AI-FANN-Evolving

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

 'mse'  => error is the mean squared difference between observed and expected
 CODE   => error function is the provided code reference

=back

=cut

sub error_func {
	my $self = shift;
	
	# process the argument
	if ( @_ ) {
		my $arg = shift;
		if ( ref $arg eq 'CODE' ) {
			$self->{'error_func'} = $arg;
			$log->info("using custom error function");
		}
		elsif ( $arg eq 'sign' ) {
			$self->{'error_func'} = \&_sign;
			$log->info("using sign test error function");
		}

script/aivolver  view on Meta::CPAN

	my $yaml = LoadFile($conf);
	$outfile    = $yaml->{'outfile'}         if defined $yaml->{'outfile'};
	$verbosity  = $yaml->{'verbosity'}       if defined $yaml->{'verbosity'};
	$formatter  = $yaml->{'formatter'}       if defined $yaml->{'formatter'};
	%initialize = %{ $yaml->{'initialize'} } if defined $yaml->{'initialize'};
	%data       = %{ $yaml->{'data'} }       if defined $yaml->{'data'};
	%experiment = %{ $yaml->{'experiment'} } if defined $yaml->{'experiment'};
	%ann        = %{ $yaml->{'ann'} }        if defined $yaml->{'ann'};
}

# process command line arguments
GetOptions(
	'verbose+'     => \$verbosity,
	'formatter=s'  => \$formatter,
	'outfile=s'    => \$outfile,
	'initialize=s' => \%initialize,
	'data=s'       => \%data,
	'experiment=s' => \%experiment,
	'ann=s'        => \%ann,
	'help|?'       => sub { pod2usage( '-verbose' => 1 ) },
	'manual'       => sub { pod2usage( '-verbose' => 2 ) },

script/aivolver  view on Meta::CPAN

=item B<-h/--help/-?>

Prints help message and exits.

=item B<-m/--manual>

Prints manual page and exits.

=item B<-v/--verbose>

Increments verbosity of the process. Can be used multiple times.

=item B<-o/--outfile <file.annE<gt>>

File name for the fittest ANN file over all generations.

=item B<-d/--data <key=valueE<gt>>

The C<data> argument is used multiple times, each time followed by a key/value pair
that defines the location of one of the data files. The key/value pairs are as follows:

script/aivolver  view on Meta::CPAN

=item B<gene_count=<countE<gt>>

Defines the number of genes per chromosome. Normally 1 gene (i.e. 1 ANN) suffices.

=back

=item B<-e/--experiment <key=valueE<gt>>

The C<experiment> argument is used multiple times, each time followed by a key/value pair
that defines one of the properties of the evolutionary process. The key/value pairs are
as follows:

=over

=item B<crossover_rate=<rateE<gt>>

p of exchange between chromosomes.

=item B<mutation_rate=<rateE<gt>>

script/aivolver  view on Meta::CPAN

Artificial neural networks (ANNs) are decision-making machines that develop their
capabilities by training on input data. During this training, the ANN builds a
topology of input neurons, hidden neurons, and output neurons that respond to signals
in ways (and with sensitivities) that are determined by a variety of parameters. How
these parameters will interact to give rise to the final functionality of the ANN is
hard to predict I<a priori>, but can be optimized in a variety of ways.

C<aivolver> is a program that does this by evolving parameter settings using a genetic
algorithm that runs for a number of generations determined by C<ngens>. During this
process it writes the intermediate ANNs into the C<workdir> until the best result is
written to the C<outfile>.

The genetic algorithm proceeds by simulating a population of C<individual_count> diploid
individuals that each have C<chromosome_count> chromosomes whose C<gene_count> genes
encode the parameters of the ANN. During each generation, each individual is trained
on a sample data set, and the individual's fitness is then calculated by testing its
predictive abilities on an out-of-sample data set. The fittest individuals (whose
fraction of the total is determined by C<reproduction_rate>) are selected for breeding
in proportion to their fitness.

Before breeding, each individual undergoes a process of mutation, where a fraction of
the ANN parameters is randomly perturbed. Both the size of the fraction and the
maximum extent of the perturbation is determined by C<mutation_rate>. Subsequently, the
homologous chromosomes recombine (i.e. exchange parameters) at a rate determined by
C<crossover_rate>, which then results in (haploid) gametes. These gametes are fused with
those of other individuals to give rise to the next generation.

=head1 TRAINING AND TEST DATA

The data that is used for training the ANNs and for subsequently testing their predictive
abilities are provided as tab-separated tables. An example of an input data set is here: