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lib/AI/FANN/Evolving.pm  view on Meta::CPAN

		'FANN_SIGMOID_SYMMETRIC'          => FANN_SIGMOID_SYMMETRIC,
		'FANN_SIGMOID_SYMMETRIC_STEPWISE' => FANN_SIGMOID_SYMMETRIC_STEPWISE,
#		'FANN_GAUSSIAN'                   => FANN_GAUSSIAN, # range is between 0 and 1
		'FANN_GAUSSIAN_SYMMETRIC'         => FANN_GAUSSIAN_SYMMETRIC,
		'FANN_GAUSSIAN_STEPWISE'          => FANN_GAUSSIAN_STEPWISE,
#		'FANN_ELLIOT'                     => FANN_ELLIOT, # range is between 0 and 1
		'FANN_ELLIOT_SYMMETRIC'           => FANN_ELLIOT_SYMMETRIC,
#		'FANN_LINEAR_PIECE'               => FANN_LINEAR_PIECE, # range is between 0 and 1
		'FANN_LINEAR_PIECE_SYMMETRIC'     => FANN_LINEAR_PIECE_SYMMETRIC,
		'FANN_SIN_SYMMETRIC'              => FANN_SIN_SYMMETRIC,
		'FANN_COS_SYMMETRIC'              => FANN_COS_SYMMETRIC,
#		'FANN_SIN'                        => FANN_SIN, # range is between 0 and 1
#		'FANN_COS'                        => FANN_COS, # range is between 0 and 1
	},
	'errorfunc' => {
		'FANN_ERRORFUNC_LINEAR' => FANN_ERRORFUNC_LINEAR,
		'FANN_ERRORFUNC_TANH'   => FANN_ERRORFUNC_TANH,	
	},
	'stopfunc' => {
		'FANN_STOPFUNC_MSE' => FANN_STOPFUNC_MSE,
#		'FANN_STOPFUNC_BIT' => FANN_STOPFUNC_BIT,
	}	
);

my %constant;
for my $hashref ( values %enum ) {
	while( my ( $k, $v ) = each %{ $hashref } ) {
		$constant{$k} = $v;
	}
}

my %default = (
	'error'               => 0.0001,
	'epochs'              => 5000,
	'train_type'          => 'ordinary',
	'epoch_printfreq'     => 100,
	'neuron_printfreq'    => 0,
	'neurons'             => 15,
	'activation_function' => FANN_SIGMOID_SYMMETRIC,
);

=head1 NAME

AI::FANN::Evolving - artificial neural network that evolves

=head1 METHODS

=over

=item new

Constructor requires 'file', or 'data' and 'neurons' arguments. Optionally takes 
'connection_rate' argument for sparse topologies. Returns a wrapper around L<AI::FANN>.

=cut

sub new {
	my $class = shift;
	my %args  = @_;
	my $self  = {};
	bless $self, $class;
	$self->_init(%args);
	
	# de-serialize from a file
	if ( my $file = $args{'file'} ) {
		$self->{'ann'} = AI::FANN->new_from_file($file);
		$log->debug("instantiating from file $file");
		return $self;
	}
	
	# build new topology from input data
	elsif ( my $data = $args{'data'} ) {
		$log->debug("instantiating from data $data");
		$data = $data->to_fann if $data->isa('AI::FANN::Evolving::TrainData');
		
		# prepare arguments
		my $neurons = $args{'neurons'} || ( $data->num_inputs + 1 );
		my @sizes = ( 
			$data->num_inputs, 
			$neurons,
			$data->num_outputs
		);
		
		# build topology
		if ( $args{'connection_rate'} ) {
			$self->{'ann'} = AI::FANN->new_sparse( $args{'connection_rate'}, @sizes );
		}
		else {
			$self->{'ann'} = AI::FANN->new_standard( @sizes );
		}
		
		# finalize the instance
		return $self;
	}
	
	# build new ANN using argument as a template
	elsif ( my $ann = $args{'ann'} ) {
		$log->debug("instantiating from template $ann");
		
		# copy the wrapper properties
		%{ $self } = %{ $ann };
		
		# instantiate the network dimensions
		$self->{'ann'} = AI::FANN->new_standard(
			$ann->num_inputs, 
			$ann->num_inputs + 1,
			$ann->num_outputs,
		);
		
		# copy the AI::FANN properties
		$ann->template($self->{'ann'});
		return $self;
	}
	else {
		die "Need 'file', 'data' or 'ann' argument!";
	}
}

=item template

Uses the object as a template for the properties of the argument, e.g.