AI-NeuralNet-BackProp

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BackProp.pm  view on Meta::CPAN

#		  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 }
	}
    
	

BackProp.pm  view on Meta::CPAN

	# See documentation above for usage.
	sub new {
    	no strict;
    	my $type	=	shift;
		my $self	=	{};
		my $layers	=	shift;
		my $size	=	shift;
		my $out		=	shift || $size;
		my $flag	=	shift || 0;
		
		bless $self, $type;
		
		# If $layers is a string, then it will be nummerically equal to 0, so try to load it
		# as a network file.
		if($layers == 0) {  
		    # We use a "1" flag as the second argument to indicate that we want load()
		    # to call the new constructor to make a network the same size as in the file
		    # and return a refrence to the network, instead of just creating the network from
		    # pre-exisiting refrence
			return $self->load($layers,1);
		}

BackProp.pm  view on Meta::CPAN

		
1;

# Internal input class. Not to be used directly.
package AI::NeuralNet::BackProp::_run;
	
	use strict;
	
	# Dummy constructor.
	sub new {
		bless { PARENT => $_[1] }, $_[0]
	}
	
	# This is so we comply with the neuron interface.
	sub weight {}
	sub input  {}
	
	# Again, compliance with neuron interface.
	sub register_synapse {
		my $self	=	shift;		
		my $sid		=	$self->{REGISTRATION} || 0;

BackProp.pm  view on Meta::CPAN

	
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;

BackProp.pm  view on Meta::CPAN

	# 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	=	();

BackProp.pm  view on Meta::CPAN

called with a 0 value, it will disable randomness in the network. See NOTES on learning 
a 0 value in the input map with randomness disabled.



=item $net->load_pcx($filename);

Oh heres a treat... this routine will load a PCX-format file (yah, I know ... ancient format ... but
it is the only one I could find specs for to write it in Perl. If anyone can get specs for
any other formats, or could write a loader for them, I would be very grateful!) Anyways, a PCX-format
file that is exactly 320x200 with 8 bits per pixel, with pure Perl. It returns a blessed refrence to 
a AI::NeuralNet::BackProp::PCX object, which supports the following routinges/members. See example 
files ex_pcxl.pl and ex_pcx.pl in the ./examples/ directory.



=item $pcx->{image}

This is an array refrence to the entire image. The array containes exactly 64000 elements, each
element contains a number corresponding into an index of the palette array, details below.

docs.htm  view on Meta::CPAN

This will set the randomness factor from the network. Default is 0.001. When called 
with no arguments, or an undef value, it will return current randomness value. When
called with a 0 value, it will disable randomness in the network. See NOTES on learning 
a 0 value in the input map with randomness disabled.
<P></P>
<DT><STRONG><A NAME="item_load_pcx">$net-&gt;load_pcx($filename);</A></STRONG><BR>
<DD>
Oh heres a treat... this routine will load a PCX-format file (yah, I know ... ancient format ... but
it is the only one I could find specs for to write it in Perl. If anyone can get specs for
any other formats, or could write a loader for them, I would be very grateful!) Anyways, a PCX-format
file that is exactly 320x200 with 8 bits per pixel, with pure Perl. It returns a blessed refrence to 
a AI::NeuralNet::BackProp::PCX object, which supports the following routinges/members. See example 
files ex_pcxl.pl and ex_pcx.pl in the ./examples/ directory.
<P></P>
<DT><STRONG><A NAME="item_%24pcx%2D%3E%7Bimage%7D">$pcx-&gt;{image}</A></STRONG><BR>
<DD>
This is an array refrence to the entire image. The array containes exactly 64000 elements, each
element contains a number corresponding into an index of the palette array, details below.
<P></P>
<DT><STRONG><A NAME="item_%24pcx%2D%3E%7Bpalette%7D">$pcx-&gt;{palette}</A></STRONG><BR>
<DD>