Mercurial > hg > smallbox
diff examples/SMALL_test_coherence.m @ 169:290cca7d3469 danieleb
Added dictionary decorrelation functions and test script for ICASSP paper.
| author | Daniele Barchiesi <daniele.barchiesi@eecs.qmul.ac.uk> |
|---|---|
| date | Thu, 29 Sep 2011 09:46:52 +0100 |
| parents | 8324c7ea6602 |
| children |
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--- a/examples/SMALL_test_coherence.m Tue Sep 20 15:52:33 2011 +0100 +++ b/examples/SMALL_test_coherence.m Thu Sep 29 09:46:52 2011 +0100 @@ -18,8 +18,8 @@ % Dependent parameters nActiveAtoms = fix(blockSize/100*percActiveAtoms); %number of active atoms epsilon = 1/sigma; %error constraint for sparse representation step (corresponds to noise applied to signals) -%minCoherence = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); %target coherence (based on coherence lower bound) -minCoherence = 0.4; %target coherence +minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); %target coherence (based on coherence lower bound) +minCoherence = [0.1:0.1:1]; % Initial dictionaries dctDict = dictionary('dct',blockSize,dictSize); @@ -29,7 +29,7 @@ %% Generate audio denoising problem with low noise (audio representation) SMALL.Problem = generateAudioDenoiseProblem(signal.s,[],blockSize,... - dictSize,overlap,sigma); % generate representation problem + dictSize,overlap,sigma); % generate representation problem SMALL.Problem.b1 = SMALL.Problem.b; % copy signals from training set b to test set b1 (needed for later functions) % omp2 sparse representation solver @@ -37,147 +37,144 @@ solver = SMALL_init_solver('ompbox','omp2',ompParam,false); %solver structure -%% Test ksvd dictionary update +%% Test name = dicUpdate{1}; %use ksvd update -SMALL.DL(1:9) = SMALL_init_DL(toolbox,name); %create dictionary learning structures +SMALL.DL(1:36) = SMALL_init_DL(toolbox,name); %create dictionary learning structures % learn with random initialisation and no decorrelation SMALL.DL(1).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','none'); %parameters for the dictionary learning + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none'); %parameters for the dictionary learning SMALL.DL(1) = SMALL_learn(SMALL.Problem,SMALL.DL(1)); %learn dictionary +%save('SMALL','SMALL'); % learn with random initialisation and mailhe decorrelation -SMALL.DL(2).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','mailhe','coherence',minCoherence); %parameters for the dictionary learning -SMALL.DL(2) = SMALL_learn(SMALL.Problem,SMALL.DL(2)); %learn dictionary - -% learn with random initialisation and tropp decorrelation -SMALL.DL(3).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning -SMALL.DL(3) = SMALL_learn(SMALL.Problem,SMALL.DL(3)); %learn dictionary - -% Learn with dct initialisation and no decorrelation -SMALL.DL(4).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','none','initdict',dctDict); %parameters for the dictionary learning -SMALL.DL(4) = SMALL_learn(SMALL.Problem,SMALL.DL(4)); %learn dictionary - -% learn with dct initialisation and mailhe decorrelation -SMALL.DL(5).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','mailhe','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning -SMALL.DL(5) = SMALL_learn(SMALL.Problem,SMALL.DL(5)); %learn dictionary - -% learn with dct initialisation and tropp decorrelation -SMALL.DL(6).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning -SMALL.DL(6) = SMALL_learn(SMALL.Problem,SMALL.DL(6)); %learn dictionary - -% Learn with gabor initialisation and no decorrelation -SMALL.DL(7).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning -SMALL.DL(7) = SMALL_learn(SMALL.Problem,SMALL.DL(7)); %learn dictionary - -% learn with gabor initialisation and mailhe decorrelation -SMALL.DL(8).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','mailhe','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning -SMALL.DL(8) = SMALL_learn(SMALL.Problem,SMALL.DL(8)); %learn dictionary - -% learn with gabor initialisation and tropp decorrelation -SMALL.DL(9).