wolffd@0: wolffd@0:
wolffd@0:wolffd@0: n2 = mdndist2(mixparams, t) wolffd@0:wolffd@0: wolffd@0: wolffd@0:
n2 = mdndist2(mixparams, t) takes takes the centres of the Gaussian 
wolffd@0: contained in
wolffd@0:  mixparams and the target data matrix, t, and computes the squared 
wolffd@0: Euclidean distance between them.  If t has m rows and n
wolffd@0: columns, then the centres field in
wolffd@0: the mixparams structure should have m rows and
wolffd@0: n*mixparams.ncentres columns: the centres in each row relate to
wolffd@0: the corresponding row in t.
wolffd@0: The result has m rows and mixparams.ncentres columns.
wolffd@0: The i, jth entry is the 
wolffd@0: squared distance from the ith row of x to the jth
wolffd@0: centre in the ith row of mixparams.centres.
wolffd@0: 
wolffd@0: mdnfwd, mdnprobCopyright (c) Ian T Nabney (1996-9) wolffd@0:
David J Evans (1998) wolffd@0: wolffd@0: wolffd@0: