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        = line 4 =              my $self  = shift;
        = line 5 =              $self->{top_value}=$sum if($sum>$self->{top_value});
        = line 6 =              my $index = intr($sum/$self->{top_value}*$#values);
        = line 7 =              return $values[$index];
        = line 8 =      }</PRE>
<P>Now, the actual function fits in one line of code, but I expanded it a bit
here. Line 1 creates our array of allowed output values. Lines two and
three grab our parameters off the stack which allow us access to the
internals of this node. Line 5 checks to see if the sum output of this
node is higher than any previously encountered, and, if so, it sets
the marker higher. This also shows that you can use the $self refrence
to maintain information across activations. This technique is also used
in the <A HREF="#item_ramp"><CODE>ramp()</CODE></A> activator. Line 6 computes the index into the allowed
values array by first scaling the $sum to be between 0 and 1 and then
expanding it to fit smoothly inside the number of elements in the array. Then
we simply round to an integer and pluck that index from the array and
use it as the output value for that node.</P>
<P>See? It's not that hard! Using custom activation functions, you could do
just about anything with the node that you want to, since you have
access to the node just as if you were a blessed member of that node's object.</P>
<P></P>
<DT><STRONG><A NAME="item_ramp">ramp($r);</A></STRONG><BR>
<DD>
<A HREF="#item_ramp"><CODE>ramp()</CODE></A> preforms smooth ramp activation between 0 and 1 if $r is 1, 
or between -1 and 1 if $r is 2. $r defaults to 1.
<P>You can get this into your namespace with the ':acts' export 
tag as so:
</P>
<PRE>
        use AI::NeuralNet::Mesh ':acts';</PRE>
<P>Note: when using a <A HREF="#item_ramp"><CODE>ramp()</CODE></A> activatior, train the
net at least TWICE on the data set, because the first 
time the <A HREF="#item_ramp"><CODE>ramp()</CODE></A> function searches for the top value in
the inputs, and therefore, results could flucuate.
The second learning cycle guarantees more accuracy.</P>
<P>No code to show here, as it is almost exactly the same as range().</P>
<P></P>
<DT><STRONG><A NAME="item_and_gate">and_gate($threshold);</A></STRONG><BR>
<DD>
Self explanitory, pretty much. This turns the node into a basic AND gate.
$threshold is used to decide if an input is true or false (1 or 0). If 
an input is below $threshold, it is false. $threshold defaults to 0.5.
<P>You can get this into your namespace with the ':acts' export 
tag as so:
</P>
<PRE>
        use AI::NeuralNet::Mesh ':acts';</PRE>
<P>Let's look at the code real quick, as it shows how to get at the indivudal
input connections:</P>
<PRE>
        = line 1 =      sub {
        = line 2 =              my $sum  = shift;
        = line 3 =              my $self = shift;
        = line 4 =              my $threshold = 0.50;
        = line 5 =              for my $x (0..$self-&gt;{_inputs_size}-1) { 
        = line 6 =                      return 0.000001 if(!$self-&gt;{_inputs}-&gt;[$x]-&gt;{value}&lt;$threshold)
        = line 7 =              }
        = line 8 =              return $sum/$self-&gt;{_inputs_size};
        = line 9 =      }</PRE>
<P>Line 2 and 3 pulls in our sum and self refrence. Line 5 opens a loop to go over
all the input lines into this node. Line 6 looks at each input line's value 
and comparse it to the threshold. If the value of that line is below threshold, then
we return 0.000001 to signify a 0 value. (We don't return a 0 value so that the network
doen't get hung trying to multiply a 0 by a huge weight during training [it just will
keep getting a 0 as the product, and it will never learn]). Line 8 returns the mean 
value of all the inputs if all inputs were above threshold.</P>
<P>Very simple, eh? :)
</P>
<P></P>
<DT><STRONG><A NAME="item_or_gate">or_gate($threshold);</A></STRONG><BR>
<DD>
<P>Self explanitory. Turns the node into a basic OR gate, $threshold is used same as above.</P>
<P>You can get this into your namespace with the ':acts' export 
tag as so:
</P>
<PRE>
        use AI::NeuralNet::Mesh ':acts';</PRE>
<P></P></DL>
<P>
<HR>
<H1><A NAME="variables">VARIABLES</A></H1>
<DL>
<DT><STRONG><A NAME="item_%24AI%3A%3ANeuralNet%3A%3AMesh%3A%3AConnector">$AI::NeuralNet::Mesh::Connector</A></STRONG><BR>
<DD>
This is an option is step up from average use of this module. This variable 
should hold the fully qualified name of the function used to make the actual connections
between the nodes in the network. This contains '_c' by default, but if you use
this variable, be sure to add the fully qualified name of the method. For example,
in the ALN example, I use a connector in the main package called <CODE>tree()</CODE> instead of
the default connector. Before I call the <A HREF="#item_new"><CODE>new()</CODE></A> constructor, I use this line of code:
<PRE>
        $AI::NeuralNet::Mesh::Connector = 'main::tree'
</PRE>
<P>The tree() function is called as a blessed method when it is used internally, providing
access to the bless refrence in the first argument. See notes on CUSTOM NETWORK CONNECTORS,
below, for more information on creating your own custom connector.</P>
<P></P>
<DT><STRONG><A NAME="item_%24AI%3A%3ANeuralNet%3A%3AMesh%3A%3ADEBUG">$AI::NeuralNet::Mesh::DEBUG</A></STRONG><BR>
<DD>
This variable controls the verbosity level. It will not hurt anything to set this 
directly, yet most people find it easier to set it using the <A HREF="#item_debug"><CODE>debug()</CODE></A> method, or 
any of its aliases.
<P></P></DL>
<P>
<HR>
<H1><A NAME="custom network connectors">CUSTOM NETWORK CONNECTORS</A></H1>
<P>Creating custom network connectors is step up from average use of this module. 
However, it can be very useful in creating other styles of neural networks, other
than the default fully-connected feed-foward network.</P>
<P>You create a custom connector by setting the variable $AI::NeuralNet::Mesh::Connector
to the fully qualified name of the function used to make the actual connections
between the nodes in the network. This variable contains '_c' by default, but if you use
this variable, be sure to add the fully qualified name of the method. For example,
in the ALN example, I use a connector in the main package called <CODE>tree()</CODE> instead of
the default connector. Before I call the <A HREF="#item_new"><CODE>new()</CODE></A> constructor, I use this line of code:</P>
<PRE>
        $AI::NeuralNet::Mesh::Connector = 'main::tree'
</PRE>
<P>The tree() function is called as a blessed method when it is used internally, providing
access to the bless refrence in the first argument.</P>
<P>Example connector:</P>