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning -SMALL.DL(9) = SMALL_learn(SMALL.Problem,SMALL.DL(9)); %learn dictionary - -%% Test mailhe dictionary update -name = dicUpdate{2}; %use mailhe update -SMALL.DL(10:18) = SMALL_init_DL(toolbox,name); %create dictionary learning structure - -% learn with random initialisation and no decorrelation -SMALL.DL(10).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','none'); %parameters for the dictionary learning -SMALL.DL(10) = SMALL_learn(SMALL.Problem,SMALL.DL(10)); %learn dictionary - -% learn with random initialisation and mailhe decorrelation -SMALL.DL(11).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','mailhe','coherence',minCoherence); %parameters for the dictionary learning -SMALL.DL(11) = SMALL_learn(SMALL.Problem,SMALL.DL(11)); %learn dictionary +for iMu=1:10 + SMALL.DL(1+iMu).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence(iMu)); %parameters for the dictionary learning + SMALL.DL(1+iMu) = SMALL_learn(SMALL.Problem,SMALL.DL(1+iMu)); %learn dictionary + %save('SMALL','SMALL'); +end % learn with random initialisation and tropp decorrelation SMALL.DL(12).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence); %parameters for the dictionary learning SMALL.DL(12) = SMALL_learn(SMALL.Problem,SMALL.DL(12)); %learn dictionary % Learn with dct initialisation and no decorrelation SMALL.DL(13).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','none','initdict',dctDict); %parameters for the dictionary learning + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none','initdict',dctDict); %parameters for the dictionary learning SMALL.DL(13) = SMALL_learn(SMALL.Problem,SMALL.DL(13)); %learn dictionary % learn with dct initialisation and mailhe decorrelation -SMALL.DL(14).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','mailhe','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning -SMALL.DL(14) = SMALL_learn(SMALL.Problem,SMALL.DL(14)); %learn dictionary +for iMu=1:10 + SMALL.DL(13+iMu).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence(iMu),'initdict',dctDict); %parameters for the dictionary learning + SMALL.DL(13+iMu) = SMALL_learn(SMALL.Problem,SMALL.DL(13+iMu)); %learn dictionary + %save('SMALL','SMALL'); +end % learn with dct initialisation and tropp decorrelation -SMALL.DL(15).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning -SMALL.DL(15) = SMALL_learn(SMALL.Problem,SMALL.DL(15)); %learn dictionary +SMALL.DL(24).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence,'initdict',dctDict); %parameters for the dictionary learning +SMALL.DL(24) = SMALL_learn(SMALL.Problem,SMALL.DL(24)); %learn dictionary + % Learn with gabor initialisation and no decorrelation -SMALL.DL(16).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning -SMALL.DL(16) = SMALL_learn(SMALL.Problem,SMALL.DL(16)); %learn dictionary +SMALL.DL(25).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','none','initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(25) = SMALL_learn(SMALL.Problem,SMALL.DL(25)); %learn dictionary % learn with gabor initialisation and mailhe decorrelation -SMALL.DL(17).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','mailhe','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning -SMALL.DL(17) = SMALL_learn(SMALL.Problem,SMALL.DL(17)); %learn dictionary +for iMu=1:10 + SMALL.DL(25+iMu).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','mailhe','coherence',minCoherence(iMu),'initdict',gaborDict); %parameters for the dictionary learning + SMALL.DL(25+iMu) = SMALL_learn(SMALL.Problem,SMALL.DL(25+iMu)); %learn dictionary + %save('SMALL','SMALL'); +end % learn with gabor initialisation and tropp decorrelation -SMALL.DL(18).