AI-NeuralNet-BackProp
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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);
}
}
# Used internally by output().
sub get_output {
my $self = shift;
my $size = $self->{SYNAPSES}->{SIZE} || 0;
my $value = 0;
my $state = 0;
my (@map,@weight);
# We loop through all the syanpses connected to this one and add the weighted
# valyes together, saving in a debugging list.
for (0..$size-1) {
$value += $self->{SYNAPSES}->{LIST}->[$_]->{VALUE};
$self->{SYNAPSES}->{LIST}->[$_]->{FIRED} = 0;
$map[$_]=$self->{SYNAPSES}->{LIST}->[$_]->{VALUE};
$weight[$_]=$self->{SYNAPSES}->{LIST}->[$_]->{WEIGHT};
}
# Debugger
AI::NeuralNet::BackProp::join_cols(\@map,5) if(($AI::NeuralNet::BackProp::DEBUG eq 3) || ($AI::NeuralNet::BackProp::DEBUG eq 2));
AI::NeuralNet::BackProp::out2("Weights: ".join(" ",@weight)."\n");
# Simply average the values and get the integer of the average.
$state = intr($value/$size);
# Debugger
AI::NeuralNet::BackProp::out1("From get_output, value is $value, so state is $state.\n");
# Possible future exapnsion for self excitation. Not currently used.
$self->{LAST_VALUE} = $value;
# Just return the $state
return $state;
}
# Used by input() to check if all registered synapses have fired.
sub input_complete {
my $self = shift;
my $size = $self->{SYNAPSES}->{SIZE} || 0;
my $retvalue = 1;
# Very simple loop. Doesn't need explaning.
for (0..$size-1) {
$retvalue = 0 if(!$self->{SYNAPSES}->{LIST}->[$_]->{FIRED});
}
return $retvalue;
}
# Used to recursively adjust the weights of synapse input channeles
# to give a desired value. Designed to be called via
# AI::NeuralNet::BackProp::NeuralNetwork::learn().
sub weight {
my $self = shift;
my $ammount = shift;
my $what = shift;
my $size = $self->{SYNAPSES}->{SIZE} || 0;
my $value;
AI::NeuralNet::BackProp::out1("Weight: ammount is $ammount, what is $what with size at $size.\n");
# Now this sub is the main cog in the learning wheel. It is called recursively on
# each neuron that has been bad (given incorrect output.)
for my $i (0..$size-1) {
$value = $self->{SYNAPSES}->{LIST}->[$i]->{VALUE};
if(0) {
# Formula by Steve Purkis
# Converges very fast for low-value inputs. Has trouble converging on high-value
# inputs. Feel free to play and try to get to work for high values.
my $delta = $ammount * ($what - $value) * $self->{SYNAPSES}->{LIST}->[$i]->{INPUT};
$self->{SYNAPSES}->{LIST}->[$i]->{WEIGHT} += $delta;
$self->{SYNAPSES}->{LIST}->[$i]->{PKG}->weight($ammount,$what);
}
# This formula in use by default is original by me (Josiah Bryan) as far as I know.
# If it is equal, then don't adjust
#
### Disabled because this soemtimes causes
### infinte loops when learning with range limits enabled
#
#next if($value eq $what);
# Adjust increment by the weight of the synapse of
# this neuron & apply direction delta
my $delta =
$ammount *
($value<$what?1:-1) *
$self->{SYNAPSES}->{LIST}->[$i]->{WEIGHT};
#print "($value,$what) delta:$delta\n";
# Recursivly apply
$self->{SYNAPSES}->{LIST}->[$i]->{WEIGHT} += $delta;
$self->{SYNAPSES}->{LIST}->[$i]->{PKG}->weight($ammount,$what);
