comparison cpack/dml/lib/kern_analysis.pl @ 0:718306e29690 tip

commiting public release

+/* Part of DML (Digital Music Laboratory)
+	Copyright 2014-2015 Samer Abdallah, University of London
+	 
+	This program is free software; you can redistribute it and/or
+	modify it under the terms of the GNU General Public License
+	as published by the Free Software Foundation; either version 2
+	of the License, or (at your option) any later version.
+
+	This program is distributed in the hope that it will be useful,
+	but WITHOUT ANY WARRANTY; without even the implied warranty of
+	MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+	GNU General Public License for more details.
+
+	You should have received a copy of the GNU General Public
+	License along with this library; if not, write to the Free Software
+	Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301  USA
+*/
+
+:- module(kern_analysis, []).
+
+:- use_module(library(kerndata)).
+:- use_module(library(grammars)).
+:- use_module(library(compression)).
+:- use_module(library(swipe)).
+
+grammars:dataset_sequences(DS,Seqs) :-
+   kerndata:dataset_sequences(DS,Seqs).
+
+
+
+write_dataset_sequences(DS) :-
+   dataset_sequences(DS,SS),
+   with_stream_encoding(current_output,octet,
+      maplist(write_seq(notenums),SS)).
+
+write_dataset(Prep,DS) :-
+   dataset_sequences(DS,SS),
+   with_stream_encoding(current_output,octet,
+      maplist(write_seq(Prep),SS)).
+
+write_item(Prep,Item) :-
+   item_sequence(Item,Sequence),
+   write_seq(Prep,Sequence).
+
+write_seq(notenums,NS) :-
+   write_bytes(NS),
+   write_bytes([255]).
+
+write_seq(intervals,NS) :-
+   nth1(1,NS,N1),
+   nnums_ivals(NS,IS),
+   append(IS2,[end],IS),
+   maplist(plus(127),IS2,CS),
+   write_bytes([N1|CS]),
+   write_bytes([255]).
+
+ds_complexity(Method,Prep,DS,K) :-
+   complexity(Method,
+      write_dataset(Prep,DS),
+      K).
+
+ds_conditional_complexity(Method,Prep,Ref,DS,K) :-
+   conditional_complexity(Method,
+      write_dataset(Prep,Ref),
+      write_dataset(Prep,DS),
+      K).
+
+% ds_mutual_information(Method,Prep,DS1,DS2,I) :-
+%    mutual_information(Method,
+%       write_dataset(Prep,DS1),
+%       write_dataset(Prep,DS2),
+%       I).
+
+arg_min(K-Meth,Goal,KMin-Best) :-
+   aggregate_all(min(K,Meth),(Goal,Meth\=naive(_,_)),min(KMin,Best)).
+
+:- use_module(library(lambda)).
+:- use_module(library(mlserver)).
+:- use_module(library(uri)).
+
+exp(conditioning_scat(DS,Prep,Ref)) :-
+   dataset_items(DS,Items),
+   findall([KMin,CKMin], 
+      (  member(I,Items), 
+         min_complexity(_,write_item(Prep,I),KMin),
+         min_conditional_complexity(_,write_dataset(Prep,Ref), 
+                                      write_item(Prep,I),CKMin)
+      ), XX), 
+   ??scat(transpose(arr(XX))),
+   ??xlabel(q(complexity)),
+   ??ylabel(q('conditional complexity')).
+
+exp(relative_erasure(DS,Prep)) :-
+   dataset_items(DS,Items),
+   findall(CKRel, 
+      (  select(I,Items,Rest), 
+         min_complexity(_,write_item(Prep,I),KMin),
+         min_conditional_complexity(_,maplist(write_item(Prep),Rest), 
+                                              write_item(Prep,I),CKMin),
+         CKRel is 1- CKMin/KMin
+      ), XX), 
+   ??barh(XX),
+   ??xlabel(q('1 - relative erasure complexity')),
+   item_yticks(Items).
+
+exp(odd_one_out(Prep,DS,DS2,N)) :-
+   dataset_items(DS,Items),
+   dataset_items(DS2,Items2),
+   nth1(N,Items2,OddBall),
+   AllItems=[OddBall|Items],
+   findall(CKRel, 
+      (  select(I,AllItems,Rest), 
+         min_complexity(_,write_item(Prep,I),KMin),
+         min_conditional_complexity(_,maplist(write_item(Prep),Rest), 
+                                              write_item(Prep,I),CKMin),
+         CKRel is 1 - CKMin/KMin
+      ), XX), 
+   ??barh(XX),
+   ??xlabel(q('1 - relative erasure complexity')),
+   item_yticks(AllItems).
+
+exp(erasure(DS,Prep)) :-
+   dataset_items(DS,Items),
+   findall([KMin,CKMin], 
+      (  select(I,Items,Rest), 
+         min_complexity(_,write_item(Prep,I),KMin),
+         min_conditional_complexity(_,maplist(write_item(Prep),Rest), 
+                                      write_item(Prep,I),CKMin)
+      ), XX), 
+   ??barh(transpose(arr(XX))),
+   ??legend(cell([q(isolated),q(erasure)])),
+   ??xlabel(q(bits)),
+   item_yticks(Items).
