%TESTK Error estimation of the K-NN rule% % 	E = TESTK(A,K,T)%% INPUT% 	A 	Training dataset% 	K 	Number of nearest neighbors (default 1)% 	T 	Test dataset (default [], i.e. find leave-one-out estimate on A)%% OUTPUT% 	E 	Estimated error of the K-NN rule%% DESCRIPTION  % Tests a dataset T on the training dataset A using the K-NN rule and% returns the classification error E. In case no set T is provided, the% leave-one-out error estimate on A is returned.% % The advantage of using TESTK over TESTC is that it enables leave-one-out % error estimation.% % SEE ALSO% DATASETS, KNNC, KNN_MAP, TESTC% 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: testk.m,v 1.7 2004/05/25 06:05:58 duin Exp $function e = testk(a,knn,t)	prtrace(mfilename);	if (nargin < 2)			prwarning(2,'number of neighbours K not specified, assuming 1');		knn = 1; 	end	% Calculate the KNN classifier.	a = seldat(a);   % get labeled objects only	w = knnc(a,knn);	[m,k] = size(a); nlab = getnlab(a);	if (nargin <= 2)						% Leave-one-out error estimate.		d = knn_map([],w);				% Posterior probabilities of KNNC(A,KNN).		[dmax,J] = max(d,[],2);		% Find the maximum.		e = nlabcmp(J,nlab)/m;		% Calculate error: compare numerical labels.	else												% Error estimation on tuning set T.		[n,kt] = size(t);		if (k ~= kt)			error('Number of features of A and T do not match.');		end		d = knn_map(t,w); 				% Posterior probabilities of T*KNNC(A,KNN).		[dmax,J] = max(d,[],2); 	% Find the maximum.		lablist = getlablist(a);	% Calculate error: compare full labels.		e = nlabcmp(getlab(t),lablist(J,:))/n;	endreturn