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#!/usr/bin/perl	

# $Id: BackProp.pm,v 0.89 2000/08/12 01:05:27 josiah Exp $
#
# Copyright (c) 2000  Josiah Bryan  USA
#
# See AUTHOR section in pod text below for usage and distribution rights.   
# See UPDATES section in pod text below for info on what has changed in this release.
#

BEGIN {
	$AI::NeuralNet::BackProp::VERSION = "0.89";
}

#
# name:   AI::NeuralNet::BackProp
#
# author: Josiah Bryan 
# date:   Tuesday August 15 2000
# desc:   A simple back-propagation, feed-foward neural network with
#		  learning implemented via a generalization of Dobbs rule and
#		  several principals of Hoppfield networks. 
# online: http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl
#

package AI::NeuralNet::BackProp::neuron;
	
	use strict;
	
	# Dummy constructor
    sub new {
    	bless {}, shift
	}	
	
	# Rounds floats to ints
	sub intr  {
    	shift if(substr($_[0],0,4) eq 'AI::');
      	try   { return int(sprintf("%.0f",shift)) }
      	catch { return 0 }
	}
    
	
	# Receives input from other neurons. They must
	# be registered as a synapse of this neuron to effectively
	# input.
	sub input {
		my $self 	 =	shift;
		my $sid		 =	shift;
		my $value	 =	shift;
		
		# We simply weight the value sent by the neuron. The neuron identifies itself to us
		# using the code we gave it when it registered itself with us. The code is in $sid, 
		# (synapse ID) and we use that to track the weight of the connection.
		# This line simply multiplies the value by its weight and gets the integer from it.
		$self->{SYNAPSES}->{LIST}->[$sid]->{VALUE}	=	intr($value	*	$self->{SYNAPSES}->{LIST}->[$sid]->{WEIGHT});
		$self->{SYNAPSES}->{LIST}->[$sid]->{FIRED}	=	1;                                 
		$self->{SYNAPSES}->{LIST}->[$sid]->{INPUT}	=	$value;
		
		# Debugger
		AI::NeuralNet::BackProp::out1("\nRecieved input of $value, weighted to $self->{SYNAPSES}->{LIST}->[$sid]->{VALUE}, synapse weight is $self->{SYNAPSES}->{LIST}->[$sid]->{WEIGHT} (sid is $sid for $self).\n");
		AI::NeuralNet::BackProp::out1((($self->input_complete())?"All synapses have fired":"Not all synapses have fired"));
		AI::NeuralNet::BackProp::out1(" for $self.\n");
		
		# Check and see if all synapses have fired that are connected to this one.
		# If they have, then generate the output value for this synapse.
		$self->output() if($self->input_complete());
	}
	
	# Loops thru and outputs to every neuron that this
	# neuron is registered as synapse of.
	sub output {
		my $self	=	shift;
		my $size	=	$self->{OUTPUTS}->{SIZE} || 0;
		my $value	=	$self->get_output();
		for (0..$size-1) {
			AI::NeuralNet::BackProp::out1("Outputing to $self->{OUTPUTS}->{LIST}->[$_]->{PKG}, index $_, a value of $value with ID $self->{OUTPUTS}->{LIST}->[$_]->{ID}.\n");
			$self->{OUTPUTS}->{LIST}->[$_]->{PKG}->input($self->{OUTPUTS}->{LIST}->[$_]->{ID},$value);
		}

BackProp.pm  view on Meta::CPAN

=head1 OTHER INCLUDED PACKAGES

=item AI::NeuralNet::BackProp::neuron

AI::NeuralNet::BackProp::neuron is the worker package for AI::NeuralNet::BackProp.
It implements the actual neurons of the nerual network.
AI::NeuralNet::BackProp::neuron is not designed to be created directly, as
it is used internally by AI::NeuralNet::BackProp.

=item AI::NeuralNet::BackProp::_run

=item AI::NeuralNet::BackProp::_map

These two packages, _run and _map are used to insert data into
the network and used to get data from the network. The _run and _map packages 
are connected to the neurons so that the neurons think that the IO packages are
just another neuron, sending data on. But the IO packs. are special packages designed
with the same methods as neurons, just meant for specific IO purposes. You will
never need to call any of the IO packs. directly. Instead, they are called whenever
you use the run() or learn() methods of your network.
        



=head1 BUGS

This is an alpha release of C<AI::NeuralNet::BackProp>, and that holding true, I am sure 
there are probably bugs in here which I just have not found yet. If you find bugs in this module, I would 
appreciate it greatly if you could report them to me at F<E<lt>jdb@wcoil.comE<gt>>,
or, even better, try to patch them yourself and figure out why the bug is being buggy, and
send me the patched code, again at F<E<lt>jdb@wcoil.comE<gt>>. 



=head1 AUTHOR

Josiah Bryan F<E<lt>jdb@wcoil.comE<gt>>

Copyright (c) 2000 Josiah Bryan. All rights reserved. This program is free software; 
you can redistribute it and/or modify it under the same terms as Perl itself.

The C<AI::NeuralNet::BackProp> and related modules are free software. THEY COME WITHOUT WARRANTY OF ANY KIND.

                                                             




=head1 THANKS

Below is a list of people that have helped, made suggestions, patches, etc. No particular order.

		Tobias Bronx, tobiasb@odin.funcom.com
		Pat Trainor, ptrainor@title14.com
		Steve Purkis, spurkis@epn.nu
		Rodin Porrata, rodin@ursa.llnl.gov
		Daniel Macks dmacks@sas.upenn.edu

Tobias was a great help with the initial releases, and helped with learning options and a great
many helpful suggestions. Rodin has gave me some great ideas for the new internals, as well
as disabling Storable. Steve is the author of AI::Perceptron, and gave some good suggestions for 
weighting the neurons. Daniel was a great help with early beta testing of the module and related 
ideas. Pat has been a great help for running the module through the works. Pat is the author of 
the new Inter game, a in-depth strategy game. He is using a group of neural networks internally 
which provides a good test bed for coming up with new ideas for the network. Thankyou for all of
your help, everybody.


=head1 DOWNLOAD

You can always download the latest copy of AI::NeuralNet::BackProp
from http://www.josiah.countystart.com/modules/AI/cgi-bin/rec.pl


=head1 MAILING LIST

A mailing list has been setup for AI::NeuralNet::BackProp for discussion of AI and 
neural net related topics as they pertain to AI::NeuralNet::BackProp. I will also 
announce in the group each time a new release of AI::NeuralNet::BackProp is available.

The list address is at: ai-neuralnet-backprop@egroups.com 

To subscribe, send a blank email to: ai-neuralnet-backprop-subscribe@egroups.com  


=cut