%UDC Uncorrelated normal based quadratic Bayes classifier (BayesNormal_U)% %  W = UDC(A)%  W = A*UDC% % INPUT%  A  input dataset%% OUTPUT%  W   output mapping%% DESCRIPTION% Computation a quadratic classifier between the classes in the % dataset A assuming normal densities with uncorrelated features.%% The use of probabilistic labels is supported.  The classification A*W is% computed by normal_map.% % EXAMPLES% PREX_DENSITY%% SEE ALSO% MAPPINGS, DATASETS, NMC, NMSC, LDC, QDC, QUADRC, NORMAL_MAP% 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: udc.m,v 1.6 2007/06/05 12:45:44 duin Exp $function W = udc(a)	prtrace(mfilename);		if nargin == 0		W = mapping(mfilename);		W = setname(W,'Bayes-Normal-U');		return	end	islabtype(a,'crisp','soft');	isvaldfile(a,2,2); % at least 2 objects per class, 2 classes	[m,k,c] = getsize(a);	[U,G] = meancov(a); %computing mean and covariance matrix	p = getprior(a);	for j = 1:c		G(:,:,j) = diag(diag(G(:,:,j)));	end	w.mean = +U;	w.cov = G;	w.prior = p;	%W = mapping('normal_map','trained',w,getlab(U),k,c);	W = normal_map(w,getlab(U),k,c);	W = setname(W,'Bayes-Normal-U');	W = setcost(W,a);return