%PCLDC Linear classifier using PC expansion on the joint data.% % 	W = PCLDC(A,N)% 	W = PCLDC(A,ALF)%% INPUT%  A    Dataset%  N    Number of eigenvectors%  ALF  Total explained variance (default: ALF = 0.9)%% OUTPUT%  W    Mapping% % DESCRIPTION% Finds the linear discriminant function W for the dataset A % computing the LDC on a projection of the data on the first N  % eigenvectors of the total dataset (Principle Component Analysis).% % When ALF is supplied the number of eigenvalues is chosen such that at % least a part ALF of the total variance is explained. % % SEE ALSO% MAPPINGS, DATASETS, KLLDC, KLM, FISHERM% 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: pcldc.m,v 1.7 2003/11/22 23:20:38 bob Exp $function W = pcldc(a,n)		prtrace(mfilename);	if nargin < 2, n = []; end	if nargin == 0 | isempty(a)		W = mapping('pcldc',{n});		W = setname(W,'PC Bayes-Normal-1');		return;	end	islabtype(a,'crisp','soft');	isvaldset(a,2,2); % at least 2 object per class, 2 classes	% Make a sequential classifier combining PCA and LDC:	v = pca(a,n);	W = v*ldc(a*v);	W = setname(W,'PC Bayes-Normal-1');	W = setcost(W,a);return