comparison toolboxes/FullBNT-1.0.7/netlab3.3/knnfwd.m @ 0:e9a9cd732c1e tip

first hg version after svn

+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