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... - 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... - 'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning -SMALL.DL(18) = SMALL_learn(SMALL.Problem,SMALL.DL(18)); %learn dictionary +SMALL.DL(36).param = struct('data',SMALL.Problem.b,'Tdata',nActiveAtoms,... + 'dictsize',dictSize,'iternum',iterNum,'memusage','high','solver',solver,... + 'decFcn','tropp','coherence',minCoherence,'initdict',gaborDict); %parameters for the dictionary learning +SMALL.DL(36) = SMALL_learn(SMALL.Problem,SMALL.DL(36)); %learn dictionary %% Evaluate coherence and snr of representation for the various methods -sigNoiseRatio = zeros(18,1); -mu = zeros(18,1); -for i=1:18 - SMALL.Problem.A = SMALL.DL(i).D; - tempSolver = SMALL_solve(SMALL.Problem,solver); - sigNoiseRatio(i) = snr(SMALL.Problem.b,SMALL.DL(i).D*tempSolver.solution); - dic(i) = dictionary(SMALL.DL(i).D); - mu(i) = dic(i).coherence; +sigNoiseRatio = zeros(36,1); +mu = zeros(36,1); +for i=1:36 + SMALL.Problem.A = SMALL.DL(i).D; + tempSolver = SMALL_solve(SMALL.Problem,solver); + sigNoiseRatio(i) = snr(SMALL.Problem.b,SMALL.DL(i).D*tempSolver.solution); + dic(i) = dictionary(SMALL.DL(i).D); + mu(i) = dic(i).coherence; end %% Plot results minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); -maxSNR = max(sigNoiseRatio); -figure, subplot(1,2,1) -snrMat = buffer(sigNoiseRatio(1:9),3); -bar(snrMat'); -title('Signal to noise ratio') -xlabel('Initial dictionary') -ylabel('SNR (dB)') -set(gca,'XTickLabel',{'data','dct','gabor'}); -legend('none','Mailhe','Tropp') -grid on +figure, +%subplot(3,1,1) +hold on, grid on +title('Data Initialisation') +plot([1 1],[0 25],'k-'); +plot(mu(1),sigNoiseRatio(1),'ks'); +plot(mu(12),sigNoiseRatio(12),'kd'); +plot(mu(2:11),sigNoiseRatio(2:11),'k*-'); +plot([minMu minMu],[0 25],'k--') +set(gca,'YLim',[0 25],'XLim',[0 1.4]); +legend({'\mu_{max}','K-SVD','Grassmannian','INK-SVD','\mu_{min}'}); +xlabel('\mu'); +ylabel('SNR (dB)'); +figure +%subplot(3,1,2) +hold on, grid on +title('DCT Initialisation') +plot([1 1],[0 25],'k-'); +plot(mu(13),sigNoiseRatio(13),'ks'); +plot(mu(24),sigNoiseRatio(24),'kd'); +plot(mu(14:23),sigNoiseRatio(14:23),'k*-'); +plot([minMu minMu],[0 25],'k--') +set(gca,'YLim',[0 25],'XLim',[0 1.4]); +legend({'\mu_{max}','K-SVD','Grassmannian','INK-SVD','\mu_{min}'}); +xlabel('\mu'); +ylabel('SNR (dB)'); + +figure +%subplot(3,1,3) +hold on, grid on +title('Gabor Initialisation') +plot([1 1],[0 25],'k-'); +plot(mu(25),sigNoiseRatio(25),'ks'); +plot(mu(36),sigNoiseRatio(36),'kd'); +plot(mu(26:35),sigNoiseRatio(26:35),'k*-'); +plot([minMu minMu],[0 25],'k--') +set(gca,'YLim',[0 25],'XLim',[0 1.4]); +legend({'\mu_{max}','K-SVD','Grassmannian','INK-SVD','\mu_{min}'}); +xlabel('\mu'); +ylabel('SNR (dB)'); + +% minMu = sqrt((dictSize-blockSize)/(blockSize*(dictSize-1))); +% maxSNR = max(sigNoiseRatio); +% +% figure, subplot(2,2,1) +% snrMat = buffer(sigNoiseRatio(1:9),3); +% bar(snrMat'); +% title('Signal to noise ratio') +% xlabel('Initial dictionary') +% ylabel('SNR (dB)') +% set(gca,'XTickLabel',{'data','dct','gabor'}); +% legend('none','Mailhe','Tropp') +% grid on +% % subplot(2,2,2), grid on % snrMat = buffer(sigNoiseRatio(10:18),3); % bar(snrMat'); @@ -187,18 +184,18 @@ % set(gca,'XTickLabel',{'data','dct','gabor'},'YLim',[0 maxSNR+1]); % legend('none','Mailhe','Tropp') % grid on - -subplot(1,2,2), hold on, grid on -title('Coherence') -muMat = buffer(mu(1:9),3); -line([0.5 3.5],[1 1],'Color','r'); -bar(muMat'); -line([0.5 3.5],[minMu minMu],'Color','k'); -set(gca,'XTick',1:3,'XTickLabel',{'data','dct','gabor'},'YLim',[0 1.05]) -legend('\mu_{max}','none','Mailhe','Tropp','\mu_{min}') -ylabel('\mu') -xlabel('Initial dictionary') - +% +% subplot(2,2,3), hold on, grid on +% title('Coherence') +% muMat = buffer(mu(1:9),3); +% line([0.5 3.5],[1 1],'Color','r'); +% bar(muMat'); +% line([0.5 3.5],[minMu minMu],'Color','k'); +% set(gca,'XTick',1:3,'XTickLabel',{'data','dct','gabor'},'YLim',[0 1.05]) +% legend('\mu_{max}','none','Mailhe','Tropp','\mu_{min}') +% ylabel('\mu') +% xlabel('Initial dictionary') +% % subplot(2,2,4), hold on, grid on % title('Coherence - Mailhe Update') % muMat = buffer(mu(10:18),3);