function [y, l] = knnfwd(net, x)
%KNNFWD	Forward propagation through a K-nearest-neighbour classifier.%%	Description%	[Y, L] = KNNFWD(NET, X) takes a matrix X of input vectors (one vector%	per row)   and uses the K-nearest-neighbour rule on the training data%	contained in NET to  produce  a matrix Y of outputs and a matrix L of%	classification labels. The nearest neighbours are determined using%	Euclidean distance. The IJth entry of Y counts the number of%	occurrences that an example from class J is among the K closest%	training examples to example I from X. The matrix L contains the%	predicted class labels as an index 1..N, not as 1-of-N coding.%%	See also%	KMEANS, KNN%%	Copyright (c) Ian T Nabney (1996-2001)
errstring = consist(net, 'knn', x);
if ~isempty(errstring)
  error(errstring);
end

ntest = size(x, 1);		              % Number of input vectors.nclass = size(net.tr_targets, 2);		% Number of classes.% Compute matrix of squared distances between input vectors from the training % and test sets.  The matrix distsq has dimensions (ntrain, ntest).distsq = dist2(net.tr_in, x);% Now sort the distances. This generates a matrix kind of the same % dimensions as distsq, in which each column gives the indices of the% elements in the corresponding column of distsq in ascending order.[vals, kind] = sort(distsq);y = zeros(ntest, nclass);for k=1:net.k  % We now look at the predictions made by the Kth nearest neighbours alone,  % and represent this as a 1-of-N coded matrix, and then accumulate the   % predictions so far.  y = y + net.tr_targets(kind(k,:),:);end
if nargout == 2
  % Convert this set of outputs to labels, randomly breaking ties
  [temp, l] = max((y + 0.1*rand(size(y))), [], 2);end