}
}
# Registers some neuron as a synapse of this neuron.
# This is called exclusively by connect(), except for
# in initalize_group() to connect the _map() package.
sub register_synapse {
my $self = shift;
my $synapse = shift;
my $sid = $self->{SYNAPSES}->{SIZE} || 0;
$self->{SYNAPSES}->{LIST}->[$sid]->{PKG} = $synapse;
$self->{SYNAPSES}->{LIST}->[$sid]->{WEIGHT} = 1.00 if(!$self->{SYNAPSES}->{LIST}->[$sid]->{WEIGHT});
$self->{SYNAPSES}->{LIST}->[$sid]->{FIRED} = 0;
AI::NeuralNet::BackProp::out1("$self: Registering sid $sid with weight $self->{SYNAPSES}->{LIST}->[$sid]->{WEIGHT}, package $self->{SYNAPSES}->{LIST}->[$sid]->{PKG}.\n");
$self->{SYNAPSES}->{SIZE} = ++$sid;
return ($sid-1);
}
# Called via AI::NeuralNet::BackProp::NeuralNetwork::initialize_group() to
# form the neuron grids.
# This just registers another synapes as a synapse to output to from this one, and
# then we ask that synapse to let us register as an input connection and we
# save the sid that the ouput synapse returns.
sub connect {
my $self = shift;
my $to = shift;
my $oid = $self->{OUTPUTS}->{SIZE} || 0;
AI::NeuralNet::BackProp::out1("Connecting $self to $to at $oid...\n");
$self->{OUTPUTS}->{LIST}->[$oid]->{PKG} = $to;
$self->{OUTPUTS}->{LIST}->[$oid]->{ID} = $to->register_synapse($self);
$self->{OUTPUTS}->{SIZE} = ++$oid;
return $self->{OUTPUTS}->{LIST}->[$oid]->{ID};
}
1;
package AI::NeuralNet::BackProp;
use Benchmark;
use strict;
# Returns the number of elements in an array ref, undef on error
sub _FETCHSIZE {
my $a=$_[0];
my ($b,$x);
return undef if(substr($a,0,5) ne "ARRAY");
foreach $b (@{$a}) { $x++ };
return $x;
}
# Debugging subs
$AI::NeuralNet::BackProp::DEBUG = 0;
sub whowasi { (caller(1))[3] . '()' }
sub debug { shift; $AI::NeuralNet::BackProp::DEBUG = shift || 0; }
sub out1 { print shift() if ($AI::NeuralNet::BackProp::DEBUG eq 1) }
sub out2 { print shift() if (($AI::NeuralNet::BackProp::DEBUG eq 1) || ($AI::NeuralNet::BackProp::DEBUG eq 2)) }
sub out3 { print shift() if ($AI::NeuralNet::BackProp::DEBUG) }
sub out4 { print shift() if ($AI::NeuralNet::BackProp::DEBUG eq 4) }
# Rounds a floating-point to an integer with int() and sprintf()
sub intr {
shift if(substr($_[0],0,4) eq 'AI::');
try { return int(sprintf("%.0f",shift)) }
catch { return 0 }
}
# Used to format array ref into columns
# Usage:
# join_cols(\@array,$row_length_in_elements,$high_state_character,$low_state_character);
# Can also be called as method of your neural net.
# If $high_state_character is null, prints actual numerical values of each element.
sub join_cols {
no strict 'refs';
shift if(substr($_[0],0,4) eq 'AI::');
my $map = shift;
my $break = shift;
my $a = shift;
my $b = shift;
my $x;
foreach my $el (@{$map}) {
my $str = ((int($el))?$a:$b);
$str=$el."\0" if(!$a);
print $str;
$x++;
if($x>$break-1) {
print "\n";
$x=0;
}
}
print "\n";
}
# Returns percentage difference between all elements of two
BackProp.pm view on Meta::CPAN
sub run {
my $self = shift;
my $map = shift;
my $x = 0;
$map = $self->{PARENT}->crunch($map) if($map == 0);
return undef if(substr($map,0,5) ne "ARRAY");
foreach my $el (@{$map}) {
# Catch ourself if we try to run more inputs than neurons
return $x if($x>$self->{PARENT}->{DIV}-1);
