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use strict;


###########################################################
#AI::NNFlex::Reinforce::learn
###########################################################
sub learn
{

	my $network = shift;

	my @layers = @{$network->{'layers'}};

	# no connections westwards from input, so no weights to adjust
	shift @layers;

	# reverse to start with the last layer first
	foreach my $layer (reverse @layers)
	{
		my @nodes = @{$layer->{'nodes'}};

		foreach my $node (@nodes)
		{
			my @westNodes = @{$node->{'connectedNodesWest'}->{'nodes'}};
			my @westWeights = @{$node->{'connectedNodesWest'}->{'weights'}};
			my $connectedNodeCounter=0;
			foreach my $westNode (@westNodes)
			{
				my $dW = $westNode->{'activation'} * $westWeights[$connectedNodeCounter] * $network->{'learning rate'};
				$node->{'connectedNodesWest'}->{'weights'}->[$connectedNodeCounter] += $dW;
			}
		}
	}
}



1;

=pod

=head1 NAME

AI::NNFlex::Reinforce - A very simple experimental NN module

=head1 SYNOPSIS

 use AI::NNFlex::Reinforce;

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

 $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