comparison toolboxes/FullBNT-1.0.7/netlab3.3/gmmsamp.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function [data, label] = gmmsamp(mix, n)
+%GMMSAMP Sample from a Gaussian mixture distribution.
+%
+%	Description
+%
+%	DATA = GSAMP(MIX, N) generates a sample of size N from a Gaussian
+%	mixture distribution defined by the MIX data structure. The matrix X
+%	has N rows in which each row represents a MIX.NIN-dimensional sample
+%	vector.
+%
+%	[DATA, LABEL] = GMMSAMP(MIX, N) also returns a column vector of
+%	classes (as an index 1..N) LABEL.
+%
+%	See also
+%	GSAMP, GMM
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check input arguments
+errstring = consist(mix, 'gmm');
+if ~isempty(errstring)
+  error(errstring);
+end
+if n < 1
+  error('Number of data points must be positive')
+end
+
+% Determine number to sample from each component
+priors = rand(1, n);
+
+% Pre-allocate data array
+data = zeros(n, mix.nin);
+if nargout > 1
+  label = zeros(n, 1);
+end
+cum_prior = 0;		% Cumulative sum of priors
+total_samples = 0;	% Cumulative sum of number of sampled points
+for j = 1:mix.ncentres
+  num_samples = sum(priors >= cum_prior & ...
+    priors < cum_prior + mix.priors(j));
+  % Form a full covariance matrix
+  switch mix.covar_type
+    case 'spherical'
+      covar = mix.covars(j) * eye(mix.nin);
+    case 'diag'
+      covar = diag(mix.covars(j, :));
+    case 'full'
+      covar = mix.covars(:, :, j);
+    case 'ppca'
+      covar = mix.covars(j) * eye(mix.nin) + ...
+        mix.U(:, :, j)* ...
+        (diag(mix.lambda(j, :))-(mix.covars(j)*eye(mix.ppca_dim)))* ...
+        (mix.U(:, :, j)');
+    otherwise
+      error(['Unknown covariance type ', mix.covar_type]);
+  end
+  data(total_samples+1:total_samples+num_samples, :) = ...
+    gsamp(mix.centres(j, :), covar, num_samples);
+  if nargout > 1
+    label(total_samples+1:total_samples+num_samples) = j;
+  end
+  cum_prior = cum_prior + mix.priors(j);
+  total_samples = total_samples + num_samples;
+end
+