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AI-NNEasy

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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' ;

lib/AI/NNEasy.pm view on Meta::CPAN

      $error_ok = $min_dif / 2 ;
      $error_ok -= $error_ok*0.1 ;
    }
    
    $this->{ERROR_OK} = $error_ok ;
    
    return $this ;
  }
  
  sub _layer_conf { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $def = shift(@_) ;
    my $conf = shift(@_) ;
    
    $def ||= {} ;
    $conf = { nodes=>$conf } if !ref($conf) ;
    
    foreach my $Key ( keys %$def ) { $$conf{$Key} = $$def{$Key} if !exists $$conf{$Key} ;}
  
    my $layer_conf  = {nodes=>1  , persistent_activation=>0 , decay=>0 , random_activation=>0 , threshold=>0 , activation_function=>'tanh' , random_weights=>1} ;
    foreach my $Key ( keys %$layer_conf ) { $$layer_conf{$Key} = $$conf{$Key} if exists $$conf{$Key} ;}

    return $layer_conf ;
  }
  
  sub reset_nn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    $this->{NN} = AI::NNEasy::NN->new( @{ $this->{NN_ARGS} } ) ;
  }
  
  sub load { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $file = shift(@_) ;
    
    $file ||= $this->{FILE} ;
    if ( -s $file ) {
      open (my $fh, $file) ;
      my $dump = join '' , <$fh> ;
      close ($fh) ;
      
      my $restored = thaw($dump) ;

lib/AI/NNEasy.pm view on Meta::CPAN

        my $fl = $this->{FILE} ;
        %$this = %$restored ;
        $this->{FILE} = $fl if $fl ;
        return 1 ;
      }
    }
    return ;
  }
  
  sub save { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $file = shift(@_) ;
    
    $file ||= $this->{FILE} ;
        
    my $dump = freeze( {%$this} ) ;
    open (my $fh,">$this->{FILE}") ;
    print $fh $dump ;
    close ($fh) ;
  }
  
  sub learn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $in = shift(@_) ;
    my $out = shift(@_) ;
    my $n = shift(@_) ;
    
    $n ||= 100 ;
    
    my $err ;
    for (1..100) {
      $this->{NN}->run($in) ;
      $err = $this->{NN}->learn($out) ;
    }
    
    $err *= -1 if $err < 0 ;
    return $err ;
  }
  
  *_learn_set_get_output_error = \&_learn_set_get_output_error_c ;
  
  sub _learn_set_get_output_error_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $set = shift(@_) ;
    my $error_ok = shift(@_) ;
    my $ins_ok = shift(@_) ;
    my $verbose = shift(@_) ;
    
    for (my $i = 0 ; $i < @$set ; $i+=2) {
      $this->{NN}->run($$set[$i]) ;
      $this->{NN}->learn($$set[$i+1]) ;
    }

lib/AI/NNEasy.pm view on Meta::CPAN

    $err /= $ins_ok ;
    
    return ( $err , $learn_ok , $print ) ;
  }
  
  
  
  
    
  sub learn_set { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my @set = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $ins_ok = shift(@_) ;
    my $limit = shift(@_) ;
    my $verbose = shift(@_) ;
    
    my $ins_sz = @set / 2 ;

    $ins_ok ||= $ins_sz ;

lib/AI/NNEasy.pm view on Meta::CPAN

        }
      }

      print "epoch $counter : error = $err : ok = $learn_ok\n" if $verbose > 1 ;
      
    }
    
  }
  
  sub get_set_error { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my @set = ref($_[0]) eq 'ARRAY' ? @{ shift(@_) } : ( ref($_[0]) eq 'HASH' ? %{ shift(@_) } : shift(@_) ) ;
    my $ins_ok = shift(@_) ;
    
    my $ins_sz = @set / 2 ;

    $ins_ok ||= $ins_sz ;
  
    my $err ;
    for (my $i = 0 ; $i < @set ; $i+=2) {

lib/AI/NNEasy.pm view on Meta::CPAN

      my $er = $this->{NN}->RMSErr($set[$i+1]) ;
      $er *= -1 if $er < 0 ;
      $err += $er ;
    }
    
    $err /= $ins_ok ;
    return $err ;
  }
  
  sub run { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $in = shift(@_) ;
    
    $this->{NN}->run($in) ;
    my $out = $this->{NN}->output() ;
    return $out ;
  }
  
  sub run_get_winner { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    my $out = $this->run(@_) ;
    
    foreach my $out_i ( @$out ) {
      $out_i = $this->out_type_winner($out_i) ;
    }
    
    return $out ;
  }
  
  sub out_type_winner { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $val = shift(@_) ;
    
    my ($out_type , %err) ;
    
    foreach my $types_i ( @{ $this->{OUT_TYPES} } ) {
      my $er = $types_i - $val ;
      $er *= -1 if $er < 0 ;
      $err{$types_i} = $er ;
    }

lib/AI/NNEasy/NN.pm view on Meta::CPAN


  use Class::HPLOO::Base ;

  use AI::NNEasy::NN::layer ;
  use AI::NNEasy::NN::feedforward ;
  use AI::NNEasy::NN::backprop ;

  use vars qw($AUTOLOAD) ;
  
  sub NN { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $params = shift(@_) ;
    my $netParams = shift(@_) ;
    
    my @layers ;

    foreach my $i (keys %$netParams) {
      $this->{$i} = $$netParams{$i};
    }

lib/AI/NNEasy/NN.pm view on Meta::CPAN

      $this->{biasNode} = AI::NNEasy::NN::node->new( {activation_function => 'linear'} ) ;
      $this->{biasNode}->{activation} = 1;
    }

