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Diffstat (limited to 'sourcecodes/bnt-master/netlab3.3/knn.m')
| -rw-r--r-- | sourcecodes/bnt-master/netlab3.3/knn.m | 34 |
1 files changed, 34 insertions, 0 deletions
diff --git a/sourcecodes/bnt-master/netlab3.3/knn.m b/sourcecodes/bnt-master/netlab3.3/knn.m new file mode 100644 index 00000000..2a171804 --- /dev/null +++ b/sourcecodes/bnt-master/netlab3.3/knn.m @@ -0,0 +1,34 @@ +function net = knn(nin, nout, k, tr_in, tr_targets) +%KNN Creates a K-nearest-neighbour classifier. +% +% Description +% NET = KNN(NIN, NOUT, K, TR_IN, TR_TARGETS) creates a KNN model NET +% with input dimension NIN, output dimension NOUT and K neighbours. +% The training data is also stored in the data structure and the +% targets are assumed to be using a 1-of-N coding. +% +% The fields in NET are +% type = 'knn' +% nin = number of inputs +% nout = number of outputs +% tr_in = training input data +% tr_targets = training target data +% +% See also +% KMEANS, KNNFWD +% + +% Copyright (c) Ian T Nabney (1996-2001) + + +net.type = 'knn'; +net.nin = nin; +net.nout = nout; +net.k = k; +errstring = consist(net, 'knn', tr_in, tr_targets); +if ~isempty(errstring) + error(errstring); +end +net.tr_in = tr_in; +net.tr_targets = tr_targets; + |