%KNN_MAP Map a dataset on a K-NN based classifier% % 	F = knn_map(A,W)% % Maps the dataset A by the K-NN classfier W on the [0,1] interval % for each of the classes W is trained on. The posterior % probabilities stored in F sum row-wise to one. W should be trained % by a classifier like knnc. This routine is called automatically to % solve A*W if W is trained by knnc.%% Warning: Class prior probabilities in dataset A are neglected.% % See also mappings, datasets, knnc, testk% 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 Netherlandsfunction F = knn_map(T,W)[a,classlist,type,k,c,v,knn] = mapping(W);[nlab,lablist,m,k,c] = dataset(a);[mt,kt] = size(T);if kt ~= k, error('Wrong feature size'); endr = sum(expandd(nlab,c));[num,n] = prmem(mt,m);F = ones(mt,c);D = ones(mt,c);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];	DD = distm(a,T(range,:));	[DD,L] = sort(DD);     			% sort distances		L = reshape(nlab(L),size(L));	% find labels	for j = 1:c     				% find label frequencies		F(range,j) = sum(L(1:knn,:)==j,1)';	end	K = max(F(range,:)');	for j = 1:c		K = min(K,r(j));		J = reshape(find(L==j),r(j),nn); % find the distances to the		J = J(K+[0:nn-1]*r(j));		% objects of that neighbor		D(range,j) = DD(J)';		% number for all classes	end                                    % estimate posterior probabilities	if knn > 2                          % use Bayes estimators on frequencies		F(range,:) = (F(range,:)+1)/(knn+c);	else                                % use distances		F(range,:) = sigm(log(sum(D(range,:),2)*ones(1,c)./(D(range,:)+realmin) - 1 + realmin));	end	F(range,:) = F(range,:) ./ (sum(F(range,:),2)*ones(1,c));endF = invsig(F);[nlab,lablist,m,k,c,p] = dataset(T);F = dataset(F,getlab(T),classlist,p,lablist);