%NMSC Nearest Mean Scaled Classifier
% 
% 	W = NMSC(A)
%   W = A*NMSC
% 
% 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 normal distributions with zero covariances and equal class variances. 
% The use of soft labels is supported.
%
% The difference with NMC is that NMSC is based on an assumption of normal
% distributions and thereby automatically scales the features and is
% sensitive to class priors. NMC is a plain nearest mean classifier that is
% feature scaling sensitive and unsensitive to class priors.
% 
% 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 1.7 2008/01/25 10:20:07 duin Exp $

function w = nmsc(a)

		% No input arguments: return an untrained mapping.

	if (nargin < 1) | (isempty(a))
		w = prmapping(mfilename);
		w = setname(w,'S-NearestMean');
		return
	end

	islabtype(a,'crisp','soft');
	isvaldfile(a,1,2); % at least 1 object per class, 2 classes

	[m,k,c] = getsize(a); 
	p = getprior(a);
	a = setprior(a,p);
	[U,GG] = meancov(a);
	
	% All class covariance matrices are assumed to be diagonal. They are
	% weighted 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 = 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,:);
		R = G*(ua - ub)';
		R = ((ua - ub)./G)';
		offset = ((ub./G)*ub' - (ua./G)*ua')/2 + log(p(1)/p(2));
		w = affine(R,offset,a,getlablist(a),k,c); 
		w = cnormc(w,a);
	else
		pars.mean = +U; pars.cov = G; pars.prior = p;
		w = normal_map(pars,getlab(U),k,c);
	end

	w = setname(w,'S-NearestMean');
	w = setcost(w,a);

return
