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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
|---|---|---|
| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/gtm.htm | |
| parent | 7cc31810d53176e805532b2789955f4eedbce6bb (diff) | |
| download | BNW-8070dc963753142bb86c4ed698d91fd623ed28e7.tar.gz | |
BNW using Octave instead of Matlab.
This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02
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diff --git a/sourcecodes/bnt-master/nethelp3.3/gtm.htm b/sourcecodes/bnt-master/nethelp3.3/gtm.htm new file mode 100644 index 00000000..e02ef536 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gtm.htm @@ -0,0 +1,66 @@ +<html> +<head> +<title> +Netlab Reference Manual gtm +</title> +</head> +<body> +<H1> gtm +</H1> +<h2> +Purpose +</h2> +Create a Generative Topographic Map. + +<p><h2> +Synopsis +</h2> +<PRE> +net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc) +net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior) +</PRE> + + +<p><h2> +Description +</h2> + +<p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc)</CODE>, +takes the dimension of the latent space <CODE>dimlatent</CODE>, the +number of data points sampled in the latent space <CODE>nlatent</CODE>, the +dimension of the data space <CODE>dimdata</CODE>, the number of centres in the +RBF model <CODE>ncentres</CODE>, the activation function for the RBF +<CODE>rbfunc</CODE> +and returns a data structure <CODE>net</CODE>. The parameters in the +RBF and GMM sub-models are set by calls to the corresponding creation routines +<CODE>rbf</CODE> and <CODE>gmm</CODE>. + +<p>The fields in <CODE>net</CODE> are +<PRE> + type = 'gtm' + nin = dimension of data space + dimlatent = dimension of latent space + rbfnet = RBF network data structure + gmmnet = GMM data structure + X = sample of latent points +</PRE> + + +<p><CODE>net = gtm(dimlatent, nlatent, dimdata, ncentres, rbfunc, prior)</CODE>, + sets a Gaussian zero mean prior on the +parameters of the RBF model. <CODE>prior</CODE> must be a scalar and represents +the inverse variance of the prior distribution. This gives rise to +a weight decay term in the error function. + +<p><h2> +See Also +</h2> +<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> +<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 |