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1 <html>
2 <head>
3 <title>
4 Netlab Reference Manual gtm
5 </title>
6 </head>
7 <body>
8 <H1> gtm
9 </H1>
10 <h2>
11 Purpose
12 </h2>
13 Create a Generative Topographic Map.
14
15 <p><h2>
16 Synopsis
17 </h2>
18 <PRE>
19 net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)
20 net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)
21 </PRE>
22
23
24 <p><h2>
25 Description
26 </h2>
27
28 <p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)</CODE>,
29 takes the dimension of the latent space <CODE>dimlatent</CODE>, the
30 number of data points sampled in the latent space <CODE>nlatent</CODE>, the
31 dimension of the data space <CODE>dimdata</CODE>, the number of centres in the
32 RBF model <CODE>ncentres</CODE>, the activation function for the RBF
33 <CODE>rbfunc</CODE>
34 and returns a data structure <CODE>net</CODE>. The parameters in the
35 RBF and GMM sub-models are set by calls to the corresponding creation routines
36 <CODE>rbf</CODE> and <CODE>gmm</CODE>.
37
38 <p>The fields in <CODE>net</CODE> are
39 <PRE>
40 type = 'gtm'
41 nin = dimension of data space
42 dimlatent = dimension of latent space
43 rbfnet = RBF network data structure
44 gmmnet = GMM data structure
45 X = sample of latent points
46 </PRE>
47
48
49 <p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)</CODE>,
50 sets a Gaussian zero mean prior on the
51 parameters of the RBF model. <CODE>prior</CODE> must be a scalar and represents
52 the inverse variance of the prior distribution. This gives rise to
53 a weight decay term in the error function.
54
55 <p><h2>
56 See Also
57 </h2>
58 <CODE><a href="gtmfwd.htm">gtmfwd</a></CODE>, <CODE><a href="gtmpost.htm">gtmpost</a></CODE>, <CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="gmm.htm">gmm</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>