# Here we add a small ammount of randomness to the network.
# This is to keep the network from getting stuck on a 0 value internally.
$self->{PARENT}->{NET}->[$x]->input(0,$el+(rand()*$self->{ramdom}));
$x++;
};
# Incase we tried to run less inputs than neurons, run const 1 in extra neurons
if($x<$self->{PARENT}->{DIV}) {
for my $y ($x..$self->{PARENT}->{DIV}-1) {
$self->{PARENT}->{NET}->[$y]->input(0,1);
}
}
return $x;
}
1;
# Internal output class. Not to be used directly.
package AI::NeuralNet::BackProp::_map;
use strict;
# Dummy constructor.
sub new {
bless { PARENT => $_[1] }, $_[0]
}
# Compliance with neuron interface
sub weight {}
# Compliance with neuron interface
sub register_synapse {
my $self = shift;
my $sid = $self->{REGISTRATION} || 0;
$self->{REGISTRATION} = ++$sid;
$self->{RMAP}->{$sid-1} = $self->{PARENT}->{_tmp_synapse};
return $sid-1;
}
# This acts just like a regular neuron by receiving
# values from input synapes. Yet, unlike a regularr
# neuron, it doesnt weight the values, just stores
# them to be retrieved by a call to map().
sub input {
no strict 'refs';
my $self = shift;
my $sid = shift;
my $value = shift;
my $size = $self->{PARENT}->{DIV};
my $flag = 1;
$self->{OUTPUT}->[$sid]->{VALUE} = $self->{PARENT}->intr($value);
$self->{OUTPUT}->[$sid]->{FIRED} = 1;
AI::NeuralNet::BackProp::out1 "Received value $self->{OUTPUT}->[$sid]->{VALUE} and sid $sid, self $self.\n";
}
# Here we simply collect the value of every neuron connected to this
# one from the layer below us and return an array ref to the final map..
sub map {
my $self = shift;
my $size = $self->{PARENT}->{DIV};
my $out = $self->{PARENT}->{OUT};
my $divide = AI::NeuralNet::BackProp->intr($size/$out);
my @map = ();
my $value;
AI::NeuralNet::BackProp::out1 "Num output neurons: $out, Input neurons: $size, Division: $divide\n";
for(0..$out-1) {
$value=0;
for my $a (0..$divide-1) {
$value += $self->{OUTPUT}->[($_*$divide)+$a]->{VALUE};
AI::NeuralNet::BackProp::out1 "\$a is $a, index is ".(($_*$divide)+$a).", value is $self->{OUTPUT}->[($_*$divide)+$a]->{VALUE}\n";
}
$map[$_] = AI::NeuralNet::BackProp->intr($value/$divide);
AI::NeuralNet::BackProp::out1 "Map position $_ is $map[$_] in @{[\@map]} with self set to $self.\n";
$self->{OUTPUT}->[$_]->{FIRED} = 0;
}
my $ret=\@map;
return $self->{PARENT}->_range($ret);
}
1;
# load_pcx() wrapper package
package AI::NeuralNet::BackProp::PCX;
# Called by load_pcx in AI::NeuralNet::BackProp;
sub new {
my $type = shift;
my $self = {
parent => $_[0],
file => $_[1]
};
my (@a,@b)=load_pcx($_[1]);
$self->{image}=\@a;
$self->{palette}=\@b;
bless \%{$self}, $type;
}
# Returns a rectangular block defined by an array ref in the form of
# [$x1,$y1,$x2,$y2]
# Return value is an array ref
sub get_block {
my $self = shift;
my $ref = shift;
my ($x1,$y1,$x2,$y2) = @{$ref};
my @block = ();
my $count = 0;
for my $x ($x1..$x2-1) {
for my $y ($y1..$y2-1) {
$block[$count++] = $self->get($x,$y);
}
}
return \@block;
}
# Returns pixel at $x,$y
sub get {
my $self = shift;
my ($x,$y) = (shift,shift);
return $self->{image}->[$y*320+$x];
}
# Returns array of (r,g,b) value from palette index passed
sub rgb {
my $self = shift;
my $color = shift;
return ($self->{palette}->[$color]->{red},$self->{palette}->[$color]->{green},$self->{palette}->[$color]->{blue});
}
# Returns mean of (rgb) value of palette index passed
sub avg {
my $self = shift;
my $color = shift;
return $self->{parent}->intr(($self->{palette}->[$color]->{red}+$self->{palette}->[$color]->{green}+$self->{palette}->[$color]->{blue})/3);
}
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