%KNN_MAP Map a dataset on a K-NN classifier%%   F = KNN_MAP(A,W)% % INPUT%   A  Dataset%   W  K-NN classifier trained by KNNC%% OUTPUT%   F  Posterior probabilities%% DESCRIPTION  % Maps the dataset A by the K-NN classifier W on the [0,1] interval for % each of the classes that W is trained on. The posterior probabilities, % stored in F, are computed in the following ways:% soft labeled training set: the normalised average of the soft labels %              of the K neighbors.% crisp labeled training set, K = 1: normalisation of sigm(log(F)) with %              F(1:C) = sum(NN_Dist(1:C))./NN_Dist(1:C) - 1%              in which C is the number of classes and NN_Dist stores%              the distance to the nearest neighbor of each class.% crisp labeled training set, K > 1: normalisation of%              (N(1:C) + 1)/(K+C), in which N stores the number of%              objects per class within the K first neighbors.%% This routine is called automatically to determine A*W if W is trained % by KNNC.%% Warning: Class prior probabilities in the dataset A are neglected.%% SEE ALSO% MAPPINGS, DATASETS, KNNC, TESTK% 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: knn_map.m,v 1.3 2007/06/19 11:44:14 duin Exp $function F = knn_map(T,W)	prtrace(mfilename);	% Get the training data and parameters from the mapping:	data = getdata(W);	a = data{1};	knn = data{2};	[m,k,c] = getsize(a);	nlab = getnlab(a);	% If there is no test set, then the leave-one-out is done on the	% training set (see TESTK).	if isempty(T) 		T = a;		loo = 1;	else		loo = 0;	end	[mt,kt] = size(T);	if (kt ~= k), error('Wrong feature size'); end	r = classsizes(a);	[num,n] = prmem(mt,m);				% Check the available memory. 	F = ones(mt,c);	D = ones(mt,c);	% Loop in batches. 	for i = 0:num-1											if (i == num-1)			nn = mt - num*n + n;		else			nn = n;		end		range = [i*n+1:i*n+nn];		if loo,			DD = +distm(a,a(range,:));			dmax = max(DD(:));			% Set distances to itself at INF to find later the nearest			% neighbors more easily			DD(i*n+1:m+1:i*n+nn*m) = inf*ones(1,nn); 		else			DD = distm(+a,+T(range,:));			dmax = max(DD(:));		end		J = find(isnan(DD));		if length(J) > 0			DD(J) = dmax*10;		end		[DD,L] = sort(DD);     					switch getlabtype(a)						case 'soft'				for j=1:c					F(range,j) = sum(reshape(a.targets(L(1:knn,:),j),knn,m),1)';				end							case 'crisp'				L = reshape(nlab(L),size(L));				% Find labels.				% Find label frequencies.				for j = 1:c																		F(range,j) = sum(L(1:knn,:)==j,1)';				end							otherwise				error('Illegal label type')		end		% Estimate posterior probabilities		if islabtype(a,'crisp') 			if (knn >= 2)                      % Use Bayes estimators on frequencies.				F(range,:) = (F(range,:)+1)/(knn+c);			else			% Use distances.				K = max(F(range,:)');				for j = 1:c					K = min(K,r(j));					J = reshape(find(L==j),r(j),nn);	% Find the distances between 					J = J(K+[0:nn-1]*r(j));						% that neighbor and other objects.					D(range,j) = DD(J)';							% Number for all classes.				end				F(range,:) = sigm(log(sum(D(range,:),2)*ones(1,c)./ ...									 (D(range,:)+realmin) - 1 + realmin));			end		end		% Normalize the probabilities.		F(range,:) = F(range,:) ./ (sum(F(range,:),2)*ones(1,c));	end	if (isdataset(T))		F = setdata(T,F,getlabels(W));	end;return;