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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 knnfwd |
| wolffd@0 | 5 </title> |
| wolffd@0 | 6 </head> |
| wolffd@0 | 7 <body> |
| wolffd@0 | 8 <H1> knnfwd |
| wolffd@0 | 9 </H1> |
| wolffd@0 | 10 <h2> |
| wolffd@0 | 11 Purpose |
| wolffd@0 | 12 </h2> |
| wolffd@0 | 13 Forward propagation through 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 [y, l] = knnfwd(net, x) |
| 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>[y, l] = knnfwd(net, x)</CODE> takes a matrix <CODE>x</CODE> |
| wolffd@0 | 28 of input vectors (one vector per row) |
| wolffd@0 | 29 and uses the <CODE>k</CODE>-nearest-neighbour rule on the training data contained |
| wolffd@0 | 30 in <CODE>net</CODE> to |
| wolffd@0 | 31 produce |
| wolffd@0 | 32 a matrix <CODE>y</CODE> of outputs and a matrix <CODE>l</CODE> of classification |
| wolffd@0 | 33 labels. |
| wolffd@0 | 34 The nearest neighbours are determined using Euclidean distance. |
| wolffd@0 | 35 The <CODE>ij</CODE>th entry of <CODE>y</CODE> counts the number of occurrences that |
| wolffd@0 | 36 an example from class <CODE>j</CODE> is among the <CODE>k</CODE> closest training |
| wolffd@0 | 37 examples to example <CODE>i</CODE> from <CODE>x</CODE>. |
| wolffd@0 | 38 The matrix <CODE>l</CODE> contains the predicted class labels |
| wolffd@0 | 39 as an index 1..N, not as 1-of-N coding. |
| wolffd@0 | 40 |
| wolffd@0 | 41 <p><h2> |
| wolffd@0 | 42 Example |
| wolffd@0 | 43 </h2> |
| wolffd@0 | 44 <PRE> |
| wolffd@0 | 45 |
| wolffd@0 | 46 net = knn(size(xtrain, 2), size(t_train, 2), 3, xtrain, t_train); |
| wolffd@0 | 47 y = knnfwd(net, xtest); |
| wolffd@0 | 48 conffig(y, t_test); |
| wolffd@0 | 49 </PRE> |
| wolffd@0 | 50 |
| wolffd@0 | 51 Creates a 3 nearest neighbour model <CODE>net</CODE> and then applies it to |
| wolffd@0 | 52 the data <CODE>xtest</CODE>. The results are plotted as a confusion matrix with |
| wolffd@0 | 53 <CODE>conffig</CODE>. |
| wolffd@0 | 54 |
| wolffd@0 | 55 <p><h2> |
| wolffd@0 | 56 See Also |
| wolffd@0 | 57 </h2> |
| wolffd@0 | 58 <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knn.htm">knn</a></CODE><hr> |
| wolffd@0 | 59 <b>Pages:</b> |
| wolffd@0 | 60 <a href="index.htm">Index</a> |
| wolffd@0 | 61 <hr> |
| wolffd@0 | 62 <p>Copyright (c) Ian T Nabney (1996-9) |
| wolffd@0 | 63 |
| wolffd@0 | 64 |
| wolffd@0 | 65 </body> |
| wolffd@0 | 66 </html> |