csci5521/assignments/hwk03/E_step.m
2023-11-15 09:53:18 -06:00

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1.1 KiB
Matlab

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Name: E_step.m
% Input: x - a nxd matrix (nx3 if using RGB)
% Q - vector of values from the complete data log-likelihood function
% h - a nxk matrix, the expectation of the hidden variable z given the data set and distribution params
% pi - vector of mixing coefficients
% m - cluster means
% S - cluster covariance matrices
% k - the number of clusters
% Output: h - a nxk matrix, the expectation of the hidden variable z given the data set and distribution params
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
function [h] = E_step(x, h, pi, m, S, k)
[num_data, ~] = size(x);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% TODO: perform E-step of EM algorithm
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
parts = zeros(num_data, k);
for j = 1:k
parts(:, j) = pi(j) * mvnpdf(x, m(j, :), S(:, :, j));
end
s = sum(parts);
for i = 1:num_data
h(i, :) = parts(i, :) ./ s;
end
end