C# 클래스 Encog.Neural.Networks.Training.Strategy.HybridStrategy

A hybrid stragey allows a secondary training algorithm to be used. Once the primary algorithm is no longer improving by much, the secondary will be used. Using simulated annealing in as a secondary to one of the propagation methods is often a very efficient combination as it can help the propagation method escape a local minimum. This is particularly true with backpropagation.
상속: IStrategy
파일 보기 프로젝트 열기: encog/encog-silverlight-core

공개 메소드들

메소드 설명
HybridStrategy ( ITrain altTrain ) : System

Construct a hybrid strategy with the default minimum improvement and toleration cycles.
HybridStrategy ( ITrain altTrain, double minImprovement, int tolerateMinImprovement, int alternateCycles ) : System

Create a hybrid strategy.
Init ( ITrain train ) : void

Initialize this strategy.
PostIteration ( ) : void

Called just after a training iteration.
PreIteration ( ) : void

Called just before a training iteration.

메소드 상세

HybridStrategy() 공개 메소드

Construct a hybrid strategy with the default minimum improvement and toleration cycles.
public HybridStrategy ( ITrain altTrain ) : System
altTrain ITrain The alternative training strategy.
리턴 System

HybridStrategy() 공개 메소드

Create a hybrid strategy.
public HybridStrategy ( ITrain altTrain, double minImprovement, int tolerateMinImprovement, int alternateCycles ) : System
altTrain ITrain The alternate training algorithm.
minImprovement double The minimum improvement to switch algorithms.
tolerateMinImprovement int The number of cycles to tolerate the /// minimum improvement for.
alternateCycles int How many cycles should the alternate /// training algorithm be used for.
리턴 System

Init() 공개 메소드

Initialize this strategy.
public Init ( ITrain train ) : void
train ITrain The training algorithm.
리턴 void

PostIteration() 공개 메소드

Called just after a training iteration.
public PostIteration ( ) : void
리턴 void

PreIteration() 공개 메소드

Called just before a training iteration.
public PreIteration ( ) : void
리턴 void