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

first hg version after svn

+function a = gmmactiv(mix, x)
+%GMMACTIV Computes the activations of a Gaussian mixture model.
+%
+%	Description
+%	This function computes the activations A (i.e. the  probability
+%	P(X|J) of the data conditioned on each component density)  for a
+%	Gaussian mixture model.  For the PPCA model, each activation is the
+%	conditional probability of X given that it is generated by the
+%	component subspace. The data structure MIX defines the mixture model,
+%	while the matrix X contains the data vectors.  Each row of X
+%	represents a single vector.
+%
+%	See also
+%	GMM, GMMPOST, GMMPROB
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+
+% Check that inputs are consistent
+errstring = consist(mix, 'gmm', x);
+if ~isempty(errstring)
+  error(errstring);
+end
+
+ndata = size(x, 1);
+a = zeros(ndata, mix.ncentres);  % Preallocate matrix
+
+switch mix.covar_type
+  
+case 'spherical'
+  % Calculate squared norm matrix, of dimension (ndata, ncentres)
+  n2 = dist2(x, mix.centres);
+  
+  % Calculate width factors
+  wi2 = ones(ndata, 1) * (2 .* mix.covars);
+  normal = (pi .* wi2) .^ (mix.nin/2);
+  
+  % Now compute the activations
+  a = exp(-(n2./wi2))./ normal;
+  
+case 'diag'
+  normal = (2*pi)^(mix.nin/2);
+  s = prod(sqrt(mix.covars), 2);
+  for j = 1:mix.ncentres
+    diffs = x - (ones(ndata, 1) * mix.centres(j, :));
+    a(:, j) = exp(-0.5*sum((diffs.*diffs)./(ones(ndata, 1) * ...
+      mix.covars(j, :)), 2)) ./ (normal*s(j));
+  end
+  
+case 'full'
+  normal = (2*pi)^(mix.nin/2);
+  for j = 1:mix.ncentres
+    diffs = x - (ones(ndata, 1) * mix.centres(j, :));
+    % Use Cholesky decomposition of covariance matrix to speed computation
+    c = chol(mix.covars(:, :, j));
+    temp = diffs/c;
+    a(:, j) = exp(-0.5*sum(temp.*temp, 2))./(normal*prod(diag(c)));
+  end
+case 'ppca'
+  log_normal = mix.nin*log(2*pi);
+  d2 = zeros(ndata, mix.ncentres);
+  logZ = zeros(1, mix.ncentres);
+  for i = 1:mix.ncentres
+    k = 1 - mix.covars(i)./mix.lambda(i, :);
+    logZ(i) = log_normal + mix.nin*log(mix.covars(i)) - ...
+      sum(log(1 - k));
+    diffs = x - ones(ndata, 1)*mix.centres(i, :);
+    proj = diffs*mix.U(:, :, i);
+    d2(:,i) = (sum(diffs.*diffs, 2) - ...
+      sum((proj.*(ones(ndata, 1)*k)).*proj, 2)) / ...
+      mix.covars(i);
+  end
+  a = exp(-0.5*(d2 + ones(ndata, 1)*logZ));
+otherwise
+  error(['Unknown covariance type ', mix.covar_type]);
+end
+