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1 <html> | |
2 <head> | |
3 <title> | |
4 Netlab Reference Manual gtminit | |
5 </title> | |
6 </head> | |
7 <body> | |
8 <H1> gtminit | |
9 </H1> | |
10 <h2> | |
11 Purpose | |
12 </h2> | |
13 Initialise the weights and latent sample in a GTM. | |
14 | |
15 <p><h2> | |
16 Synopsis | |
17 </h2> | |
18 <PRE> | |
19 net = gtminit(net, options, data, samptype) | |
20 net = gtminit(net, options, data, samptype, lsampsize, rbfsampsize) | |
21 </PRE> | |
22 | |
23 | |
24 <p><h2> | |
25 Description | |
26 </h2> | |
27 <CODE>net = gtminit(net, options, data, samptype)</CODE> takes a GTM <CODE>net</CODE> | |
28 and generates a sample of latent data points and sets the centres (and | |
29 widths if appropriate) of | |
30 <CODE>net.rbfnet</CODE>. | |
31 | |
32 <p>If the <CODE>samptype</CODE> is <CODE>'regular'</CODE>, then regular grids of latent | |
33 data points and RBF centres are created. The dimension of the latent data | |
34 space must be | |
35 1 or 2. For one-dimensional latent space, the <CODE>lsampsize</CODE> parameter | |
36 gives the number of latent points and the <CODE>rbfsampsize</CODE> parameter | |
37 gives the number of RBF centres. For a two-dimensional latent space, | |
38 these parameters must be vectors of length 2 with the number of points | |
39 in each of the x and y directions to create a rectangular grid. The | |
40 widths of the RBF basis functions are set by a call to <CODE>rbfsetfw</CODE> | |
41 passing <CODE>options(7)</CODE> as the scaling parameter. | |
42 | |
43 <p>If the <CODE>samptype</CODE> is <CODE>'uniform'</CODE> or <CODE>'gaussian'</CODE> then the | |
44 latent data is found by sampling from a uniform or | |
45 Gaussian distribution correspondingly. The RBF basis function parameters | |
46 are set | |
47 by a call to <CODE>rbfsetbf</CODE> with the <CODE>data</CODE> parameter | |
48 as dataset and the <CODE>options</CODE> vector. | |
49 | |
50 <p>Finally, the output layer weights of the RBF are initialised by | |
51 mapping the mean of the latent variable to the mean of the target variable, | |
52 and the L-dimensional latent variale variance to the variance of the | |
53 targets along the first L principal components. | |
54 | |
55 <p><h2> | |
56 See Also | |
57 </h2> | |
58 <CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtmem.htm">gtmem</a></CODE>, <CODE><a href="pca.htm">pca</a></CODE>, <CODE><a href="rbfsetbf.htm">rbfsetbf</a></CODE>, <CODE><a href="rbfsetfw.htm">rbfsetfw</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> |