view release on metacpan or  search on metacpan

lib/AI/NNFlex.pm  view on Meta::CPAN

		my $nodeCounter=0;
		foreach my $connectedNode (@{$node->{'connectedNodesEast'}->{'nodes'}})
		{
			my $probability = rand(1);
			if ($probability < $connectionLesion)
			{
				my $reverseNodeCounter=0; # maybe should have done this differntly in init, but 2 late now!
				${$node->{'connectedNodesEast'}->{'nodes'}}[$nodeCounter] = undef;
				foreach my $reverseConnection (@{$connectedNode->{'connectedNodesWest'}->{'nodes'}})
				{
					if ($reverseConnection == $node)
					{
						${$connectedNode->{'connectedNodesEast'}->{'nodes'}}[$reverseNodeCounter] = undef;
					}
					$reverseNodeCounter++;
				}

			}
                                
			$nodeCounter++;
		}


        }
        

        return 1;
}

1;

=pod

=head1 NAME

AI::NNFlex - A base class for implementing neural networks

=head1 SYNOPSIS

 use AI::NNFlex;

 my $network = AI::NNFlex->new(config parameter=>value);

 $network->add_layer(	nodes=>x,
 			activationfunction=>'function');

 $network->init(); 

 $network->lesion(	nodes=>PROBABILITY,
			connections=>PROBABILITY);

 $network->dump_state (filename=>'badgers.wts');

 $network->load_state (filename=>'badgers.wts');

 my $outputsRef = $network->output(layer=>2,round=>1);


=head1 DESCRIPTION

AI::NNFlex is a base class for constructing your own neural network modules. To implement a neural network, start with the documentation for AI::NNFlex::Backprop, included in this distribution

=head1 CONSTRUCTOR 

=head2 AI::NNFlex->new ( parameter => value );
	

randomweights=>MAXIMUM VALUE FOR INITIAL WEIGHT

fixedweights=>WEIGHT TO USE FOR ALL CONNECTIONS

debug=>[LIST OF CODES FOR MODULES TO DEBUG]

round=>0 or 1, a true value sets the network to round output values to nearest of 1, -1 or 0


The constructor implements a fairly generalised network object with a number of parameters.


The following parameters are optional:
 randomweights
 fixedweights
 debug
 round


(Note, if randomweights is not specified the network will default to a random value from 0 to 1.

=head1 METHODS

This is a short list of the main methods implemented in AI::NNFlex. 

=head2 AI::NNFlex

=head3 add_layer

 Syntax:

 $network->add_layer(	nodes=>NUMBER OF NODES IN LAYER,
			persistentactivation=>RETAIN ACTIVATION BETWEEN PASSES,
			decay=>RATE OF ACTIVATION DECAY PER PASS,
			randomactivation=>MAXIMUM STARTING ACTIVATION,
			threshold=>NYI,
			activationfunction=>"ACTIVATION FUNCTION",
			randomweights=>MAX VALUE OF STARTING WEIGHTS);

Add layer adds whatever parameters you specify as attributes of the layer, so if you want to implement additional parameters simply use them in your calling code.

Add layer returns success or failure, and if successful adds a layer object to the $network->{'layers'} array. This layer object contains an attribute $layer->{'nodes'}, which is an array of nodes in the layer.

=head3 init

 Syntax:

 $network->init();

Initialises connections between nodes, sets initial weights. The base AI::NNFlex init method implementes connections backwards and forwards from each node in each layer to each node in the preceeding and following layers. 

init adds the following attributes to each node:

=over