wehub-resource-sync
34 lines
1.0 KiB
Matlab
Executable File
34 lines
1.0 KiB
Matlab
Executable File
function [z, R] = mixGaussVbPred(model, X)
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% Predict label and responsibility for Gaussian mixture model trained by VB.
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% Input:
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% X: d x n data matrix
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% model: trained model structure outputed by the EM algirthm
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% Output:
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% label: 1 x n cluster label
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% R: k x n responsibility
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% Written by Mo Chen (sth4nth@gmail.com).
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alpha = model.alpha; % Dirichlet
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kappa = model.kappa; % Gaussian
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m = model.m; % Gasusian
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v = model.v; % Whishart
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U = model.U; % Whishart
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logW = model.logW;
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n = size(X,2);
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[d,k] = size(m);
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EQ = zeros(n,k);
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for i = 1:k
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Q = (U(:,:,i)'\bsxfun(@minus,X,m(:,i)));
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EQ(:,i) = d/kappa(i)+v(i)*dot(Q,Q,1); % 10.64
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end
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ElogLambda = sum(psi(0,0.5*bsxfun(@minus,v+1,(1:d)')),1)+d*log(2)+logW; % 10.65
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Elogpi = psi(0,alpha)-psi(0,sum(alpha)); % 10.66
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logRho = -0.5*bsxfun(@minus,EQ,ElogLambda-d*log(2*pi)); % 10.46
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logRho = bsxfun(@plus,logRho,Elogpi); % 10.46
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logR = bsxfun(@minus,logRho,logsumexp(logRho,2)); % 10.49
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R = exp(logR);
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z = zeros(1,n);
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[~,z(:)] = max(R,[],2);
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[~,~,z(:)] = unique(z);
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