%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% 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
    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

end