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	OUTPUT
) ] );

our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );

our @EXPORT = qw(
);

our $VERSION = '0.02';

sub AUTOLOAD {
    # This AUTOLOAD is used to 'autoload' constants from the constant()
    # XS function.  If a constant is not found then control is passed
    # to the AUTOLOAD in AutoLoader.
    my $constname;
    our $AUTOLOAD;
    ($constname = $AUTOLOAD) =~ s/.*:://;
    croak "& not defined" if $constname eq 'constant';
    my $val = constant($constname, @_ ? $_[0] : 0);
    if ($! != 0) {
	if ($! =~ /Invalid/ || $!{EINVAL}) {
	    $AutoLoader::AUTOLOAD = $AUTOLOAD;
	    goto &AutoLoader::AUTOLOAD;
	}
	else {
	    croak "Your vendor has not defined AI::LibNeural macro $constname";
	}
    }
    {
	no strict 'refs';
	# Fixed between 5.005_53 and 5.005_61
#	if ($] >= 5.00561) {
#	    *$AUTOLOAD = sub () { $val };
#	}
#	else {
	    *$AUTOLOAD = sub { $val };
#	}
    }
    goto &$AUTOLOAD;
}

bootstrap AI::LibNeural $VERSION;

# Preloaded methods go here.

# Autoload methods go after =cut, and are processed by the autosplit program.

1;
__END__

=head1 NAME

AI::LibNeural - Perl extension libneural

=head1 SYNOPSIS

  use AI::LibNeural;

  my $nn = AI::LibNeural->new( 2, 4, 1 );

  # teach it the logical AND
  $nn->train( [ 0, 0 ], [ 0.05 ], 0.0000000005, 0.2 );
  $nn->train( [ 0, 1 ], [ 0.05 ], 0.0000000005, 0.2 );
  $nn->train( [ 1, 0 ], [ 0.05 ], 0.0000000005, 0.2 );
  $nn->train( [ 1, 1 ], [ 0.95 ], 0.0000000005, 0.2 );

  my $result = $nn->run( [ 1, 1 ] );
  # result should be ~ 0.95
  $result = $nn->run( [ 0, 1 ] );
  # result should be ~ 0.05

  $nn->save('and.mem');

=head1 ABSTRACT

  Perl bindings for the libneural c++ neural netowrk library.

=head1 DESCRIPTION

Provides accessors for the libneural library as a perl object. libneural is a
C++ library that impelements a feed-forward back-proprogation neural network.
The interface is extremely simple and should take no more than a few minutes to
master given a reasonable knowledge of back proprogation neural networks.

=head2 FUNCTIONS

=over

=item $nn = AI:LibNeural->new()

Creates an empty AI::LibNeural object, should only be used when the load method
will be called soon after.

=item $nn = AI::LibNeural->new(FILENAME)

Creates a new AI::LibNeural object from the supplied memory file.

=item $nn = AI::LibNeural->new(INTPUTS,HIDDENS,OUTPUTS)

Creates a new AI::LibNeural object with INPUTS input nodes, HIDDENS hidden
nodes, and OUTPUTS output nodes.

=item $nn->train([I1,I2,...],[O1,O2,...],MINERR,TRAINRATE)

Completes a training cycle for the given inputs I1-IN, with the expected
results of O1-OM, where N is the number of inputs and M is the number of
outputs. MINERR is the mean squared error at the output that you wish to be achieved. TRAINRATE is the learning rate to be used.

=item (O1,O2) = $nn->run([I1,I2,...])

Calculate the corresponding outputs (O1-OM) for the given inputs (I1-ON) based
on the previous training. Should only be called after the network has been
suitably trained.

=item NUM = $nn->get_layersize(WHICH)

Retrieves the number of nodes at the specified layer, WHICH. WHICH should be
one of ALL, INPUT, HIDDEN, OUTPUT. Usefully mainly with a network is loaded
from a file.

=item status = $nn->load(FILENAME)