22 lines
No EOL
1 KiB
Matlab
22 lines
No EOL
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: Q - vector of values of the complete data log-likelihood function
|
|
% h - a nxk matrix, the expectation of the hidden variable z given the data set and distribution params
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
function [Q, h] = E_step(x, Q, h, pi, m, S, k)
|
|
|
|
[num_data, ~] = size(x);
|
|
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
% TODO: perform E-step of EM algorithm
|
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
|
z = 1 + 1
|
|
|
|
end |