21 lines
1 KiB
Mathematica
21 lines
1 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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% 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: Q - vector of values of 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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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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function [Q, h] = E_step(x, Q, h, pi, m, S, k)
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[num_data, ~] = size(x);
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% TODO: perform E-step of EM algorithm
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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end
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