wehub-resource-sync
41 lines
1.0 KiB
Matlab
Executable File
41 lines
1.0 KiB
Matlab
Executable File
function [label, model, llh] = mixBernEm(X, k)
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% Perform EM algorithm for fitting the Bernoulli mixture model.
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% Input:
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% X: d x n binary (0/1) data matrix
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% k: number of cluster
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% Output:
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% label: 1 x n cluster label
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% model: trained model structure
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% llh: loglikelihood
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% Written by Mo Chen (sth4nth@gmail.com).
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%% initialization
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fprintf('EM for mixture model: running ... \n');
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X = sparse(X);
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n = size(X,2);
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label = ceil(k*rand(1,n)); % random initialization
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R = full(sparse(1:n,label,1));
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tol = 1e-8;
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maxiter = 500;
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llh = -inf(1,maxiter);
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for iter = 2:maxiter
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model = maximization(X,R);
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[R, llh(iter)] = expectation(X,model);
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if abs(llh(iter)-llh(iter-1)) < tol*abs(llh(iter)); break; end;
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end
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[~,label(:)] = max(R,[],2);
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llh = llh(2:iter);
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function [R, llh] = expectation(X, model)
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mu = model.mu;
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w = model.w;
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R = X'*log(mu)+(1-X)'*log(1-mu)+log(w);
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T = logsumexp(R,2);
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llh = mean(T); % loglikelihood
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R = exp(R-T);
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function model = maximization(X, R)
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nk = sum(R,1);
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w = nk/sum(nk);
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mu = (X*R)./nk;
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model.mu = mu;
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model.w = w; |