annotate toolboxes/FullBNT-1.0.7/netlab3.3/knn.m @ 0:cc4b1211e677 tip

initial commit to HG from Changeset: 646 (e263d8a21543) added further path and more save "camirversion.m"
author Daniel Wolff
date Fri, 19 Aug 2016 13:07:06 +0200
parents
children
rev   line source
Daniel@0 1 function net = knn(nin, nout, k, tr_in, tr_targets)
Daniel@0 2 %KNN Creates a K-nearest-neighbour classifier.
Daniel@0 3 %
Daniel@0 4 % Description
Daniel@0 5 % NET = KNN(NIN, NOUT, K, TR_IN, TR_TARGETS) creates a KNN model NET
Daniel@0 6 % with input dimension NIN, output dimension NOUT and K neighbours.
Daniel@0 7 % The training data is also stored in the data structure and the
Daniel@0 8 % targets are assumed to be using a 1-of-N coding.
Daniel@0 9 %
Daniel@0 10 % The fields in NET are
Daniel@0 11 % type = 'knn'
Daniel@0 12 % nin = number of inputs
Daniel@0 13 % nout = number of outputs
Daniel@0 14 % tr_in = training input data
Daniel@0 15 % tr_targets = training target data
Daniel@0 16 %
Daniel@0 17 % See also
Daniel@0 18 % KMEANS, KNNFWD
Daniel@0 19 %
Daniel@0 20
Daniel@0 21 % Copyright (c) Ian T Nabney (1996-2001)
Daniel@0 22
Daniel@0 23
Daniel@0 24 net.type = 'knn';
Daniel@0 25 net.nin = nin;
Daniel@0 26 net.nout = nout;
Daniel@0 27 net.k = k;
Daniel@0 28 errstring = consist(net, 'knn', tr_in, tr_targets);
Daniel@0 29 if ~isempty(errstring)
Daniel@0 30 error(errstring);
Daniel@0 31 end
Daniel@0 32 net.tr_in = tr_in;
Daniel@0 33 net.tr_targets = tr_targets;
Daniel@0 34