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use warnings;
use strict;
use Carp;

use lib qw(../../lib ../../../../Algorithm-Evolutionary/lib/ 
	   ../../../lib
	   ../../Algorithm-Evolutionary/lib/);

our $VERSION =   sprintf "%d.%03d", q$Revision: 1.8 $ =~ /(\d+)\.(\d+)/g; 

use base 'Algorithm::MasterMind::Evolutionary_Base';

use Algorithm::Evolutionary::Op::String_Mutation; 
use Algorithm::Evolutionary::Op::Permutation; 
use Algorithm::Evolutionary::Op::Uniform_Crossover;
use Algorithm::Evolutionary::Op::Easy;
use Algorithm::Evolutionary::Individual::String;

# ---------------------------------------------------------------------------


sub initialize {
  my $self = shift;
  my $options = shift;
  for my $o ( keys %$options ) {
    $self->{"_$o"} = $options->{$o};
  }

  # Variation operators
  my $m = new Algorithm::Evolutionary::Op::String_Mutation; # Rate = 1
  my $p = new Algorithm::Evolutionary::Op::Permutation; # Rate = 1
  my $crossover_probability = 0.5;
  my $crossover_rate = 3;
  my $c = Algorithm::Evolutionary::Op::Uniform_Crossover->new( $crossover_probability,
							       $crossover_rate ); 

  my $fitness = sub { $self->fitness_orig(@_) };
  my $ga = new Algorithm::Evolutionary::Op::Easy( $fitness, 
						  $options->{'replacement_rate'},
						  [ $m, $p, $c] );
  $self->{'_fitness'} = $fitness;
  $self->{'_ga'} = $ga;

}


sub issue_next {
  my $self = shift;
  my $rules =  $self->number_of_rules();
  my @alphabet = @{$self->{'_alphabet'}};
  my $length = $self->{'_length'};
  my $pop = $self->{'_pop'};
  my $ga = $self->{'_ga'};
  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 @pop_by_matches;
    my $best;
    do {
      $ga->apply( $pop );
      print "Población ", scalar @$pop, "\n";
      map( $_->{'_matches'} = $_->{'_matches'}?$_->{'_matches'}:-1, @$pop ); #To avoid warnings
      @pop_by_matches = sort { $b->{'_matches'} <=> $a->{'_matches'} } @$pop;
      $best = $pop_by_matches[0];
    } while ( $best->{'_matches'} < $rules );
    return  $self->{'_last'} = $best->{'_str'};
  }

}

"some blacks, 0 white"; # Magic true value required at end of module

__END__

=head1 NAME

Algorithm::MasterMind::Evolutionary - Tries to compute new solution from last


=head1 SYNOPSIS

    use Algorithm::MasterMind::Evolutionary;

  
=head1 DESCRIPTION

Mainly used in test functions, and as a way of instantiating base
class. 


=head1 INTERFACE 

=head2 fitness()

Returns the vectorial fitness for each combination, which combines
entropy and the distance to a consistent combination.

=head2 initialize()

Does nothing, really

=head2 new ( $options )

This function, and all the rest, are directly inherited from base

=head2 issue_first ()

Issues the first combination, which might be generated in a particular
way 

=head2 issue_next()

Issues the next combination

=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>.