C# 클래스 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/
상속: Encog.Solve.Genetic.Genes.BasicGene
파일 보기 프로젝트 열기: encog/encog-silverlight-core 1 사용 예제들

공개 메소드들

메소드 설명
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.

메소드 상세

Copy() 공개 메소드

Copy another gene to this one.
public Copy ( IGene gene ) : void
gene IGene The other gene.
리턴 void

NEATNeuronGene() 공개 메소드

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.
리턴 System

NEATNeuronGene() 공개 메소드

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.
리턴 System