wehub-resource-sync
48 lines
1.5 KiB
Matlab
Executable File
48 lines
1.5 KiB
Matlab
Executable File
function [label, Theta, w, llh] = mixDpGb(X, alpha, theta)
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% Collapsed Gibbs sampling for Dirichlet process (infinite) mixture model.
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% Any component model can be used, such as Gaussian.
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% Input:
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% X: d x n data matrix
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% alpha: parameter for Dirichlet process prior
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% theta: class object for prior of component distribution (such as Gauss)
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% Output:
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% label: 1 x n cluster label
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% Theta: 1 x k structure of trained components
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% w: 1 x k component weight vector
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% llh: loglikelihood
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% Written by Mo Chen (sth4nth@gmail.com).
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n = size(X,2);
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[label,Theta,w] = mixDpGbOl(X,alpha,theta);
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nk = n*w;
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maxIter = 50;
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llh = zeros(1,maxIter);
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for iter = 1:maxIter
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for i = randperm(n)
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x = X(:,i);
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k = label(i);
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Theta{k} = Theta{k}.delSample(x);
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nk(k) = nk(k)-1;
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if nk(k) == 0 % remove empty cluster
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Theta(k) = [];
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nk(k) = [];
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which = label>k;
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label(which) = label(which)-1;
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end
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Pk = log(nk)+cellfun(@(t) t.logPredPdf(x), Theta);
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P0 = log(alpha)+theta.logPredPdf(x);
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p = [Pk,P0];
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llh(iter) = llh(iter)+sum(p-log(n));
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k = discreteRnd(exp(p-logsumexp(p)));
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if k == numel(Theta)+1 % add extra cluster
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Theta{k} = theta.clone().addSample(x);
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nk = [nk,1];
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else
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Theta{k} = Theta{k}.addSample(x);
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nk(k) = nk(k)+1;
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end
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label(i) = k;
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end
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end
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w = nk/n;
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