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package AI::FANN;

our $VERSION = '0.10';

use strict;
use warnings;
use Carp;

require XSLoader;
XSLoader::load('AI::FANN', $VERSION);

use Exporter qw(import);

{
    my @constants = _constants();

    our %EXPORT_TAGS = ( 'all' => [ @constants ] );
    our @EXPORT_OK = ( @{ $EXPORT_TAGS{'all'} } );

    require constant;
    for my $constant (@constants) {
        constant->import($constant, $constant);
    }
}

sub num_neurons {

    @_ == 1 or croak "Usage: AI::FANN::get_neurons(self)";

    my $self = shift;
    if (wantarray) {
        map { $self->layer_num_neurons($_) } (0 .. $self->num_layers - 1);
    }
    else {
        $self->total_neurons;
    }
}

1;
__END__

=head1 NAME

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

=head1 SYNOPSIS

Train...

  use AI::FANN qw(:all);

  # create an ANN with 2 inputs, a hidden layer with 3 neurons and an
  # output layer with 1 neuron:
  my $ann = AI::FANN->new_standard(2, 3, 1);

  $ann->hidden_activation_function(FANN_SIGMOID_SYMMETRIC);
  $ann->output_activation_function(FANN_SIGMOID_SYMMETRIC);

  # create the training data for a XOR operator:
  my $xor_train = AI::FANN::TrainData->new( [-1, -1], [-1],
                                            [-1, 1], [1],
                                            [1, -1], [1],
                                            [1, 1], [-1] );

  $ann->train_on_data($xor_train, 500000, 1000, 0.001);

  $ann->save("xor.ann");

Run...

  use AI::FANN;

  my $ann = AI::FANN->new_from_file("xor.ann");

  for my $a (-1, 1) {
    for my $b (-1, 1) {
      my $out = $ann->run([$a, $b]);
      printf "xor(%f, %f) = %f\n", $a, $b, $out->[0];
    }
  }

=head1 DESCRIPTION


  WARNING:  THIS IS A VERY EARLY RELEASE,
            MAY CONTAIN CRITICAL BUGS!!!