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

		quickprop_decay                      => \&_mutate_double,
		quickprop_mu                         => \&_mutate_double,
		rprop_increase_factor                => \&_mutate_double,
		rprop_decrease_factor                => \&_mutate_double,
		rprop_delta_min                      => \&_mutate_double,
		rprop_delta_max                      => \&_mutate_double,
		cascade_output_change_fraction       => \&_mutate_double,
		cascade_candidate_change_fraction    => \&_mutate_double,
		cascade_output_stagnation_epochs     => \&_mutate_int,
		cascade_candidate_stagnation_epochs  => \&_mutate_int,
		cascade_max_out_epochs               => \&_mutate_int,
		cascade_max_cand_epochs              => \&_mutate_int,
		cascade_num_candidate_groups         => \&_mutate_int,
		bit_fail_limit                       => \&_mutate_double, # 'fann_type',
		cascade_weight_multiplier            => \&_mutate_double, # 'fann_type',
		cascade_candidate_limit              => \&_mutate_double, # 'fann_type',
	)
}

=item defaults

Getter/setter to influence default ANN configuration

=cut

sub defaults {
	my $self = shift;
	my %args = @_;
	for my $key ( keys %args ) {
		$log->info("setting $key to $args{$key}");
		if ( $key eq 'activation_function' ) {
			$args{$key} = $constant{$args{$key}};
		}
		$default{$key} = $args{$key};
	}
	return %default;
}

sub _init {
	my $self = shift;
	my %args = @_;
	for ( qw(error epochs train_type epoch_printfreq neuron_printfreq neurons activation_function) ) {
		$self->{$_} = $args{$_} // $default{$_};
	}
	return $self;
}

=item clone

Clones the object

=cut

sub clone {
	my $self = shift;
	$log->debug("cloning...");
	
	# we delete the reference here so we can use 
	# Algorithm::Genetic::Diploid::Base's cloning method, which
	# dumps and loads from YAML. This wouldn't work if the 
	# reference is still attached because it cannot be 
	# stringified, being an XS data structure
	my $ann = delete $self->{'ann'};
	my $clone = $self->SUPER::clone;
	
	# clone the ANN by writing it to a temp file in "FANN/FLO"
	# format and reading that back in, then delete the file
	my ( $fh, $file ) = tempfile();
	close $fh;
	$ann->save($file);
	$clone->{'ann'} = __PACKAGE__->new_from_file($file);
	unlink $file;
	
	# now re-attach the original ANN to the invocant
	$self->{'ann'} = $ann;
	
	return $clone;
}

=item train

Trains the AI on the provided data object

=cut

sub train {
	my ( $self, $data ) = @_;
	if ( $self->train_type eq 'cascade' ) {
		$log->debug("cascade training");
	
		# set learning curve
		$self->cascade_activation_functions( $self->activation_function );
		
		# train
		$self->{'ann'}->cascadetrain_on_data(
			$data,
			$self->neurons,
			$self->neuron_printfreq,
			$self->error,
		);
	}
	else {
		$log->debug("normal training");
	
		# set learning curves
		$self->hidden_activation_function( $self->activation_function );
		$self->output_activation_function( $self->activation_function );
		
		# train
		$self->{'ann'}->train_on_data(
			$data,
			$self->epochs,
			$self->epoch_printfreq,
			$self->error,
		);	
	}
}

=item enum_properties

Returns a hash whose keys are names of enums and values the possible states for the
enum

=cut

=item error

Getter/setter for the error rate. Default is 0.0001

=cut

sub error {
	my $self = shift;
	if ( @_ ) {