Mercurial > hg > camir-aes2014
diff toolboxes/FullBNT-1.0.7/nethelp3.3/gtminit.htm @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/toolboxes/FullBNT-1.0.7/nethelp3.3/gtminit.htm Tue Feb 10 15:05:51 2015 +0000 @@ -0,0 +1,66 @@ +<html> +<head> +<title> +Netlab Reference Manual gtminit +</title> +</head> +<body> +<H1> gtminit +</H1> +<h2> +Purpose +</h2> +Initialise the weights and latent sample in a GTM. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gtminit(net, options, data, samptype) +net = gtminit(net, options, data, samptype, lsampsize, rbfsampsize) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = gtminit(net, options, data, samptype)</CODE> takes a GTM <CODE>net</CODE> +and generates a sample of latent data points and sets the centres (and +widths if appropriate) of +<CODE>net.rbfnet</CODE>. + +<p>If the <CODE>samptype</CODE> is <CODE>'regular'</CODE>, then regular grids of latent +data points and RBF centres are created. The dimension of the latent data +space must be +1 or 2. For one-dimensional latent space, the <CODE>lsampsize</CODE> parameter +gives the number of latent points and the <CODE>rbfsampsize</CODE> parameter +gives the number of RBF centres. For a two-dimensional latent space, +these parameters must be vectors of length 2 with the number of points +in each of the x and y directions to create a rectangular grid. The +widths of the RBF basis functions are set by a call to <CODE>rbfsetfw</CODE> +passing <CODE>options(7)</CODE> as the scaling parameter. + +<p>If the <CODE>samptype</CODE> is <CODE>'uniform'</CODE> or <CODE>'gaussian'</CODE> then the +latent data is found by sampling from a uniform or +Gaussian distribution correspondingly. The RBF basis function parameters +are set +by a call to <CODE>rbfsetbf</CODE> with the <CODE>data</CODE> parameter +as dataset and the <CODE>options</CODE> vector. + +<p>Finally, the output layer weights of the RBF are initialised by +mapping the mean of the latent variable to the mean of the target variable, +and the L-dimensional latent variale variance to the variance of the +targets along the first L principal components. + +<p><h2> +See Also +</h2> +<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> +<b>Pages:</b> +<a href="index.htm">Index</a> +<hr> +<p>Copyright (c) Ian T Nabney (1996-9) + + +</body> +</html> \ No newline at end of file