comparison toolboxes/FullBNT-1.0.7/KPMstats/mixgauss_init.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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
+  end
+end
+