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first hg version after svn
| author | wolffd |
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| date | Tue, 10 Feb 2015 15:05:51 +0000 |
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| rev | line source |
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| wolffd@0 | 1 <html> |
| wolffd@0 | 2 <head> |
| wolffd@0 | 3 <title> |
| wolffd@0 | 4 Netlab Reference Manual knn |
| wolffd@0 | 5 </title> |
| wolffd@0 | 6 </head> |
| wolffd@0 | 7 <body> |
| wolffd@0 | 8 <H1> knn |
| wolffd@0 | 9 </H1> |
| wolffd@0 | 10 <h2> |
| wolffd@0 | 11 Purpose |
| wolffd@0 | 12 </h2> |
| wolffd@0 | 13 Creates a K-nearest-neighbour classifier. |
| wolffd@0 | 14 |
| wolffd@0 | 15 <p><h2> |
| wolffd@0 | 16 Synopsis |
| wolffd@0 | 17 </h2> |
| wolffd@0 | 18 <PRE> |
| wolffd@0 | 19 |
| wolffd@0 | 20 net = knn(nin, nout, k, tr_in, tr_targets) |
| wolffd@0 | 21 </PRE> |
| wolffd@0 | 22 |
| wolffd@0 | 23 |
| wolffd@0 | 24 <p><h2> |
| wolffd@0 | 25 Description |
| wolffd@0 | 26 </h2> |
| wolffd@0 | 27 <CODE>net = knn(nin, nout, k, tr_in, tr_targets)</CODE> creates a KNN model <CODE>net</CODE> |
| wolffd@0 | 28 with input dimension <CODE>nin</CODE>, output dimension <CODE>nout</CODE> and <CODE>k</CODE> |
| wolffd@0 | 29 neighbours. The training data is also stored in the data structure and the |
| wolffd@0 | 30 targets are assumed to be using a 1-of-N coding. |
| wolffd@0 | 31 |
| wolffd@0 | 32 <p>The fields in <CODE>net</CODE> are |
| wolffd@0 | 33 <PRE> |
| wolffd@0 | 34 |
| wolffd@0 | 35 type = 'knn' |
| wolffd@0 | 36 nin = number of inputs |
| wolffd@0 | 37 nout = number of outputs |
| wolffd@0 | 38 tr_in = training input data |
| wolffd@0 | 39 tr_targets = training target data |
| wolffd@0 | 40 </PRE> |
| wolffd@0 | 41 |
| wolffd@0 | 42 |
| wolffd@0 | 43 <p><h2> |
| wolffd@0 | 44 See Also |
| wolffd@0 | 45 </h2> |
| wolffd@0 | 46 <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knnfwd.htm">knnfwd</a></CODE><hr> |
| wolffd@0 | 47 <b>Pages:</b> |
| wolffd@0 | 48 <a href="index.htm">Index</a> |
| wolffd@0 | 49 <hr> |
| wolffd@0 | 50 <p>Copyright (c) Ian T Nabney (1996-9) |
| wolffd@0 | 51 |
| wolffd@0 | 52 |
| wolffd@0 | 53 </body> |
| wolffd@0 | 54 </html> |