35 lines
1.0 KiB
Matlab
35 lines
1.0 KiB
Matlab
function [StateMu,StateSqrtP,StateWeights] = fit_gaussian_mixture(X,StateMu,StateSqrtP,StateWeights,crit,niters,check)
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[dim,Ndata] = size(X);
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M = size(StateMu,2) ;
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if check % Ensure that covariances don't collapse
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MIN_COVAR_SQRT = sqrt(eps);
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init_covars = StateSqrtP;
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end
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eold = -Inf;
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for n=1:niters
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% Calculate posteriors based on old parameters
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[prior,likelihood,marginal,posterior] = probability(StateMu,StateSqrtP,StateWeights,X);
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e = sum(log(marginal));
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if (n > 1 && abs((e - eold)/eold) < crit)
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return;
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else
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eold = e;
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end
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new_pr = (sum(posterior,2))';
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StateWeights = new_pr/Ndata;
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StateMu = bsxfun(@rdivide,(posterior*X')',new_pr);
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for j=1:M
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diffs = bsxfun(@minus,X,StateMu(:,j));
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tpost = (1/sqrt(new_pr(j)))*sqrt(posterior(j,:));
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diffs = bsxfun(@times,diffs,tpost);
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[foo,tcov] = qr2(diffs',0);
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StateSqrtP(:,:,j) = tcov';
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if check
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if min(abs(diag(StateSqrtP(:,:,j)))) < MIN_COVAR_SQRT
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StateSqrtP(:,:,j) = init_covars(:,:,j);
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end
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end
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end
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end
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