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be provided if not set.

The third argument is the targeted Mean Square Error (MSE). When provided,
the traning sequence will compute the maximum MSE seen during an iteration
over the training set, and if it is less than the supplied target, the
training stops.  Computing the MSE at each iteration costs, but you are
certain to not over-train your network.

  $net->train_set([
    [1,1] => [0,1],
    [1,0] => [0,1],
    [0,1] => [0,1],
    [0,0] => [1,0],
  ], 10000, 0.01);

The routine returns the MSE of the last iteration, which is the highest MSE
seen over the whole training set (and not an average MSE).

=head2 C<iterations([$integer])>

If called with a positive integer argument, this method will allow you to set
number of iterations that train_set will use and will return the network
object.  If called without an argument, it will return the number of iterations
it was set to.

  $net->iterations;         # returns 100000
  my @training_data = ( 
    [1,1] => [0,1],
    [1,0] => [0,1],
    [0,1] => [0,1],
    [0,0] => [1,0],
  );
  $net->iterations(100000) # let's have lots more iterations!
      ->train_set(\@training_data);
  
=head2 C<learn_rate($rate)>)

This method, if called without an argument, will return the current learning
rate.  .20 is the default learning rate.

If called with an argument, this argument must be greater than zero and less
than one.  This will set the learning rate and return the object.
  
  $net->learn_rate; #returns the learning rate
  $net->learn_rate(.1)
      ->iterations(100000)
      ->train_set(\@training_data);

If you choose a lower learning rate, you will train the network slower, but you
may get a better accuracy.  A higher learning rate will train the network
faster, but it can have a tendancy to "overshoot" the answer when learning and
not learn as accurately.

=head2 C<infer(\@input)>

This method, if provided with an input array reference, will return an array
reference corresponding to the output values that it is guessing.  Note that
these values will generally be close, but not exact.  For example, with the 
"logical or" program, you might expect results similar to:

  use Data::Dumper;
  print Dumper $net->infer([1,1]);
  
  $VAR1 = [
          '0.00993729281477686',
          '0.990100297418451'
        ];

That clearly has the second output item being close to 1, so as a helper method
for use with a winner take all strategy, we have ...

=head2 C<winner(\@input)>

This method returns the index of the highest value from inferred results:

  print $net->winner([1,1]); # will likely print "1"

For a more comprehensive example of how this is used, see the 
"examples/game_ai.pl" program.

=head1 EXPORT

None by default.

=head1 CAVEATS

This is B<alpha> code.  Very alpha.  Not even close to ready for production,
don't even think about it.  I'm putting it on the CPAN lest it languish on my
hard-drive forever.  Hopefully someone will get some use out of it and think to
send me a patch or two.

=head1 TODO

=over 4

=item * Allow different numbers of layers

=back

=head1 BUGS

Probably.

=head1 SEE ALSO

L<AI::FANN> - Perl wrapper for the Fast Artificial Neural Network library

L<AI::NNFlex> - A base class for implementing neural networks 

L<AI::NeuralNet::BackProp> - A simple back-prop neural net that uses Delta's
and Hebbs' rule

"AI Application Programming by M. Tim Jones, copyright (c) by Charles River
Media, Inc.  

The C code in this module is based heavily upon Mr. Jones backpropogation
network in the book.  The "game ai" example in the examples directory is based
upon an example he has graciously allowed me to use.  I I<had> to use it
because it's more fun than many of the dry examples out there :)

"Naturally Intelligent Systems", by Maureen Caudill and Charles Butler,