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<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>
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