function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type, method)% MIXGAUSS_INIT Initial parameter estimates for a mixture of Gaussians% function [mu, Sigma, weights] = mixgauss_init(M, data, cov_type. method)%% INPUTS:% data(:,t) is the t'th example% M = num. mixture components% cov_type = 'full', 'diag' or 'spherical'% method = 'rnd' (choose centers randomly from data) or 'kmeans' (needs netlab)%% OUTPUTS:% mu(:,k) % Sigma(:,:,k) % weights(k)if nargin < 4, method = 'kmeans'; end[d T] = size(data);data = reshape(data, d, T); % in case it is data(:, t, sequence_num)switch method case 'rnd',   C = cov(data');  Sigma = repmat(diag(diag(C))*0.5, [1 1 M]);  % Initialize each mean to a random data point  indices = randperm(T);  mu = data(:,indices(1:M));  weights = normalise(ones(M,1)); case 'kmeans',  mix = gmm(d, M, cov_type);  options = foptions;  max_iter = 5;  options(1) = -1; % be quiet!  options(14) = max_iter;  mix = gmminit(mix, data', options);  mu = reshape(mix.centres', [d M]);  weights = mix.priors(:);  for m=1:M    switch cov_type     case 'diag',      Sigma(:,:,m) = diag(mix.covars(m,:));     case 'full',      Sigma(:,:,m) = mix.covars(:,:,m);     case 'spherical',      Sigma(:,:,m) = mix.covars(m) * eye(d);    end  endend