+
+exp(compressibility(DS,Prep,Ref)) :-
+   dataset_items(DS,Items),
+   findall([CKMin,KMin], 
+      (  member(I,Items), 
+         % complexity(id,write_item(Prep,I),L), 
+         min_complexity(_,write_item(Prep,I),KMin),
+         min_conditional_complexity(_,write_dataset(Prep,Ref), 
+                                      write_item(Prep,I),CKMin)
+         % arg_min(K-C, conditional_complexity(naive(C),write_dataset(Prep,Ref),
+         %                                              write_item(Prep,I),K), NCK-_)
+      ), XX), 
+   ??barh(transpose(arr(XX))),
+   item_yticks(Items),
+   ??xlabel(q(bits)),
+   ??legend(cell([q('delta compressed'),q(compressed)])).
+
+exp(info_density(DS,Prep,Ref)) :-
+   dataset_items(DS,Items),
+   findall([H,HRef], 
+      (  member(I,Items), 
+         item_sequence(I,Pitches),
+         length(Pitches,L),
+         min_complexity(_,write_item(Prep,I),KMin),
+         min_conditional_complexity(_,write_dataset(Prep,Ref), 
+                                      write_item(Prep,I),CKMin),
+         H is KMin/L,
+         HRef is CKMin/L
+      ), XX), 
+   ??barh(transpose(arr(XX))),
+   ??legend(cell([q(isolated),q(conditional)])),
+   ??xlabel(q('bits per note')),
+   item_yticks(Items).
+
+exp(ds_similarity_matrix(Prep,Meth,Datasets)) :-
+   ncd_matrix(ncd(Meth), write_dataset(Prep), Datasets, Matrix),
+   ncd_image(Matrix),
+   yticks(Datasets).
+
+exp(similarity_matrix(Prep,Meth,DS)) :-
+   dataset_items(DS,Items),
+   ncd_matrix(ncd(Meth), write_item(Prep), Items, Matrix),
+   ncd_image(Matrix),
+   item_yticks(Items).
+
+exp(conditional_similarity_matrix(Prep,Method,DS,Ref)) :-
+   dataset_items(DS,Items),
+   ncd_matrix(conditional_ncd(Method,write_dataset(Prep,Ref)), write_item(Prep), Items, Matrix),
+   ncd_image(Matrix),
+   item_yticks(Items).
+
+exp(ds_conditional_similarity_matrix(Prep,Method,Datasets,Ref)) :-
+   ncd_matrix(conditional_ncd(Method,write_dataset(Prep,Ref)), write_dataset(Prep), Datasets, Matrix),
+   ncd_image(Matrix),
+   yticks(Datasets).
+
+ncd_image(Matrix) :- ??imagesc(1-arr(Matrix)), ??colorbar.
+
+ncd_matrix(Sim,Writer,Items,Matrix) :-
+   length(Items,N),
+   numlist(1,N,IX),
+   maplist(ncd_matrix_column(Sim,Writer,Items,IX),IX,Matrix).
+
+ncd_matrix_column(Sim,Writer,Items,JX,I,Column) :-
+   maplist(ncd_matrix_entry(Sim,Writer,Items,I),JX,Column).
+
+ncd_matrix_entry(Sim,Writer,Items,I,J,D) :-
+   (  I=J -> D=nan
+   ;  nth1(I,Items,XI),
+      nth1(J,Items,XJ),
+      call(Sim, call(Writer,XI), call(Writer,XJ), D)
+   ).
+
+item_yticks(Items) :-
+   length(Items,NumItems),
+   maplist(item_label,Items,Labels),
+   ??yticks(1:NumItems,cell(Labels)).
+
+item_label(Item,q(Name)) :-
+   item_uri(Item,URI),
+   uri_components(URI,Components),
+   uri_data(path,Components,Path),
+   directory_file_path(_,File,Path),
+   file_name_extension(Name,_,File).
+
+printfig(W,H,Name) :-
+   format(atom(Path),'/Users/samer/pubs/asymmus/workshop/figs/~w.eps',[Name]),
+   format(atom(Cmd),'epstopdf ~w',[Path]),
+   ??fsetup(W,H,q(centimeters)),
+   ??print(q('-depsc2'),q(Path)),
+   shell(Cmd).
+
+plot_method_rankings :-
+   setof( r(MeanRank,Method,Ranks),
+          ( compression:method_rankings(Method,Ranks),
+            mean_list(Ranks,MeanRank)),
+          Results),
+   ??newplot,
+   ??hold(q(on)),
+   forall( nth1(I,Results,r(_,_,Ranks)),
+           ??plot(Ranks,I+0.7*(rand(size(Ranks))-0.5),q('.'))),
+   ??hold(q(off)),
+   maplist(result_method,Results,Methods),
+   yticks(Methods).
+
+result_method(r(_,Method,_),Method).
+
+yticks(Terms) :-
+   length(Terms,N),
+   maplist(term_label,Terms,Labels),
+   ??yticks(1:N,cell(Labels)).
+
+term_label(Term,q(Label)) :- format(atom(Label),'~w',[Term]).
+
+
+mean_list(L,Mean) :-
+   length(L,N),
+   sumlist(L,Sum),
+   Mean is Sum/N.
+    
+
+% pairwise(DS,Matrix) :-
+%    dataset_items(DS,Items),
+%    maplist(\I1^maplist(\I2^conditional_complexity(
+
+
+% rnd_state(T) :- get_rnd_state(S), rnd_state_term(S,T).
+
+% with_sample(T,RV,Pred) :-
+%    rnd_state_term(S1,T),
+%    sample(RV,X,S1,S2),
+%    set_rnd_state(S2),
+%    call(Pred,X).
+