    $this->init ;
    
    return $this ;
  }
  
  sub init { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    my @layers = @{$this->{layers}} ;

    my $currentLayer ;

    foreach my $layer (@layers) {
      # Foreach node we need to make connections east and west
      foreach my $node ( @{$layer->{nodes}} ) {
        # only initialise to the west if layer > 0

lib/AI/NNEasy/NN.pm view on Meta::CPAN

        foreach my $node (@{$layer->{nodes}}) {
          push @{$node->{connectedNodesWest}->{nodes}},$this->{biasNode};
          my $weight = $this->{random_weights} ? rand(1) : 0 ;
          $node->{connectedNodesWest}->{weights}{ $this->{biasNode}->{nodeid} } = $weight ;
        }
      }
    }
  }
  
  sub learn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    &{$this->{learning_algorithm_class} . '::learn'}($this , @_) ;
  }
  
  sub output { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $params = shift(@_) ;
    
    my $outputLayer = defined $$params{layer} ? $this->{layers}[$$params{layer}] : $this->{layers}[-1] ;
    return AI::NNEasy::NN::layer::layer_output($outputLayer) ;
  }
  
  sub linear { my $this = ref($_[0]) ? shift : undef ;my $CLASS = ref($this) || __PACKAGE__ ;my $value = shift(@_) ; return $value ;}
  
  *tanh = \&tanh_c ;
  
  
  
  sub tanh_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $value = shift(@_) ;
    
    if    ($value > 20)  { return 1 ;}
    elsif ($value < -20) { return -1 ;}
    else {
      my $x = exp($value) ;
      my $y = exp(-$value) ;
      return ($x-$y)/($x+$y) ;
    }
  }
  
  *sigmoid = \&sigmoid_c ;
  
  sub sigmoid_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $value = shift(@_) ;
    
    return (1+exp(-$value))**-1 ;
  }
  
  
  
  sub AUTOLOAD { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    my ($name) = ( $AUTOLOAD =~ /(\w+)$/ ) ;
    my $sub = $this->{learning_algorithm_class} . '::' . $name ;
    return &$sub($this,@_) if defined &$sub ;
    my @call = caller ;
    die("Can't find $AUTOLOAD or $sub at @call\n") ;
  }

my $INLINE_INSTALL ; BEGIN { use Config ; my @installs = ($Config{installarchlib} , $Config{installprivlib} , $Config{installsitelib}) ; foreach my $i ( @installs ) { $i =~ s/[\\\/]/\//gs ;} $INLINE_INSTALL = 1 if ( __FILE__ =~ /\.pm$/ && ( join(" ",...

lib/AI/NNEasy/NN/backprop.pm view on Meta::CPAN



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


use Class::HPLOO::Base ;



  sub calc_error { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $outputPatternRef = shift(@_) ;
    
    my @outputPattern = @$outputPatternRef;

    my $outputLayer = $this->{layers}->[-1]->{nodes} ;

    return 0 if @$outputLayer != @outputPattern ;

    my $counter = 0 ;
    foreach my $node (@$outputLayer) {
      $node->{error} = $node->{activation} - $outputPattern[$counter] ;
      ++$counter ;
    }
  }
  
  sub learn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $outputPatternRef = shift(@_) ;
    
    $this->calc_error($outputPatternRef) ;
    $this->hiddenToOutput ;
    $this->hiddenOrInputToHidden if @{$this->{layers}} > 2 ;
    return $this->RMSErr($outputPatternRef) ;    
  }
  
  *hiddenToOutput = \&hiddenToOutput_c ;
  
  sub hiddenToOutput_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    foreach my $node ( @{ $this->{layers}->[-1]->{nodes} } ) {
      foreach my $connectedNode ( @{$node->{connectedNodesWest}->{nodes}} ) {
        $node->{connectedNodesWest}->{weights}->{ $connectedNode->{nodeid} } -= $this->{learning_rate} * $node->{error} * $connectedNode->{activation} ;
        $node->{connectedNodesWest}->{weights}->{ $connectedNode->{nodeid} } = 5 if $node->{connectedNodesWest}->{weights}->{ $connectedNode->{nodeid} } > 5 ;
        $node->{connectedNodesWest}->{weights}->{ $connectedNode->{nodeid} } = -5 if $node->{connectedNodesWest}->{weights}->{ $connectedNode->{nodeid} } < -5 ;
      }
    }
  }
  
  
  
