comparison toolboxes/FullBNT-1.0.7/netlab3.3/mdndist2.m @ 0:e9a9cd732c1e tip

first hg version after svn

+function n2 = mdndist2(mixparams, t)
+%MDNDIST2 Calculates squared distance between centres of Gaussian kernels and data
+%
+%	Description
+%	N2 = MDNDIST2(MIXPARAMS, T) takes takes the centres of the Gaussian
+%	contained in  MIXPARAMS and the target data matrix, T, and computes
+%	the squared  Euclidean distance between them.  If T has M rows and N
+%	columns, then the CENTRES field in the MIXPARAMS structure should
+%	have M rows and N*MIXPARAMS.NCENTRES columns: the centres in each row
+%	relate to the corresponding row in T. The result has M rows and
+%	MIXPARAMS.NCENTRES columns. The I, Jth entry is the  squared distance
+%	from the Ith row of X to the Jth centre in the Ith row of
+%	MIXPARAMS.CENTRES.
+%
+%	See also
+%	MDNFWD, MDNPROB
+%
+
+%	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
+
+ncentres   = mixparams.ncentres;
+dim_target = mixparams.dim_target;
+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
+
+% Build t that suits parameters, that is repeat t for each centre
+t = kron(ones(1, ncentres), t);
+
+% Do subtraction and square
+diff2 = (t - mixparams.centres).^2;
+
+% Reshape and sum each component
+diff2 = reshape(diff2', dim_target, (ntarget*ncentres))';
+n2 = sum(diff2, 2);
+
+% Calculate the sum of distance, and reshape
+% so that we have a distance for each centre per target
+n2 = reshape(n2, ncentres, ntarget)';
+