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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual somfwd
5 </title>
6 </head>
7 <body>
8 <H1> somfwd
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Forward propagation through a Self-Organising Map.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19
20 d2 = somfwd(net, x)
21 </PRE>
22
23
24 <p><h2>
25 Description
26 </h2>
27 <CODE>d2 = somfwd(net, x)</CODE> propagates the data matrix <CODE>x</CODE> through
28 a SOM <CODE>net</CODE>, returning the squared distance matrix <CODE>d2</CODE> with
29 dimension <CODE>nin</CODE> by <CODE>num_nodes</CODE>. The $i$th row represents the
30 squared Euclidean distance to each of the nodes of the SOM.
31
32 <p><CODE>[d2, win_nodes] = somfwd(net, x)</CODE> also returns the indices of the
33 winning nodes for each pattern.
34
35 <p><h2>
36 Example
37 </h2>
38
39 <p>The following code fragment creates a SOM with a $5times 5$ map for an
40 8-dimensional data space. It then applies the test data to the map.
41 <PRE>
42
43 net = som(8, [5, 5]);
44 [d2, wn] = somfwd(net, test_data);
45 </PRE>
46
47
48 <p><h2>
49 See Also
50 </h2>
51 <CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somtrain.htm">somtrain</a></CODE><hr>
52 <b>Pages:</b>
53 <a href="index.htm">Index</a>
54 <hr>
55 <p>Copyright (c) Ian T Nabney (1996-9)
56
57
58 </body>
59 </html>