AI-FANN-Evolving

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

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

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

		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 {

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

			$args{$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

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

}

=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,

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

		my $value = shift;
		$log->debug("setting neuron printfreq to $value");
		return $self->{'neuron_printfreq'} = $value;
	}
	else {	
		$log->debug("getting neuron printfreq");
		return $self->{'neuron_printfreq'};
	}
}

=item train_type

Getter/setter for the training type: 'cascade' or 'ordinary'. Default is ordinary

=cut

sub train_type {
	my $self = shift;
	if ( @_ ) {
		my $value = lc shift;
		$log->debug("setting train type to $value"); 
		return $self->{'train_type'} = $value;
	}
	else {
		$log->debug("getting train type");
		return $self->{'train_type'};
	}
}

=item activation_function

Getter/setter for the function that maps inputs to outputs. default is 
FANN_SIGMOID_SYMMETRIC

=back

script/aivolver  view on Meta::CPAN

# read input data
my $deps   = join ', ', @{ $data{'dependent'} };
my $ignore = join ', ', @{ $data{'ignore'} };
$log->info("going to read train data $data{file}, ignoring '$ignore', dependent columns are '$deps'");
my $inputdata = AI::FANN::Evolving::TrainData->new(
	'file'      => $data{'file'},
	'dependent' => $data{'dependent'},
	'ignore'    => $data{'ignore'},
);
my ( $traindata, $testdata );
if ( $data{'type'} and lc $data{'type'} eq 'continuous' ) {
	( $traindata, $testdata ) = $inputdata->sample_data( $data{'fraction'} );
}
else {
	( $traindata, $testdata ) = $inputdata->partition_data( $data{'fraction'} );
}

$log->info("number of training data records: ".$traindata->size);
$log->info("number of test data records: ".$testdata->size);

# create first work dir

t/perlcriticrc  view on Meta::CPAN

[BuiltinFunctions::ProhibitStringyEval]
severity = 1
[TestingAndDebugging::ProhibitNoStrict]
allow = refs
[Subroutines::ProhibitSubroutinePrototypes]
severity = 1