package Algorithm::MasterMind::EDA;
use warnings;
use strict;
use Carp;
use lib qw(../../lib
../../../lib
../../../../Algorithm-Evolutionary/lib
../../../Algorithm-Evolutionary/lib
../../Algorithm-Evolutionary/lib);
our $VERSION = sprintf "%d.%03d", q$Revision: 1.13 $ =~ /(\d+)\.(\d+)/g;
use base 'Algorithm::MasterMind::Evolutionary_Base';
use Algorithm::MasterMind qw(entropy);
use Algorithm::Evolutionary qw( Individual::BitString
Op::EDA_step );
sub fitness {
my $self = shift;
my $object = shift;
my $combination = $object->{'_str'};
my $matches = $self->matches( $combination );
$object->{'_matches'} = $matches->{'matches'};
my $blacks_and_whites = 1;
for my $r (@{$matches->{'result'}} ) {
$blacks_and_whites += $r->{'blacks'} + $r->{'whites'}+ $self->{'_length'}*$r->{'match'};
}
return $blacks_and_whites;
}
sub initialize {
my $self = shift;
my $options = shift;
for my $o ( keys %$options ) {
$self->{"_$o"} = $options->{$o};
}
$self->{'_fitness'} = 'orig' if !$self->{'_fitness'};
$self->{'_first'} = 'orig' if !$self->{'_first'};
my $length = $options->{'length'};
#----------------------------------------------------------#
# #
my $fitness;
if ( $self->{'_fitness'} eq 'orig' ) {
$fitness = sub { $self->fitness_orig(@_) };
} elsif ( $self->{'_fitness'} eq 'naive' ) {
$fitness = sub { $self->fitness(@_) };
} elsif ( $self->{'_fitness'} eq 'compress' ) {
$fitness = sub { $self->fitness_compress(@_) };
}
#EDA itself
my $eda = new Algorithm::Evolutionary::Op::EDA_step( $fitness,
$options->{'replacement_rate'},
$options->{'pop_size'},
$self->{'_alphabet'});
$self->{'_fitness'} = $fitness;
$self->{'_eda'} = $eda;
}
sub issue_first {
my $self = shift;
my ( $i, $string);
my @alphabet = @{ $self->{'_alphabet'}};
my $half = @alphabet/2;
if ( $self->{'_first'} eq 'orig' ) {
for ( $i = 0; $i < $self->{'_length'}; $i ++ ) {
$string .= $alphabet[ $i % $half ]; # Recommendation Knuth
}
} elsif ( $self->{'_first'} eq 'half' ) {
for ( $i = 0; $i < $self->{'_length'}; $i ++ ) {
$string .= $alphabet[ $i /2 ]; # Recommendation first paper
}
}
$self->{'_first'} = 1; # Flag to know when the second is due
#Initialize population for next step
my @pop;
for ( 0..$self->{'_pop_size'} ) {
my $indi = Algorithm::Evolutionary::Individual::String->new( $self->{'_alphabet'},
$self->{'_length'} );
push( @pop, $indi );
}
$self->{'_pop'}= \@pop;
return $self->{'_last'} = $string;
}
sub issue_next {
my $self = shift;
my $rules = $self->number_of_rules();
my ($match, $best);
my $pop = $self->{'_pop'};
my $eda = $self->{'_eda'};
map( $_->evaluate( $self->{'_fitness'}), @$pop );
my @ranked_pop = sort { $b->{_fitness} <=> $a->{_fitness}; } @$pop;
if ( $ranked_pop[0]->{'_matches'} == $rules ) { #Already found!
return $self->{'_last'} = $ranked_pop[0]->{'_str'};
} else {
my $generations_passed = 0;
my @pop_by_matches;
do {
$eda->apply( $pop );
map( $_->{'_matches'} = $_->{'_matches'}?$_->{'_matches'}:-1, @$pop ); #To avoid warnings
@pop_by_matches = sort { $b->{'_matches'} <=> $a->{'_matches'} } @$pop;
$generations_passed ++;
$best = $pop_by_matches[0];
if ($generations_passed == 15 ) {
$eda->reset( $pop );
$generations_passed = 0;
}
} while ( $best->{'_matches'} < $rules );
return $self->{'_last'} = $best->{'_str'};
}
}
"Many blacks, 0 white"; # Magic true value required at end of module
__END__
=head1 NAME
Algorithm::MasterMind::EDA - Solver using an Estimation of Distribution Algorithm
=head1 SYNOPSIS
use Algorithm::MasterMind::EDA;
my $secret_code = 'EAFC';
my $population_size = 200;
my @alphabet = qw( A B C D E F );
my $solver = new Algorithm::MasterMind::EDA { alphabet => \@alphabet,
length => length( $secret_code ),
pop_size => $population_size};
#The rest, same as the other solvers
=head1 DESCRIPTION
Uses L<Algorithm::Evolutionary> instance of EDAs to solve MM; as there
are two different fitness functions you can use; probably
C<fitness_orig> works better.
=head1 INTERFACE
=head2 initialize
Performs bookkeeping, and assigns flags depending on the
initialization values
=head2 new ( $options )
This function, and all the rest, are directly inherited from base
=head2 issue_first()
Yields the first combination
=head2 issue_next()
Issues the next combination
=head2 feedback()
Obtain the result to the last combination played
=head2 guesses()
Total number of guesses
=head2 evaluated()
Total number of combinations checked to issue result
=head2 number_of_rules ()
Returns the number of rules in the algorithm
=head2 rules()
Returns the rules (combinations, blacks, whites played so far) y a
reference to array
=head2 matches( $string )
Returns a hash with the number of matches, and whether it matches
every rule with the number of blacks and whites it obtains with each
of them
=head2 fitness( $individual )
Computes fitness summing the number of correct black and whites plus
the number of rules the combination meets times the length
=head2 fitness_orig( $individual )
Fitness proposed in the Applied and Soft Computing paper, difference
between the number of blacks/whites obtained by rules against the
secret code and by the combination against the combination in the
rule.
=head1 SEE ALSO
Other solvers: L<Algorithm::MasterMind::Sequential> and
L<Algorithm::MasterMind::Random>. Don't work as well, really.
=head1 AUTHOR
JJ Merelo C<< <jj@merelo.net> >>
=head1 LICENCE AND COPYRIGHT
Copyright (c) 2009, JJ Merelo C<< <jj@merelo.net> >>. All rights reserved.
This module is free software; you can redistribute it and/or
modify it under the same terms as Perl itself. See L<perlartistic>.
=head1 DISCLAIMER OF WARRANTY
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FOR THE SOFTWARE, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
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EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
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