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						($value<$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
	# array refs of exact same length (in elements).
	# Now calculates actual difference in numerical value.
	sub pdiff {
		no strict 'refs';
		shift if(substr($_[0],0,4) eq 'AI::'); 
		my $a1	=	shift;
		my $a2	=	shift;
		my $a1s	=	$#{$a1}; #AI::NeuralNet::BackProp::_FETCHSIZE($a1);
		my $a2s	=	$#{$a2}; #AI::NeuralNet::BackProp::_FETCHSIZE($a2);
		my ($a,$b,$diff,$t);
		$diff=0;
		#return undef if($a1s ne $a2s);	# must be same length
		for my $x (0..$a1s) {
			$a = $a1->[$x];
			$b = $a2->[$x];
			if($a!=$b) {
				if($a<$b){$t=$a;$a=$b;$b=$t;}
				$a=1 if(!$a);
				$diff+=(($a-$b)/$a)*100;
			}