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lib/AI/NNFlex/Reinforce.pm  view on Meta::CPAN

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

 $network->init(); 



 use AI::NNFlex::Dataset;

 my $dataset = AI::NNFlex::Dataset->new([
			[INPUTARRAY],[TARGETOUTPUT],
			[INPUTARRAY],[TARGETOUTPUT]]);

 my $sqrError = 10;

 for (1..100)

 {

	 $dataset->learn($network);

 }

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

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

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

 my $outputsRef = $dataset->run($network);

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

=head1 DESCRIPTION

Reinforce is a very simple NN module. It's mainly included in this distribution to provide an example of how to subclass AI::NNFlex to write your own NN modules. The training method strengthens any connections that are active during the run pass.

=head1 CONSTRUCTOR 

=head2 AI::NNFlex::Reinforce

 new ( parameter => value );
	
	randomweights=>MAXIMUM VALUE FOR INITIAL WEIGHT

	fixedweights=>WEIGHT TO USE FOR ALL CONNECTIONS

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

	learningrate=>the learning rate of the network

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


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. Subclasses may implement other methods.

=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);

=head3 init

 Syntax:

 $network->init();

Initialises connections between nodes, sets initial weights and loads external components. 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...

=head3 lesion

 $network->lesion ({'nodes'=>PROBABILITY,'connections'=>PROBABILITY})

 Damages the network.

B<PROBABILITY>

A value between 0 and 1, denoting the probability of a given node or connection being damaged.

Note: this method may be called on a per network, per node or per layer basis using the appropriate object.

=head2 AN::NNFlex::Dataset

=head3 learn

 $dataset->learn($network)

'Teaches' the network the dataset using the networks defined learning algorithm. Returns sqrError;

=head3 run

 $dataset->run($network)

Runs the dataset through the network and returns a reference to an array of output patterns.

=head1 EXAMPLES

See the code in ./examples. For any given version of NNFlex, xor.pl will contain the latest functionality.


=head1 PREREQs