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Note, the old method of calling crunch on the values still works just as well.	

The first two arguments may be array refs (or now, strings), and they may be 
of different lengths.

Options should be written on hash form. There are three options:
	 
	 inc      =>    $learning_gradient
	 max      =>    $maximum_iterations
	 error    =>    $maximum_allowable_percentage_of_error
	 degrade  =>    $degrade_increment_flag
	 

$learning_gradient is an optional value used to adjust the weights of the internal
connections. If $learning_gradient is ommitted, it defaults to 0.002.
 
$maximum_iterations is the maximum numbers of iteration the loop should do.
It defaults to 1024.  Set it to 0 if you never want the loop to quit before
the pattern is perfectly learned.

$maximum_allowable_percentage_of_error is the maximum allowable error to have. If 
this is set, then learn() will return when the perecentage difference between the
actual results and desired results falls below $maximum_allowable_percentage_of_error.
If you do not include 'error', or $maximum_allowable_percentage_of_error is set to -1,
then learn() will not return until it gets an exact match for the desired result OR it
reaches $maximum_iterations.

$degrade_increment_flag is a simple flag used to allow/dissalow increment degrading
during learning based on a product of the error difference with several other factors.
$degrade_increment_flag is off by default. Setting $degrade_increment_flag to a true
value turns increment degrading on. 

In previous module releases $degrade_increment_flag was not used, as increment degrading
was always on. In this release I have looked at several other network types as well
as several texts and decided that it would be better to not use increment degrading. The
option is still there for those that feel the inclination to use it. I have found some areas

mesh.htm  view on Meta::CPAN

<PRE>
        $net-&gt;learn('corn', 'cob');
</PRE>
<P>Note, the old method of calling crunch on the values still works just as well.</P>
<P>The first two arguments may be array refs (or now, strings), and they may be 
of different lengths.</P>
<P>Options should be written on hash form. There are three options:
</P>
<PRE>
         inc      =&gt;    $learning_gradient
         max      =&gt;    $maximum_iterations
         error    =&gt;    $maximum_allowable_percentage_of_error
         degrade  =&gt;    $degrade_increment_flag</PRE>
<P>$learning_gradient is an optional value used to adjust the weights of the internal
connections. If $learning_gradient is ommitted, it defaults to 0.002.
</P>
<P>$maximum_iterations is the maximum numbers of iteration the loop should do.
It defaults to 1024.  Set it to 0 if you never want the loop to quit before
the pattern is perfectly learned.</P>
<P>$maximum_allowable_percentage_of_error is the maximum allowable error to have. If 
this is set, then <A HREF="#item_learn"><CODE>learn()</CODE></A> will return when the perecentage difference between the
actual results and desired results falls below $maximum_allowable_percentage_of_error.
If you do not include 'error', or $maximum_allowable_percentage_of_error is set to -1,
then <A HREF="#item_learn"><CODE>learn()</CODE></A> will not return until it gets an exact match for the desired result OR it
reaches $maximum_iterations.</P>
<P>$degrade_increment_flag is a simple flag used to allow/dissalow increment degrading
during learning based on a product of the error difference with several other factors.
$degrade_increment_flag is off by default. Setting $degrade_increment_flag to a true
value turns increment degrading on.</P>
<P>In previous module releases $degrade_increment_flag was not used, as increment degrading
was always on. In this release I have looked at several other network types as well
as several texts and decided that it would be better to not use increment degrading. The
option is still there for those that feel the inclination to use it. I have found some areas
that do need the degrade flag to work at a faster speed. See test.pl for an example. If
the degrade flag wasn't in test.pl, it would take a very long time to learn.</P>