%NMC Nearest Mean Classifier% %   W = NMC(A)%   W = A*NMC%% INPUT%   A    Dataset%% OUTPUT%   W    Nearest Mean Classifier   %% DESCRIPTION% Computation of the nearest mean classifier between the classes in the% dataset A.  The use of soft labels is supported. Prior probabilities are% not used.%% The difference with NMSC 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 classifiers that% is feature scaling sensitive and unsensitive to class priors.%% SEE ALSO% DATASETS, MAPPINGS, NMSC, LDC, FISHERC, QDC, UDC % Copyright: R.P.W. Duin, r.p.w.duin@prtools.org% Faculty EWI, Delft University of Technology% P.O. Box 5031, 2600 GA Delft, The Netherlands% $Id: nmc.m,v 1.10 2007/11/30 16:29:49 duin Exp $function W = nmc(a)		prtrace(mfilename);		if nargin < 1 | isempty(a)		W = mapping(mfilename);		W = setname(W,'Nearest Mean');		return	end		islabtype(a,'crisp','soft');	isvaldfile(a,1,2); % at least 1 object per class, 2 classes	[m,k,c] = getsize(a);	if c == 2      % 2-class case: store linear classifier		u = meancov(a);		u1 = +u(1,:);		u2 = +u(2,:);		R = [u1-u2]';		offset =(u2*u2' - u1*u1')/2;		W = affine([R -R],[offset -offset],a,getlablist(a));		W = cnormc(W,a);		W = setname(W,'Nearest Mean');	else 		if all(classsizes(a) > 1)			a = setprior(a,0);  % NMC should be independent of priors: make tehm equal			p = getprior(a);			U = zeros(c,k);			V = zeros(c,k);			for j=1:c				b = seldat(a,j);				U(j,:) = +mean(b);				V(j,:) = +var(b);			end			%G = mean(V'*p') * eye(k);			G = mean(V(:));			w.mean = +U;			w.cov = G;			w.prior =p;			W = normal_map(w,getlablist(a),k,c);			W = setname(W,'Nearest Mean');			W = setcost(W,a);		else			u = meancov(a);			W = knnc(u,1);			W = setname(W,'Nearest Mean');		end			end		W = setcost(W,a); 		return