Mercurial > hg > camir-aes2014
comparison toolboxes/FullBNT-1.0.7/netlab3.3/knn.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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-1:000000000000 | 0:e9a9cd732c1e |
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1 function net = knn(nin, nout, k, tr_in, tr_targets) | |
2 %KNN Creates a K-nearest-neighbour classifier. | |
3 % | |
4 % Description | |
5 % NET = KNN(NIN, NOUT, K, TR_IN, TR_TARGETS) creates a KNN model NET | |
6 % with input dimension NIN, output dimension NOUT and K neighbours. | |
7 % The training data is also stored in the data structure and the | |
8 % targets are assumed to be using a 1-of-N coding. | |
9 % | |
10 % The fields in NET are | |
11 % type = 'knn' | |
12 % nin = number of inputs | |
13 % nout = number of outputs | |
14 % tr_in = training input data | |
15 % tr_targets = training target data | |
16 % | |
17 % See also | |
18 % KMEANS, KNNFWD | |
19 % | |
20 | |
21 % Copyright (c) Ian T Nabney (1996-2001) | |
22 | |
23 | |
24 net.type = 'knn'; | |
25 net.nin = nin; | |
26 net.nout = nout; | |
27 net.k = k; | |
28 errstring = consist(net, 'knn', tr_in, tr_targets); | |
29 if ~isempty(errstring) | |
30 error(errstring); | |
31 end | |
32 net.tr_in = tr_in; | |
33 net.tr_targets = tr_targets; | |
34 |