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    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!!!

AI::FANN is a Perl wrapper for the Fast Artificial Neural Network
(FANN) Library available from L<http://fann.sourceforge.net>:

  Fast Artificial Neural Network Library is a free open source neural
  network library, which implements multilayer artificial neural
  networks in C with support for both fully connected and sparsely
  connected networks. Cross-platform execution in both fixed and
  floating point are supported. It includes a framework for easy
  handling of training data sets. It is easy to use, versatile, well
  documented, and fast. PHP, C++, .NET, Python, Delphi, Octave, Ruby,
  Pure Data and Mathematica bindings are available. A reference manual
  accompanies the library with examples and recommendations on how to
  use the library. A graphical user interface is also available for
  the library.

AI::FANN object oriented interface provides an almost direct map to
the C library API. Some differences have been introduced to make it
more perlish:

=over 4

=item *

Two classes are used: C<AI::FANN> that wraps the C C<struct fann> type
and C<AI::FANN::TrainData> that wraps C<struct fann_train_data>.

=item *

Prefixes and common parts on the C function names referring to those
structures have been removed. For instance C
C<fann_train_data_shuffle> becomes C<AI::FANN::TrainData::shuffle> that
will be usually called as...

  $train_data->shuffle;

=item *

Pairs of C get/set functions are wrapped in Perl with dual accessor
methods named as the attribute (and without any C<set_>/C<get_>
prefix). For instance:

  $ann->bit_fail_limit($limit); # sets the bit_fail_limit

  $bfl = $ann->bit_fail_limit;  # gets the bit_fail_limit


Pairs of get/set functions requiring additional indexing arguments are
also wrapped inside dual accessors:

  # sets:
  $ann->neuron_activation_function($layer_ix, $neuron_ix, $actfunc);

lib/AI/FANN.pm  view on Meta::CPAN

  FANN_SIGMOID_SYMMETRIC
  FANN_SIGMOID_SYMMETRIC_STEPWISE
  FANN_GAUSSIAN
  FANN_GAUSSIAN_SYMMETRIC
  FANN_GAUSSIAN_STEPWISE
  FANN_ELLIOT
  FANN_ELLIOT_SYMMETRIC
  FANN_LINEAR_PIECE
  FANN_LINEAR_PIECE_SYMMETRIC
  FANN_SIN_SYMMETRIC
  FANN_COS_SYMMETRIC
  FANN_SIN
  FANN_COS

  # enum fann_errorfunc_enum:
  FANN_ERRORFUNC_LINEAR
  FANN_ERRORFUNC_TANH

  # enum fann_stopfunc_enum:
  FANN_STOPFUNC_MSE
  FANN_STOPFUNC_BIT

=head1 CLASSES

The classes defined by this package are:

=head2 AI::FANN

Wraps C C<struct fann> types and provides the following methods
(consult the C documentation for a full description of their usage):

=over 4

=item AI::FANN->new_standard(@layer_sizes)

-

=item AI::FANN->new_sparse($connection_rate, @layer_sizes)

-

=item AI::FANN->new_shortcut(@layer_sizes)

-

=item AI::FANN->new_from_file($filename)

-

=item $ann->save($filename)

-

=item $ann->run($input)

C<input> is an array with the input values.

returns an array with the values on the output layer.

  $out = $ann->run([1, 0.6]);
  print "@$out\n";

=item $ann->randomize_weights($min_weight, $max_weight)

=item $ann->train($input, $desired_output)

C<$input> and C<$desired_output> are arrays.

=item $ann->test($input, $desired_output)

C<$input> and C<$desired_output> are arrays.

It returns an array with the values of the output layer.

=item $ann->reset_MSE

-

=item $ann->train_on_file($filename, $max_epochs, $epochs_between_reports, $desired_error)

-

=item $ann->train_on_data($train_data, $max_epochs, $epochs_between_reports, $desired_error)

C<$train_data> is a AI::FANN::TrainData object.

=item $ann->cascadetrain_on_file($filename, $max_neurons, $neurons_between_reports, $desired_error)

-

=item $ann->cascadetrain_on_data($train_data, $max_neurons, $neurons_between_reports, $desired_error)

C<$train_data> is a AI::FANN::TrainData object.

=item $ann->train_epoch($train_data)

C<$train_data> is a AI::FANN::TrainData object.

=item $ann->print_connections

-

=item $ann->print_parameters

-

=item $ann->cascade_activation_functions()

returns a list of the activation functions used for cascade training.

=item $ann->cascade_activation_functions(@activation_functions)

sets the list of activation function to use for cascade training.

=item $ann->cascade_activation_steepnesses()

returns a list of the activation steepnesses used for cascade training.

=item $ann->cascade_activation_steepnesses(@activation_steepnesses)

sets the list of activation steepnesses to use for cascade training.

=item $ann->training_algorithm

=item $ann->training_algorithm($training_algorithm)

-

=item $ann->train_error_function

=item $ann->train_error_function($error_function)

-

=item $ann->train_stop_function

=item $ann->train_stop_function($stop_function)

-

=item $ann->learning_rate

=item $ann->learning_rate($rate)

-

=item $ann->learning_momentum

=item $ann->learning_momentum($momentun)

-

=item $ann->bit_fail_limit

=item $ann->bit_fail_limit($bfl)

-

=item $ann->quickprop_decay

=item $ann->quickprop_decay($qpd)

-