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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/somtrain.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/somtrain.htm b/sourcecodes/bnt-master/nethelp3.3/somtrain.htm new file mode 100644 index 00000000..ecf4e2cb --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/somtrain.htm @@ -0,0 +1,104 @@ +<html> +<head> +<title> +Netlab Reference Manual somtrain +</title> +</head> +<body> +<H1> somtrain +</H1> +<h2> +Purpose +</h2> +Kohonen training algorithm for SOM. + +<p><h2> +Synopsis +</h2> +<PRE> + +net = somtrain{net, options, x) +</PRE> + + +<p><h2> +Description +</h2> +<CODE>net = somtrain{net, options, x)</CODE> uses Kohonen's algorithm to +train a SOM. Both on-line and batch algorithms are implemented. +The learning rate (for on-line) and neighbourhood size decay linearly. +There is no error function minimised during training (so there is +no termination criterion other than the number of epochs), but the +sum-of-squares is computed and returned in <CODE>options(8)</CODE>. + +<p>The optional parameters have the following interpretations. + +<p><CODE>options(1)</CODE> is set to 1 to display error values; also logs learning +rate <CODE>alpha</CODE> and neighbourhood size <CODE>nsize</CODE>. +Otherwise nothing is displayed. + +<p><CODE>options(5)</CODE> determines whether the patterns are sampled randomly +with replacement. If it is 0 (the default), then patterns are sampled +in order. This is only relevant to the on-line algorithm. + +<p><CODE>options(6)</CODE> determines if the on-line or batch algorithm is +used. If it is 1 +then the batch algorithm is used. If it is 0 +(the default) then the on-line algorithm is used. + +<p><CODE>options(14)</CODE> is the maximum number of iterations (passes through +the complete pattern set); default 100. + +<p><CODE>options(15)</CODE> is the final neighbourhood size; default value is the +same as the initial neighbourhood size. + +<p><CODE>options(16)</CODE> is the final learning rate; default value is the same +as the initial learning rate. + +<p><CODE>options(17)</CODE> is the initial neighbourhood size; default 0.5*maximum +map size. + +<p><CODE>options(18)</CODE> is the initial learning rate; default 0.9. This parameter +must be positive. + +<p><h2> +Examples +</h2> +The following example performs on-line training on a SOM in two stages: +ordering and convergence. +<PRE> + +net = som(nin, [8, 7]); +options = foptions; + +<p>% Ordering phase +options(1) = 1; +options(14) = 50; +options(18) = 0.9; % Initial learning rate +options(16) = 0.05; % Final learning rate +options(17) = 8; % Initial neighbourhood size +options(15) = 1; % Final neighbourhood size +net2 = somtrain(net, options, x); + +<p>% Convergence phase +options(14) = 400; +options(18) = 0.05; +options(16) = 0.01; +options(17) = 0; +options(15) = 0; +net3 = somtrain(net2, options, x); +</PRE> + + +<p><h2> +See Also +</h2> +<CODE><a href="kmeans.htm">kmeans</a></CODE>, <CODE><a href="som.htm">som</a></CODE>, <CODE><a href="somfwd.htm">somfwd</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 |