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All that run_uc() does is that it automatically calls uncrunch() on the output, regardless
of whether the input was crunch() -ed or not.
	


=item $net->range();

This allows you to limit the possible outputs to a specific set of values. There are several 
ways you can specify the set of values to limit the output to. Each method is shown below. 
When called without any arguements, it will disable output range limits. You will need to re-learn
any data previously learned after disabling ranging, as disabling range invalidates the current
weight matrix in the network.

range() automatically scales the networks outputs to fit inside the size of range you allow, and, therefore,
it keeps track of the maximum output it can expect to scale. Therefore, you will need to learn() 
the whole data set again after calling range() on a network.

Subsequent calls to range() invalidate any previous calls to range()

NOTE: It is recomended, you call range() before you call learn() or else you will get unexpected
results from any run() call after range() .


=item $net->range($bottom..$top);

This is a common form often used in a C<for my $x (0..20)> type of for() constructor. It works
the exact same way. It will allow all numbers from $bottom to $top, inclusive, to be given 
as outputs of the network. No other values will be possible, other than those between $bottom

docs.htm  view on Meta::CPAN

<PRE>
        $net-&gt;uncrunch($net-&gt;run($input_map_ref));</PRE>
<P>All that <A HREF="#item_run_uc"><CODE>run_uc()</CODE></A> does is that it automatically calls <A HREF="#item_uncrunch"><CODE>uncrunch()</CODE></A> on the output, regardless
of whether the input was <A HREF="#item_crunch"><CODE>crunch()</CODE></A> -ed or not.</P>
<P></P>
<DT><STRONG><A NAME="item_range">$net-&gt;range();</A></STRONG><BR>
<DD>
This allows you to limit the possible outputs to a specific set of values. There are several 
ways you can specify the set of values to limit the output to. Each method is shown below. 
When called without any arguements, it will disable output range limits. You will need to re-learn
any data previously learned after disabling ranging, as disabling range invalidates the current
weight matrix in the network.
<P><A HREF="#item_range"><CODE>range()</CODE></A> automatically scales the networks outputs to fit inside the size of range you allow, and, therefore,
it keeps track of the maximum output it can expect to scale. Therefore, you will need to <A HREF="#item_learn"><CODE>learn()</CODE></A> 
the whole data set again after calling <A HREF="#item_range"><CODE>range()</CODE></A> on a network.</P>
<P>Subsequent calls to <A HREF="#item_range"><CODE>range()</CODE></A> invalidate any previous calls to <A HREF="#item_range"><CODE>range()</CODE></A></P>
<P>NOTE: It is recomended, you call <A HREF="#item_range"><CODE>range()</CODE></A> before you call <A HREF="#item_learn"><CODE>learn()</CODE></A> or else you will get unexpected
results from any <A HREF="#item_run"><CODE>run()</CODE></A> call after <A HREF="#item_range"><CODE>range()</CODE></A> .</P>
<P></P>
<DT><STRONG>$net-&gt;range($bottom..$top);</STRONG><BR>
<DD>
This is a common form often used in a <CODE>for my $x (0..20)</CODE> type of <CODE>for()</CODE> constructor. It works
the exact same way. It will allow all numbers from $bottom to $top, inclusive, to be given 
as outputs of the network. No other values will be possible, other than those between $bottom
and $top, inclusive.
<P></P>