34 lines
1.4 KiB
Mathematica
34 lines
1.4 KiB
Mathematica
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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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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% S - cluster covariance matrices
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% k - the number of clusters
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% Output: Q - vector of values of the complete data log-likelihood function
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% S - cluster covariance matrices
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% m - cluster means
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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function [Q, S, m] = M_step(x, Q, h, S, k)
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% get size of data
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[num_data, dim] = size(x);
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eps = 1e-15;
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lambda = 1e-3; % value for improved version of EM
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% TODO: update mixing coefficients
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% TODO: update cluster means
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% TODO: Calculate the covariance matrix estimate
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% further modifications will need to be made when doing 2(d)
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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end
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