AI-Perceptron

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Makefile.PL  view on Meta::CPAN

    unless (eval "use Module::Build::Compat 0.02; 1" ) {
      print "This module requires Module::Build to install itself.\n";
      
      require ExtUtils::MakeMaker;
      my $yn = ExtUtils::MakeMaker::prompt
	('  Install Module::Build now from CPAN?', 'y');
      
      unless ($yn =~ /^y/i) {
	warn " *** Cannot install without Module::Build.  Exiting ...\n";
	exit 1;
      }

README  view on Meta::CPAN

     my $p = AI::Perceptron->new
               ->num_inputs( 2 )
               ->learning_rate( 0.04 )
               ->threshold( 0.02 )
               ->weights([ 0.1, 0.2 ]);

     my @inputs  = ( 1.3, -0.45 );   # input can be any number
     my $target  = 1;                # output is always -1 or 1
     my $current = $p->compute_output( @inputs );

     print "current output: $current, target: $target\n";

     $p->add_examples( [ $target, @inputs ] );

     $p->max_iterations( 10 )->train or
       warn "couldn't train in 10 iterations!";

     print "training until it gets it right\n";
     $p->max_iterations( -1 )->train; # watch out for infinite loops

DESCRIPTION
    This module is meant to show how a single node of a neural network
    works.

    Training is done by the *Stochastic Approximation of the
    Gradient-Descent* model.

MODEL

examples/and.pl  view on Meta::CPAN

#
# And - and function using a perceptron
# Steve Purkis <spurkis@epn.nu>
# July 20, 1999
##


use Data::Dumper;
use AI::Perceptron;

print( "Example: training a perceptron to recognize an 'AND' function.\n",
       "usage: $0 [<threshold> <weight1> <weight2>]\n" );

my $p = AI::Perceptron->new
                      ->num_inputs( 2 )
                      ->learning_rate( 0.1 );
if (@ARGV) {
    $p->threshold( shift(@ARGV) )
      ->weights([ shift(@ARGV), shift(@ARGV) ]);
}

my @training_exs = (
		    [-1 => -1, -1],
		    [-1 =>  1, -1],
		    [-1 => -1,  1],
		    [ 1 =>  1,  1],
		   );

print "\nBefore Training\n";
dump_perceptron( $p );

print "\nTraining...\n";
$p->train( @training_exs );

print "\nAfter Training\n";
dump_perceptron( $p );

sub dump_perceptron {
    my $p = shift;
    print "\tThreshold: ", $p->threshold, " Weights: ", join(', ', @{ $p->weights }), "\n";
    foreach my $inputs (@training_exs) {
	my $target = $inputs->[0];
	print "\tInputs = {", join(',', @$inputs[1..2]), "}, target=$target, output=", $p->compute_output( @$inputs[1..2] ), "\n";
    }
}

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

 my $p = AI::Perceptron->new
           ->num_inputs( 2 )
           ->learning_rate( 0.04 )
           ->threshold( 0.02 )
           ->weights([ 0.1, 0.2 ]);

 my @inputs  = ( 1.3, -0.45 );   # input can be any number
 my $target  = 1;                # output is always -1 or 1
 my $current = $p->compute_output( @inputs );

 print "current output: $current, target: $target\n";

 $p->add_examples( [ $target, @inputs ] );

 $p->max_iterations( 10 )->train or
   warn "couldn't train in 10 iterations!";

 print "training until it gets it right\n";
 $p->max_iterations( -1 )->train; # watch out for infinite loops

=cut

package AI::Perceptron;

use strict;
use accessors qw( num_inputs learning_rate _weights threshold
		  training_examples max_iterations );