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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)';
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