%KNNC K-Nearest Neighbor Classifier%%   [W,K,E] = KNNC(A,K)%   [W,K,E] = KNNC(A)%% INPUT%   A  Dataset%   K  Number of the nearest neighbors (optional; default: K is %      optimized with respect to the leave-one-out error on A)%% OUTPUT%   W  k-NN classifier %   K  Number of the nearest neighbors used%   E  The leave-one-out error of the KNNC%% DESCRIPTION  % Computation of the K-nearest neighbor classifier for the dataset A. % The resulting classifier W is automatically evaluated by KNN_MAP.%% Warning: class prior probabilities in A are neglected.%% SEE ALSO% MAPPINGS, DATASETS, KNN_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: knnc.m,v 1.13 2006/03/07 16:16:54 duin Exp $function [W,knn,e,ek] = knnc(a,knn)	prtrace(mfilename);	if (nargin < 2)      prwarning(4,'Number of nearest neighbors not supplied, optimized wrt the leave-one-out error.');      knn = [];   end	% No input data, return an untrained classifier.	if (nargin == 0) | (isempty(a))		W = mapping('knnc',knn);		if (isempty(knn))			W = setname(W,'K-NN Classifier');		else			W = setname(W,[num2str(knn) '-NN Classifier']);		end		return; 	end	islabtype(a,'crisp','soft');	isvaldset(a,1,2); % at least 1 object per class, 2 classes		a = seldat(a);    % get labeled objects only	[m,k,c] = getsize(a);	nlab = getnlab(a);	if (isempty(knn))									% Optimize knn by the LOO procedure.		[num,bat] = prmem(m,m);		z = zeros(1,m);		N = zeros(c,m);		for i = 0:num-1							% Compute the distance matrix part by part			if (i == num-1)						% depending on the available memory.				nn = m - num*bat + bat;			else				nn = bat;			end			I = [i*bat+1:i*bat+nn];			D=+distm(a,a(I,:));         			[Y,L] = sort(D);					% Sort in columns.      % L are the labels of the nearest-to-further neighbors for the objects from I.			L = nlab(L)';										Ymax = zeros(nn,m);       			Yc = zeros(nn,m);			if islabtype(a,'soft')				error('Soft labels not yet allowed for optimisation of k')			end			for j = 1:c				Y = +(L == j);						% Mark by 1 the positions of the class j in 				for n = 3:m             % the ordered distances to the objects from I.  					Y(:,n) = Y(:,n-1) + Y(:,n);				end				% Y is NN x M; for objects from I, Y(:,P) counts all the objects 				% from the class j that are within the first P nearest neighbors.				Y(:,1) = zeros(nn,1);   				J = Y > Ymax;						% J is the index of the 'winning' class 				Ymax(J) = Y(J);					% within the first nearest neighbors.				Yc(J) = j*ones(size(Yc(J)));			end			z = z + sum(Yc == repmat(nlab(I),1,m),1); % number of objects correctly classified for knn = 0,1,2,...		end		name = 'K-NN Classifier';		[e,knn] = max(z); % select best neighborhood size		knn = knn-1;      % correct for leave-one-out		knn = max(knn,1); % correct for pathological case knn = 0 (it appeared to exist!: all objects were		                  % incorrectly classified for all neighbood sizes).		e = 1 - e/m;		ek = 1 - z/m;		ek(1) = []; 		else													% knn is fixed		if (knn > m)			error('The number of neighbors should not be larger than number of training objects.')		end		if (nargout > 2)			e = testk(a,knn);		end		name = [num2str(knn) '-NN Classifier'];	end	W = mapping('knn_map','trained',{a,knn},getlablist(a),k,c);	W = setname(W,name);	W = setcost(W,a);return