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

first hg version after svn

+function [prob,a] = mdnprob(mixparams, t)
+%MDNPROB Computes the data probability likelihood for an MDN mixture structure.
+%
+%	Description
+%	PROB = MDNPROB(MIXPARAMS, T) computes the probability P(T) of each
+%	data vector in T under the Gaussian mixture model represented by the
+%	corresponding entries in MIXPARAMS. Each row of T represents a single
+%	vector.
+%
+%	[PROB, A] = MDNPROB(MIXPARAMS, T) also computes the activations A
+%	(i.e. the probability P(T|J) of the data conditioned on each
+%	component density) for a Gaussian mixture model.
+%
+%	See also
+%	MDNERR, MDNPOST
+%
+
+%	Copyright (c) Ian T Nabney (1996-2001)
+%	David J Evans (1998)
+
+% Check arguments for consistency
+errstring = consist(mixparams, 'mdnmixes');
+if ~isempty(errstring)
+  error(errstring);
+end
+
+ntarget    = size(t, 1);
+if ntarget ~= size(mixparams.centres, 1)
+  error('Number of targets does not match number of mixtures')
+end
+if size(t, 2) ~= mixparams.dim_target
+  error('Target dimension does not match mixture dimension')
+end
+
+dim_target = mixparams.dim_target;
+ntarget    = size(t, 1);
+
+% Calculate squared norm matrix, of dimension (ndata, ncentres)
+% vector (ntarget * ncentres)
+dist2 = mdndist2(mixparams, t);
+
+% Calculate variance factors
+variance = 2.*mixparams.covars;
+
+% Compute the normalisation term
+normal  = ((2.*pi).*mixparams.covars).^(dim_target./2);
+
+% Now compute the activations
+a = exp(-(dist2./variance))./normal;
+
+% Accumulate negative log likelihood of targets
+prob = mixparams.mixcoeffs.*a;