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annotate toolboxes/FullBNT-1.0.7/netlab3.3/knn.m @ 0:e9a9cd732c1e tip

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first hg version after svn
author wolffd
date Tue, 10 Feb 2015 15:05:51 +0000
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wolffd@0 1 function net = knn(nin, nout, k, tr_in, tr_targets)
wolffd@0 2 %KNN Creates a K-nearest-neighbour classifier.
wolffd@0 3 %
wolffd@0 4 % Description
wolffd@0 5 % NET = KNN(NIN, NOUT, K, TR_IN, TR_TARGETS) creates a KNN model NET
wolffd@0 6 % with input dimension NIN, output dimension NOUT and K neighbours.
wolffd@0 7 % The training data is also stored in the data structure and the
wolffd@0 8 % targets are assumed to be using a 1-of-N coding.
wolffd@0 9 %
wolffd@0 10 % The fields in NET are
wolffd@0 11 % type = 'knn'
wolffd@0 12 % nin = number of inputs
wolffd@0 13 % nout = number of outputs
wolffd@0 14 % tr_in = training input data
wolffd@0 15 % tr_targets = training target data
wolffd@0 16 %
wolffd@0 17 % See also
wolffd@0 18 % KMEANS, KNNFWD
wolffd@0 19 %
wolffd@0 20
wolffd@0 21 % Copyright (c) Ian T Nabney (1996-2001)
wolffd@0 22
wolffd@0 23
wolffd@0 24 net.type = 'knn';
wolffd@0 25 net.nin = nin;
wolffd@0 26 net.nout = nout;
wolffd@0 27 net.k = k;
wolffd@0 28 errstring = consist(net, 'knn', tr_in, tr_targets);
wolffd@0 29 if ~isempty(errstring)
wolffd@0 30 error(errstring);
wolffd@0 31 end
wolffd@0 32 net.tr_in = tr_in;
wolffd@0 33 net.tr_targets = tr_targets;
wolffd@0 34