30 lines
No EOL
1.1 KiB
Matlab
30 lines
No EOL
1.1 KiB
Matlab
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% Name: E_step.m
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% Input: x - a nxd matrix (nx3 if using RGB)
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% Q - vector of values from the complete data log-likelihood function
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% h - a nxk matrix, the expectation of the hidden variable z given the data set and distribution params
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% pi - vector of mixing coefficients
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% m - cluster means
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% S - cluster covariance matrices
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% k - the number of clusters
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% Output: h - a nxk matrix, the expectation of the hidden variable z given the data set and distribution params
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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function [h] = E_step(x, h, pi, m, S, k)
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[num_data, ~] = size(x);
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% perform E-step of EM algorithm
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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parts = zeros(num_data, k);
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for i = 1:k
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parts(:, i) = pi(i) * mvnpdf(x, m(i, :), S(:, :, i));
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end
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s = sum(parts);
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for j = 1:num_data
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h(j, :) = parts(j, :) ./ s;
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end
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end |