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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual knnfwd | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> knnfwd | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Forward propagation through a K-nearest-neighbour classifier. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 | |
20 [y, l] = knnfwd(net, x) | |
21 </PRE> | |
22 | |
23 | |
24 <p><h2> | |
25 Description | |
26 </h2> | |
27 <CODE>[y, l] = knnfwd(net, x)</CODE> takes a matrix <CODE>x</CODE> | |
28 of input vectors (one vector per row) | |
29 and uses the <CODE>k</CODE>-nearest-neighbour rule on the training data contained | |
30 in <CODE>net</CODE> to | |
31 produce | |
32 a matrix <CODE>y</CODE> of outputs and a matrix <CODE>l</CODE> of classification | |
33 labels. | |
34 The nearest neighbours are determined using Euclidean distance. | |
35 The <CODE>ij</CODE>th entry of <CODE>y</CODE> counts the number of occurrences that | |
36 an example from class <CODE>j</CODE> is among the <CODE>k</CODE> closest training | |
37 examples to example <CODE>i</CODE> from <CODE>x</CODE>. | |
38 The matrix <CODE>l</CODE> contains the predicted class labels | |
39 as an index 1..N, not as 1-of-N coding. | |
40 | |
41 <p><h2> | |
42 Example | |
43 </h2> | |
44 <PRE> | |
45 | |
46 net = knn(size(xtrain, 2), size(t_train, 2), 3, xtrain, t_train); | |
47 y = knnfwd(net, xtest); | |
48 conffig(y, t_test); | |
49 </PRE> | |
50 | |
51 Creates a 3 nearest neighbour model <CODE>net</CODE> and then applies it to | |
52 the data <CODE>xtest</CODE>. The results are plotted as a confusion matrix with | |
53 <CODE>conffig</CODE>. | |
54 | |
55 <p><h2> | |
56 See Also | |
57 </h2> | |
58 <CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="knn.htm">knn</a></CODE><hr> | |
59 <b>Pages:</b> | |
60 <a href="index.htm">Index</a> | |
61 <hr> | |
62 <p>Copyright (c) Ian T Nabney (1996-9) | |
63 | |
64 | |
65 </body> | |
66 </html> |