📄 emcpm_loglike.m
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% function logLike = EMCPM_logLike(G,samplesMat)%% Compute the log likelihood of the data for the% EM-CPM used parameters stored in structure G, and% observed data stored in samplesMatfunction logLike = EMCPM_logLike(G,samplesMat)smoothLikes=getSmoothLike(G,G.z,G.u);[timePriorTerm, scalePriorTerm] = getDirichletLike(G);nuTerm = getNuTerm(G);scaleCenterPriorTerm = getScaleLike(G,G.u);for kk=1:G.numSamples %% E-step (obtaining gammas), using forward-backward in %% standard way, with scaling tricks (not in log space) myClass = getClass(G,kk); doback=0; %% do backward pass tmpZ = permute(G.z(:,myClass,:),[1 3 2]); [mainL(kk)]=FB(G,samplesMat(:,:,kk),kk,tmpZ,doback);endlogLike = sum(mainL) + scalePriorTerm + sum(timePriorTerm) + ... sum(smoothLikes) + scaleCenterPriorTerm + nuTerm;
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