  *hiddenOrInputToHidden = \&hiddenOrInputToHidden_c ;
  
  sub hiddenOrInputToHidden_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    my ( $nodeid , $nodeError , $nodeActivation ) ;
    
    my $learningRate = $this->{learning_rate} ;

    foreach my $layer ( reverse @{$this->{layers}}[0 .. $#{$this->{layers}}-1 ] ) {
      foreach my $node ( @{$layer->{nodes}} ) {
        last if !$node->{connectedNodesWest} ;

lib/AI/NNEasy/NN/backprop.pm view on Meta::CPAN

      }
    }
    
  }

  
  
  *RMSErr = \&RMSErr_c ;
  
  sub RMSErr_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $outputPatternRef = shift(@_) ;
    
    my $outputLayer = $this->{layers}->[-1]->{nodes} ;

    my $sqrErr ;
    my $counter = 0 ;
    foreach my $node (@$outputLayer) {
      $sqrErr += ($node->{activation} - $$outputPatternRef[$counter])**2 ;
      ++$counter ;

lib/AI/NNEasy/NN/feedforward.pm view on Meta::CPAN

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


use Class::HPLOO::Base ;



  *run = \&run_c ;

  sub run_pl { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $inputPatternRef = shift(@_) ;
    
    # Now apply the activation
    my $counter = 0 ;
    foreach my $node ( @{ $this->{layers}->[0]->{nodes} } ) {
      if ( $node->{active} ) {
        if ( $node->{persistent_activation} ) {
          $node->{activation} += $$inputPatternRef[$counter] ;
        }

lib/AI/NNEasy/NN/layer.pm view on Meta::CPAN


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

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

  use Class::HPLOO::Base ;

  use AI::NNEasy::NN::node ;

  sub layer { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $params = shift(@_) ;
    
    $this->{nodes} = [] ;
    for (1 .. $$params{nodes}) { push( @{$this->{nodes}} , AI::NNEasy::NN::node->new($params) ) ;}
    return $this ;
  }
  
  sub layer_output { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $params = shift(@_) ;
    
    my @outputs ;
    foreach my $node ( @{$this->{nodes}} ) {
      push(@outputs , $$node{activation}) ;
    }

    return \@outputs;    
  }

lib/AI/NNEasy/NN/node.pm view on Meta::CPAN


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

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

  use Class::HPLOO::Base ;

  my $NODEID ;

  sub node { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    my $params = shift(@_) ;
    
    $this->{nodeid} = ++$NODEID ;
    
    $this->{activation} = $$params{random_activation} ? rand($$params{random}) : 0 ;

    $this->{random_weights} = $$params{random_weights} ;
    $this->{decay} = $$params{decay} ;
    $this->{adjust_error} = $$params{adjust_error} ;

lib/AI/NNEasy/NN/reinforce.pm view on Meta::CPAN


  use vars qw(%CLASS_HPLOO @ISA) ;

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

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

  use Class::HPLOO::Base ;
  
  sub learn { 
    my $this = ref($_[0]) ? shift : undef ;
    my $CLASS = ref($this) || __PACKAGE__ ;
    
    foreach my $layer ( reverse @{$this->{'layers'}}[ 1 .. $#{$this->{'layers'}} ] ) {
      foreach my $node ( @{$layer->{nodes}} ) {
        foreach my $westNode ( @{$node->{connectedNodesWest}->{nodes}} ) {
          my $dW = $westNode->{activation} * $node->{connectedNodesWest}->{weights}->{ $westNode->{nodeid} } * $this->{learning_rate} ;
          $node->{connectedNodesWest}->{weights}->{ $westNode->{nodeid} } += $dW ;
        }
      }
    }

samples/test-nn-nonbool.pl view on Meta::CPAN

  [0.5,0] => [0.6],
  [0.5,0.5] => [0.8],
  [0.5,1] => [1],
  );

  my $set_err = $nn->get_set_error(\@set) ;
  
  print "SET ERROR NOW: $set_err\n" ; 

  while ( $set_err > $nn->{ERROR_OK} ) {
    $nn->learn_set( \@set , undef , undef , 1) ;
    $set_err = $nn->get_set_error(\@set) ;
  }
  
  $nn->save ;
  
  print "-------------------------------------------\n" ;
  
  print "ERR_OK: $nn->{ERROR_OK}\n" ;
  
  print "-------------------------------------------\n" ;

samples/test-nn-xor.pl view on Meta::CPAN

  [0,1] => [1],
  [1,0] => [1],
  [1,1] => [0],
  );

  my $set_err = $nn->get_set_error(\@set) ;
  
  print "SET ERROR NOW: $set_err\n" ; 

  while ( $set_err > $nn->{ERROR_OK} ) {
    $nn->learn_set( \@set , undef , undef , 1) ;
    $set_err = $nn->get_set_error(\@set) ;
  }
  
  $nn->save ;
  
  print "-------------------------------------------\n" ;
  
  print "ERR_OK: $nn->{ERROR_OK}\n" ;
  
  print "-------------------------------------------\n" ;

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