AI-NNFlex

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examples/bp.pl  view on Meta::CPAN

my @weightsHO;


main();


#==============================================================
#********** THIS IS THE MAIN PROGRAM **************************
#==============================================================

sub main
 {

 # initiate the weights
  initWeights();

 # load in the data
  initData();

 # train the network
    for(my $j = 0;$j <= $numEpochs;$j++)

lib/AI/NNFlex.pm  view on Meta::CPAN

use AI::NNFlex::Mathlib;
use base qw(AI::NNFlex::Mathlib);





###############################################################################
# AI::NNFlex::new
###############################################################################
sub new
{
	my $class = shift;
	my $network={};
	bless $network,$class;

	# intercept the new style 'empty network' constructor call
	# Maybe I should deprecate the old one, but its convenient, provided you
	# can follow the mess of hashes
	
	if (!grep /HASH/,@_)

lib/AI/NNFlex/Backprop.pm  view on Meta::CPAN

###########################################################
#

package AI::NNFlex::Backprop;
use AI::NNFlex;
use AI::NNFlex::Feedforward;
use base qw(AI::NNFlex::Feedforward AI::NNFlex);
use strict;


sub calc_error
{
	my $network = shift;

	my $outputPatternRef = shift;
	my @outputPattern = @$outputPatternRef;

	my @debug = @{$network->{'debug'}};

	if (scalar @debug > 0)
	{$network->dbug ("Output pattern @outputPattern received by Backprop",4);}

lib/AI/NNFlex/Dataset.pm  view on Meta::CPAN

#
###########################################################
#
use strict;
package AI::NNFlex::Dataset;


###########################################################
# AI::NNFlex::Dataset::new
###########################################################
sub new
{
	my $class = shift;
	my $params = shift;
	my $dataset;
	if ($class =~ /HASH/)
	{
		$dataset = $class;
		$dataset->{'data'} = $params;
		return 1;
	}

lib/AI/NNFlex/Hopfield.pm  view on Meta::CPAN

####################################################
#
# The hopfield network has connections from every
# node to every other node, rather than being 
# arranged in distinct layers like a feedforward
# network. We can retain the layer architecture to
# give us blocks of nodes, but need to overload init
# to perform full connections
#
#####################################################
sub init
{

	my $network = shift;
	my @nodes;

	# Get a list of all the nodes in the network
	foreach my $layer (@{$network->{'layers'}})
	{
		foreach my $node (@{$layer->{'nodes'}})
		{

lib/AI/NNFlex/Mathlib.pm  view on Meta::CPAN

#
#######################################################
#Copyright (c) 2004-2005 Charles Colbourn. All rights reserved. This program is free software; you can redistribute it and/or modify

package AI::NNFlex::Mathlib;
use strict;

#######################################################
# tanh activation function
#######################################################
sub tanh
{

	my $network = shift;
	my $value = shift;

	my @debug = @{$network->{'debug'}};

	my $a = exp($value);
	my $b = exp(-$value);
   if ($value > 20){ $value=1;}

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