C# 클래스 Encog.Neural.NEAT.Training.NEATTraining

Implements NEAT genetic training. 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.ML.Genetic.GeneticAlgorithm, IMLTrain

파일 보기 프로젝트 열기: encog/encog-silverlight-core 1 사용 예제들

공개 메소드들

메소드	설명
AddNeuronID ( long nodeID, IList vec ) : void	Add the specified neuron id.
AddStrategy ( IStrategy strategy ) : void
AdjustCompatibilityThreshold ( ) : void	Adjust the compatibility threshold.
AdjustSpeciesScore ( ) : void	Adjust each species score.
Crossover ( NEATGenome mom, NEATGenome dad ) : NEATGenome	Perform a cross over.
FinishTraining ( ) : void
Iteration ( ) : void	Perform one training iteration.
Iteration ( int count ) : void
NEATTraining ( ICalculateScore calculateScore, IPopulation population ) : System	Construct neat training with an existing population.
NEATTraining ( ICalculateScore calculateScore, int inputCount, int outputCount, int populationSize ) : System	Construct a neat trainer with a new population. The new population is created from the specified parameters.
Pause ( ) : TrainingContinuation
ResetAndKill ( ) : void	Reset counts and kill genomes with worse scores.
Resume ( TrainingContinuation state ) : void
SortAndRecord ( ) : void	Sort the genomes.
SpeciateAndCalculateSpawnLevels ( ) : void	Determine the species.
TournamentSelection ( int numComparisons ) : NEATGenome	Select a gene using a tournament.

비공개 메소드들

메소드	설명
Init ( ) : void	Init the training.

메소드 상세

AddNeuronID() 공개 메소드

Add the specified neuron id.

public AddNeuronID ( long nodeID, IList vec ) : void
nodeID	long	The neuron to add.
vec	IList	The list to add to.
리턴	void

AddStrategy() 공개 메소드

public AddStrategy ( IStrategy strategy ) : void
strategy	IStrategy
리턴	void

AdjustCompatibilityThreshold() 공개 메소드

Adjust the compatibility threshold.

public AdjustCompatibilityThreshold ( ) : void
리턴	void

AdjustSpeciesScore() 공개 메소드

Adjust each species score.

public AdjustSpeciesScore ( ) : void
리턴	void

Crossover() 공개 메소드

Perform a cross over.

public Crossover ( NEATGenome mom, NEATGenome dad ) : NEATGenome
mom	NEATGenome	The mother genome.
dad	NEATGenome	The father genome.
리턴	NEATGenome

FinishTraining() 공개 메소드

public FinishTraining ( ) : void
리턴	void

Iteration() 공개 메소드

Perform one training iteration.

public Iteration ( ) : void
리턴	void

Iteration() 공개 메소드

public Iteration ( int count ) : void
count	int
리턴	void

NEATTraining() 공개 메소드

Construct neat training with an existing population.

public NEATTraining ( ICalculateScore calculateScore, IPopulation population ) : System
calculateScore	ICalculateScore	The score object to use.
population	IPopulation	The population to use.
리턴	System

NEATTraining() 공개 메소드

Construct a neat trainer with a new population. The new population is created from the specified parameters.

public NEATTraining ( ICalculateScore calculateScore, int inputCount, int outputCount, int populationSize ) : System
calculateScore	ICalculateScore	The score calculation object.
inputCount	int	The input neuron count.
outputCount	int	The output neuron count.
populationSize	int	The population size.
리턴	System

Pause() 공개 메소드

public Pause ( ) : TrainingContinuation
리턴	Encog.Neural.Networks.Training.Propagation.TrainingContinuation

ResetAndKill() 공개 메소드

Reset counts and kill genomes with worse scores.

public ResetAndKill ( ) : void
리턴	void

Resume() 공개 메소드

public Resume ( TrainingContinuation state ) : void
state	Encog.Neural.Networks.Training.Propagation.TrainingContinuation
리턴	void

SortAndRecord() 공개 메소드

Sort the genomes.

public SortAndRecord ( ) : void
리턴	void

SpeciateAndCalculateSpawnLevels() 공개 메소드

Determine the species.

public SpeciateAndCalculateSpawnLevels ( ) : void
리턴	void

TournamentSelection() 공개 메소드

Select a gene using a tournament.

public TournamentSelection ( int numComparisons ) : NEATGenome
numComparisons	int	The number of compares to do.
리턴	NEATGenome