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Netlab Reference Manual somtrain
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<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)


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author	wolffd
date	Tue, 10 Feb 2015 15:05:51 +0000
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