_make) Ignoring invalid argument #' num2str(i+1)]); 
+  end
+  i = i+1; 
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% make the map struct
+
+%% map size
+if isempty(sTopol.msize) | ~prod(sTopol.msize), 
+  if tracking>0, fprintf(1,'Determining map size...\n'); end
+  if ~munits,     
+    sTemp = som_topol_struct('dlen',dlen);
+    munits = prod(sTemp.msize);
+    switch mapsize,
+     case 'small', munits = max(9,ceil(munits/4));
+     case 'big',   munits = munits*4;
+     otherwise % nil
+    end
+  end
+  sTemp = som_topol_struct('data',D,'munits',munits);
+  sTopol.msize = sTemp.msize;
+  if tracking>0, 
+    fprintf(1,' map size [%d, %d]\n',sTopol.msize(1), sTopol.msize(2));   
+  end
+end
+
+% map struct
+sMap = som_map_struct(dim,sTopol,neigh,'mask',mask,'name',name, ...
+                      'comp_names', comp_names, 'comp_norm', comp_norm); 
+       
+% function
+if strcmp(algorithm,'sompak'), 
+  algorithm = 'seq';
+  func = 'sompak';
+else
+  func = algorithm;
+end
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% initialization
+
+if tracking>0, fprintf(1,'Initialization...\n'); end
+
+switch initalg, 
+ case 'randinit', sMap = som_randinit(D, sMap);
+ case 'lininit', sMap = som_lininit(D, sMap); 
+end
+sMap.trainhist(1) = som_set(sMap.trainhist(1),'data_name',data_name);
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% training
+
+if tracking>0, fprintf(1,'Training using %s algorithm...\n',algorithm); end
+
+% rough train
+if tracking>0, fprintf(1,'Rough training phase...\n'); end
+sTrain = som_train_struct(sMap,'dlen',dlen,'algorithm',algorithm,'phase','rough');
+sTrain = som_set(sTrain,'data_name',data_name);
+if isnumeric(training), sTrain.trainlen = training(1); 
+else
+  switch training, 
+   case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4);
+   case 'long',  sTrain.trainlen = sTrain.trainlen*4;
+  end
+end
+switch func,
+ case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
+ case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
+ case 'batch',  sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
+end
+
+% finetune
+if tracking>0, fprintf(1,'Finetuning phase...\n'); end
+sTrain = som_train_struct(sMap,'dlen',dlen,'phase','finetune');
+sTrain = som_set(sTrain,'data_name',data_name,'algorithm',algorithm);
+if isnumeric(training), sTrain.trainlen = training(2); 
+else
+  switch training, 
+   case 'short', sTrain.trainlen = max(1,sTrain.trainlen/4);
+   case 'long',  sTrain.trainlen = sTrain.trainlen*4;
+  end
+end
+switch func,
+ case 'seq',    sMap = som_seqtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
+ case 'sompak', sMap = som_sompaktrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
+ case 'batch',  sMap = som_batchtrain(sMap,D,sTrain,'tracking',tracking,'mask',mask);
+end
+
+% quality
+if tracking>0, 
+  [mqe,tge] = som_quality(sMap,D);
+  fprintf(1,'Final quantization error: %5.3f\n',mqe)
+  fprintf(1,'Final topographic error:  %5.3f\n',tge)
+end  
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+