06 Nov 2002
02 Dec 2002)
Pattern analysis toolbox.
function [y, l] = knnfwd(net, x)
%KNNFWD Forward propagation through a K-nearest-neighbour classifier.
% [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);
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);
% 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,:),:);
if nargout == 2
% Convert this set of outputs to labels, randomly breaking ties
[temp, l] = max((y + 0.1*rand(size(y))), , 2);