%NMSC Nearest Mean Scaled Classifier% % 	W = NMSC(A)% % INPUT%   A   Trainign dataset%% OUTPUT%   W   Nearest Mean Scaled Classifier mapping%% DESCRIPTION% Computation of the linear discriminant for the classes in the dataset A% assuming zero covariances and equal class variances. The use of soft% labels is supported.% % SEE ALSO% DATASETS, MAPPINGS, NMC, LDC ,FISHERC, QDC, UDC % Copyright: R.P.W. Duin, duin@ph.tn.tudelft.nl% Faculty of Applied Physics, Delft University of Technology% P.O. Box 5046, 2600 GA Delft, The Netherlands% $Id: nmsc.m,v 1.6 2003/11/22 23:19:27 bob Exp $function w = nmsc(a)	prtrace(mfilename);	% No input arguments: return an untrained mapping.	if (nargin < 1) | (isempty(a))		w = mapping(mfilename);		w = setname(w,'Scaled Nearest Mean');		return	end	islabtype(a,'crisp','soft');	isvaldset(a,1,2); % at least 1 object per class, 2 classes	[m,k,c] = getsize(a); p = getprior(a); 	[U,GG] = meancov(a);		% All class covariance matrices are assumed to be diagonal. They are scaled	% by the priors, unlike the standard nearest mean classifier (NMC).	G = zeros(c,k);	for j = 1:c		G(j,:) = diag(GG(:,:,j))';	end	G = diag(p*G);	% The two-class case is special, as it can be conveniently stored as an	% affine mapping.	if (c == 2)		ua = +U(1,:); ub = +U(2,:);		G = inv(G); R = G*(ua - ub)';		offset = (ub*G*ub' - ua*G*ua')/2 + log(p(1)/p(2));		w = affine(R,offset,a,getlablist(a)); 		w = cnormc(w,a);	else		pars.mean = +U; pars.cov = G; pars.prior = p;		w = mapping('normal_map','trained',pars,getlab(U),k,c);	end	w = setname(w,'Scaled Nearest Mean');	w = setcost(w,a);return