C# Class Encog.Neural.Networks.Training.NEAT.NEATNeuronGene

Implements a NEAT neuron gene. NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm for the generation of evolving artificial neural networks. It was developed by Ken Stanley while at The University of Texas at Austin. http://www.cs.ucf.edu/~kstanley/

Inheritance: Encog.Solve.Genetic.Genes.BasicGene

ファイルを表示 Open project: encog/encog-silverlight-core Class Usage Examples

Public Methods

Method	Description
Copy ( IGene gene ) : void	Copy another gene to this one.
NEATNeuronGene ( NEATNeuronType type, long id, double splitY, double splitX ) : System	Construct a gene.
NEATNeuronGene ( NEATNeuronType type, long id, double splitY, double splitX, bool recurrent, double act ) : System	Construct a neuron gene.

Method Details

Copy() public method

Copy another gene to this one.

public Copy ( IGene gene ) : void
gene	IGene	The other gene.
return	void

NEATNeuronGene() public method

Construct a gene.

public NEATNeuronGene ( NEATNeuronType type, long id, double splitY, double splitX ) : System
type	NEATNeuronType	The type of neuron.
id	long	The id of this gene.
splitY	double	The split y.
splitX	double	The split x.
return	System

NEATNeuronGene() public method

Construct a neuron gene.

public NEATNeuronGene ( NEATNeuronType type, long id, double splitY, double splitX, bool recurrent, double act ) : System
type	NEATNeuronType	The type of neuron.
id	long	The id of this gene.
splitY	double	The split y.
splitX	double	The split x.
recurrent	bool	True if this is a recurrent link.
act	double	The activation response.
return	System