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+<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>
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