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 consistencyerrstring = consist(mixparams, 'mdnmixes');if ~isempty(errstring)  error(errstring);endncentres   = 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')endif 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 centret = kron(ones(1, ncentres), t);% Do subtraction and squarediff2 = (t - mixparams.centres).^2;% Reshape and sum each componentdiff2 = 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 targetn2 = reshape(n2, ncentres, ntarget)';