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#############################################################################
## This file was generated automatically by Class::HPLOO/0.21
##
## Original file:    ./lib/AI/NNEasy.hploo
## Generation date:  2005-01-16 22:07:24
##
## ** Do not change this file, use the original HPLOO source! **
#############################################################################

#############################################################################
## Name:        NNEasy.pm
## Purpose:     AI::NNEasy
## Author:      Graciliano M. P. 
## Modified by:
## Created:     2005-01-14
## RCS-ID:      
## Copyright:   (c) 2005 Graciliano M. P. 
## Licence:     This program is free software; you can redistribute it and/or
##              modify it under the same terms as Perl itself
#############################################################################


{ package AI::NNEasy ;

  
use strict qw(vars) ; no warnings ;

  
use vars qw(%CLASS_HPLOO @ISA $VERSION) ;

  
$VERSION = '0.06' ;

  
@ISA = qw(Class::HPLOO::Base UNIVERSAL) ;

  
my $CLASS = 'AI::NNEasy' ; sub __CLASS__ { 'AI::NNEasy' } ;

  
use Class::HPLOO::Base ;

  use AI::NNEasy::NN ;
  use Storable qw(freeze thaw) ;
  use Data::Dumper ;
  


  sub NNEasy { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $file = shift(@_) ;
    my @out_types = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $error_ok = shift(@_) ;
    my $in = shift(@_) ;
    my $out = shift(@_) ;
    my @layers = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $conf = shift(@_) ;
    
    $file ||= 'nneasy.nne' ;
  
    if ( $this->load($file) ) {
      return $this ;
    }
  
    my $in_sz  = ref $in  ? $in->{nodes}  : $in ;
    my $out_sz = ref $out ? $out->{nodes} : $out ;
  
    @layers = ($in_sz+$out_sz) if !@layers ;
    
    foreach my $layers_i ( @layers ) {
      $layers_i = $in_sz+$out_sz if $layers_i <= 0 ;
    }
    
    $conf ||= {} ;
    
    my $decay = $$conf{decay} || 0 ; 
    
    my $nn_in  = $this->_layer_conf( { decay=>$decay } , $in ) ;
    my $nn_out  = $this->_layer_conf( { decay=>$decay , activation_function=>'linear' } , $out ) ;
    
    foreach my $layers_i ( @layers ) {
      $layers_i = $this->_layer_conf( { decay=>$decay } , $layers_i ) ;
    }
    
    my $nn_conf = {random_connections=>0 , networktype=>'feedforward' , random_weights=>1 , learning_algorithm=>'backprop' , learning_rate=>0.1 , bias=>1} ;
    foreach my $Key ( keys %$nn_conf ) { $$nn_conf{$Key} = $$conf{$Key} if exists $$conf{$Key} ;}
    
    $this->{NN_ARGS} = [[ $nn_in , @layers , $nn_out ] , $nn_conf] ;

    $this->{NN} = AI::NNEasy::NN->new( @{$this->{NN_ARGS}} ) ;
    
    $this->{FILE} = $file ;
    
    @out_types = (0,1) if !@out_types ;
    
    @out_types = sort {$a <=> $b} @out_types ;
    
    $this->{OUT_TYPES} = \@out_types ;
    
    if ( $error_ok <= 0 ) {
      my ($min_dif , $last) ;
      my $i = -1 ;
      foreach my $out_types_i ( @out_types ) {
        ++$i ;