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	foreach (keys %{$params})
	{
		$$node{$_} = $$params{$_}
	}	

	if ($$params{'randomactivation'})
	{
		$$node{'activation'} = 
			rand($$params{'random'});
			AI::NNFlex::dbug($params,"Randomly activated at ".$$node{'activation'},2);
	}
	else
	{
		$$node{'activation'} = 0;
	}
	$$node{'active'} = 1;
	
	$$node{'error'} = 0;
	
	bless $node,$class;	
	AI::NNFlex::dbug($params,"Created node $node",2);
	return $node;
}

##############################################################################
# sub lesion
##############################################################################
sub lesion
{

        my $node = shift;

        my %params =  @_;


        my $nodeLesion = $params{'nodes'};
        my $connectionLesion = $params{'connections'};

        # go through the layers & node inactivating random nodes according
        # to probability
        
	if ($nodeLesion)
	{
		my $probability = rand(1);
		if ($probability < $nodeLesion)
		{
			$node->{'active'} = 0;
		}
	}

	if ($connectionLesion)
	{
		# init works from west to east, so we should here too
		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]