function initmemberships = gaussian_mixture_initialize(data,K,kseed,option);
% function initmemberships = gaussian_mixture_initialize(data,K,kseed,option); 
%
%  Initialize the parameters for a mixture model for Gaussian mixtures. 
%
%  INPUTS
%  data: N x d real-valued data matrix
%  K = number of components
%  kseed = random seed (integer)
%  option: 1 for kmeans, 2 for random memberships
%
%  OUTPUTS
%  initimemberships: n x k array of component memberships (rows sum to 1)
%  
%
%                                        Padhraic Smyth, CS 274A, Winter 2009


% Ideally need to add some error-checking here....

[n d] = size(data);  

if option==1  
    % Use kmeans to estimate initial class means (stored in mu, a k x d array)
    [cluster,mu,kseed] = kmeans(data,K,kseed);
    initmemberships = zeros(n,d);
    for i=1:n
        initmemberships(i,cluster(i))=1;
    end
 
elseif option==2  
    % start with random memberships for each row in the
                  % data matrix, and then call the Mstep to provide
                  % initial parameter estimates given these memberships
    rand('state',kseed);
    tmp  = rand(n,K);
    initmemberships = tmp./repmat(sum(tmp')',1,K); 
else
    error('Option parameter = %d, should be either 1 or 2',option);
end