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| author | ziejd2 | 2017-09-28 15:04:40 -0500 |
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| committer | ziejd2 | 2017-09-28 15:04:40 -0500 |
| commit | 8070dc963753142bb86c4ed698d91fd623ed28e7 (patch) | |
| tree | d0f6dd8fc46a49b819aa55c1a90faa14d8448883 /sourcecodes/bnt-master/nethelp3.3/gtmem.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/gtmem.htm b/sourcecodes/bnt-master/nethelp3.3/gtmem.htm new file mode 100644 index 00000000..95c385b1 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/gtmem.htm @@ -0,0 +1,88 @@ +<html> +<head> +<title> +Netlab Reference Manual gtmem +</title> +</head> +<body> +<H1> gtmem +</H1> +<h2> +Purpose +</h2> +EM algorithm for Generative Topographic Mapping. + +<p><h2> +Synopsis +</h2> +<PRE> + +[net, options, errlog] = gtmem(net, t, options) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>[net, options, errlog] = gtmem(net, t, options)</CODE> uses the Expectation +Maximization algorithm to estimate the parameters of +a GTM defined by a data structure <CODE>net</CODE>. +The matrix <CODE>t</CODE> represents the data whose expectation +is maximized, with each row corresponding to a vector. It is assumed +that the latent data <CODE>net.X</CODE> has been set following a call to +<CODE>gtminit</CODE>, for example. + +The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs error +values in the return argument <CODE>errlog</CODE>. +If <CODE>options(1)</CODE> is set to 0, +then only warning messages are displayed. If <CODE>options(1)</CODE> is -1, +then nothing is displayed. + +<p><CODE>options(3)</CODE> is a measure of the absolute precision required of the error +function at the solution. If the change in log likelihood between two steps of +the EM algorithm is less than this value, then the function terminates. + +<p><CODE>options(14)</CODE> is the maximum number of iterations; default 100. + +<p>The optional return value <CODE>options</CODE> contains the final error value +(i.e. data log likelihood) in +<CODE>options(8)</CODE>. + +<p><h2> +Examples +</h2> +The following code fragment sets up a GTM, initialises +the latent data sample and RBF +parameters from the data, sets the options and trains the model. +<PRE> + +% Create and initialise GTM model +net = gtm(latentdim, nlatent, datadim, numrbfcentres, ... + 'gaussian', 0.1); + +<p>options = foptions; +options(1) = -1; +options(7) = 1; % Set width factor of RBF +net = gtminit(net, options, data, 'regular', latentshape, [4 4]); + +<p>options = foptions; +options(14) = 30; +options(1) = 1; +[net, options] = gtmem(net, data, options); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="gtm.htm">gtm</a></CODE>, <CODE><a href="gtminit.htm">gtminit</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 |