AI-FANN-Evolving
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lib/AI/FANN/Evolving/Gene.pm view on Meta::CPAN
package AI::FANN::Evolving::Gene;
use strict;
use warnings;
use List::Util 'shuffle';
use File::Temp 'tempfile';
use Scalar::Util 'refaddr';
use AI::FANN::Evolving;
use Algorithm::Genetic::Diploid::Gene;
use base 'Algorithm::Genetic::Diploid::Gene';
use Data::Dumper;
my $log = __PACKAGE__->logger;
=head1 NAME
AI::FANN::Evolving::Gene - gene that codes for an artificial neural network (ANN)
=head1 METHODS
=over
=item new
Constructor is passed named arguments. Instantiates a trained L<AI::FANN::Evolving> ANN
=cut
sub new {
# initialize self up the inheritance tree
my $self = shift->SUPER::new(@_);
# instantiate and train the FANN object
my $traindata = $self->experiment->traindata;
$self->ann( AI::FANN::Evolving->new( 'data' => $traindata ) );
return $self;
}
=item ann
Getter/setter for an L<AI::FANN::Evolving> ANN
=cut
sub ann {
my $self = shift;
if ( @_ ) {
my $ann = shift;
$log->debug("setting ANN $ann");
return $self->{'ann'} = $ann;
}
else {
$log->debug("getting ANN");
return $self->{'ann'};
}
}
=item make_function
Returns a code reference to the fitness function, which when executed returns a fitness
value and writes the corresponding ANN to file
=cut
sub make_function {
my $self = shift;
my $ann = $self->ann;
my $error_func = $self->experiment->error_func;
$log->debug("making fitness function");
# build the fitness function
return sub {
# train the AI
$ann->train( $self->experiment->traindata );
# isa TrainingData object, this is what we need to use
# to make our prognostications. It is a different data
# set (out of sample) than the TrainingData object that
# the AI was trained on.
my $env = shift;
# this is a number which we try to keep as near to zero
# as possible
my $fitness = 0;
# iterate over the list of input/output pairs
for my $i ( 0 .. ( $env->length - 1 ) ) {
my ( $input, $expected ) = $env->data($i);
my $observed = $ann->run($input);
use Data::Dumper;
$log->debug("Observed: ".Dumper($observed));
$log->debug("Expected: ".Dumper($expected));
# invoke the error_func provided by the experiment
$fitness += $error_func->($observed,$expected);
}
$fitness /= $env->length;
# store result
$self->{'fitness'} = $fitness;
# store the AI
my $outfile = $self->experiment->workdir . "/${fitness}.ann";
$self->ann->save($outfile);
return $self->{'fitness'};
}
}
=item fitness
Stores the fitness value after expressing the fitness function
=cut
sub fitness { shift->{'fitness'} }
=item clone
Clones the object
=cut
sub clone {
my $self = shift;
my $ann = delete $self->{'ann'};
my $ann_clone = $ann->clone;
my $self_clone = $self->SUPER::clone;
$self_clone->ann( $ann_clone );
$self->ann( $ann );
return $self_clone;
}
=item mutate
Mutates the ANN by stochastically altering its properties in proportion to
the mutation_rate
=back
=cut
sub mutate {
my $self = shift;
# probably 0.05
my $mu = $self->experiment->mutation_rate;
# make a clone, whose untrained ANN properties are mutated
my $self_clone = $self->clone;
my $ann = AI::FANN::Evolving->new( 'ann' => $self->ann );
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