diff examples/SMALL_test_coherence2.m @ 170:68fb71aa5339 danieleb

Added dictionary decorrelation functions and test script for Letters paper.

--- a/examples/SMALL_test_coherence2.m	Thu Sep 29 09:46:52 2011 +0100
+++ b/examples/SMALL_test_coherence2.m	Thu Oct 06 14:33:41 2011 +0100
@@ -1,13 +1,13 @@
 clc, clear, close all
 
 %% Parameteres
+nTrials   = 10;										%number of trials of the experiment
+
 % Dictionary learning parameters
 toolbox   = 'TwoStepDL';							%dictionary learning toolbox
-dicUpdate = {'ksvd'};								%dictionary learning updates
-dicDecorr = {'none','mailhe','tropp','barchiesi'};	%dictionary decorrelation methods
-minCoherence = linspace(0.1,1,1);					%coherence levels
-minCoherence = 0.4;
-%dicDecorr = {'barchiesi'};
+dicUpdate = 'ksvd';									%dictionary learning updates
+dicDecorr = {'iterproj','ink-svd','shrinkgram'};	%dictionary decorrelation methods
+minCoherence = linspace(0.1,1,10);					%coherence levels
 
 iterNum   = 20;				%number of iterations
 epsilon   = 1e-6;			%tolerance level
@@ -19,20 +19,16 @@
 blockSize = 256;						%size of audio frames
 overlap   = 0.5;						%overlap between consecutive frames
 
-
 % Dependent parameters
 nActiveAtoms = fix(blockSize/100*percActiveAtoms); %number of active atoms
 
 % Initial dictionaries
-dctDict = dictionary('dct',blockSize,dictSize);
-dctDict = dctDict.phi;
 gaborParam = struct('N',blockSize,'redundancyFactor',2,'wd',@rectwin);
 gaborDict = Gabor_Dictionary(gaborParam);
-initDicts = {[],dctDict,gaborDict};
-initDicts = {[]};
+initDicts = {[],gaborDict};
 
 %% Generate audio approximation problem
-signal			 = buffer(signal,blockSize,blockSize*overlap,@rectwin);
+signal			 = buffer(signal,blockSize,blockSize*overlap,@rectwin);	%buffer frames of audio into columns of the matrix S
 SMALL.Problem.b  = signal.S;
 SMALL.Problem.b1 = SMALL.Problem.b; % copy signals from training set b to test set b1 (needed for later functions)
 
@@ -42,20 +38,19 @@
 
 
 %% Test
-nDicUpdates = length(dicUpdate);		%number of dictionary updates
 nDecorrAlgs = length(dicDecorr);		%number of decorrelation algorithms
 nCorrLevels = length(minCoherence);		%number of coherence levels
 nInitDicts  = length(initDicts);		%number of initial dictionaries
 
-SMALL.DL(nInitDicts,nCorrLevels,nDecorrAlgs,nDicUpdates) = SMALL_init_DL(toolbox); %create dictionary learning structures
-for iInitDicts=1:nInitDicts
-	for iCorrLevels=1:nCorrLevels
-		for iDecorrAlgs=1:nDecorrAlgs
-			for iDicUpdates=1:nDicUpdates
-				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).toolbox = toolbox;
-				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).name = dicUpdate{iDicUpdates};
-				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).profile = true;
-				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).param = ...
+SMALL.DL(nTrials,nInitDicts,nCorrLevels,nDecorrAlgs) = SMALL_init_DL(toolbox); %create dictionary learning structures
+for iTrial=1:nTrials
+	for iInitDicts=1:nInitDicts
+		for iCorrLevels=1:nCorrLevels
+			for iDecorrAlgs=1:nDecorrAlgs
+				SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).toolbox = toolbox;
+				SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).name = dicUpdate;
+				SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).profile = true;
+				SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).param = ...
 					struct( 'data',SMALL.Problem.b,...
 					'Tdata',nActiveAtoms,...
 					'dictsize',dictSize,...
@@ -66,8 +61,9 @@
 					'coherence',minCoherence(iCorrLevels),...
 					'initdict',initDicts(iInitDicts));
 				
-				SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
-					SMALL_learn(SMALL.Problem,SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates));
+				SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs) = ...
+					SMALL_learn(SMALL.Problem,SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs));
+				save('SMALL_DL','SMALL');
 			end
 		end
 	end
@@ -75,23 +71,23 @@
 
 %% Evaluate coherence and snr of representation for the various methods
 sr = zeros(size(SMALL.DL));				%signal to noise ratio
-mu = zeros(size(SMALL.DL));				%coherence
+mu = zeros(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs,blockSize);	%cumulative coherence
 dic(size(SMALL.DL)) = dictionary;		%initialise dictionary objects
-for iInitDict=1:nInitDicts
-	for iCorrLevels=1:nCorrLevels
-		for iDecorrAlgs=1:nDecorrAlgs
-			for iDicUpdates=1:nDicUpdates
+for iTrial=1:nTrials
+	for iInitDicts=1:nInitDicts
+		for iCorrLevels=1:nCorrLevels
+			for iDecorrAlgs=1:nDecorrAlgs
 				%Sparse representation
-				SMALL.Problem.A = SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).D;
+				SMALL.Problem.A = SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).D;
 				tempSolver = SMALL_solve(SMALL.Problem,solver);
 				%calculate snr
-				sr(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
-					snr(SMALL.Problem.b,SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).D*tempSolver.solution);
+				sr(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs) = ...
+					snr(SMALL.Problem.b,SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).D*tempSolver.solution);
 				%calculate mu
-				dic(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
-					dictionary(SMALL.DL(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).D);
-				mu(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates) = ...
-					dic(iInitDicts,iCorrLevels,iDecorrAlgs,iDicUpdates).coherence;
+				dic(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs) = ...
+					dictionary(SMALL.DL(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).D);
+				mu(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs,:) = ...
+					dic(iTrial,iInitDicts,iCorrLevels,iDecorrAlgs).cumcoherence;
 			end
 		end
 	end
@@ -99,15 +95,15 @@
 
 %% Plot results
 minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1)));	%lowe bound on coherence
-initDictsNames = {'Data','DCT','Gabor'};						
-dicDecorrNames = {'K-SVD','INK-SVD','Grassmannian','New'};
-lineStyles     = {'ks-','kd-','ko-','k*-'};
+initDictsNames = {'Data','Gabor'};
+dicDecorrNames = {'Iter. Proj. + Rotation','INK-SVD','Iter. Proj.'};
+lineStyles     = {'ks-','kd-','ko-'};
 for iInitDict=1:nInitDicts
 	figure, hold on, grid on
 	title([initDictsNames{iInitDict} ' Initialisation']);
 	plot([1 1],[0 25],'k-');
 	for iDecorrAlgs=1:nDecorrAlgs
-		plot(mu(iInitDicts,:,iDecorrAlgs,1),sr(iInitDicts,:,iDecorrAlgs,1),...
+		plot(mu(1,iInitDict,:,iDecorrAlgs,1),sr(1,iInitDict,:,iDecorrAlgs),...
 			lineStyles{iDecorrAlgs});
 	end
 	plot([minMu minMu],[0 25],'k--')