org.encog.ml.train.strategy
Class HybridStrategy

java.lang.Object
  org.encog.ml.train.strategy.HybridStrategy

All Implemented Interfaces:

Strategy

public class HybridStrategy
extends Object
implements Strategy

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.

Author:

jheaton

Field Summary

static int	DEFAULT_ALTERNATE_CYCLES The default number of cycles to use the alternate training for.
static double	DEFAULT_MIN_IMPROVEMENT The default minimum improvement before we switch to the alternate training method.
static int	DEFAULT_TOLERATE_CYCLES The default number of cycles to tolerate bad improvement for.

Constructor Summary

HybridStrategy(MLTrain altTrain)
Construct a hybrid strategy with the default minimum improvement and toleration cycles.

HybridStrategy(MLTrain altTrain, double minImprovement, int tolerateMinImprovement, int alternateCycles)
Create a hybrid strategy.

Method Summary

void	init(MLTrain train) Initialize this strategy.
void	postIteration() Called just after a training iteration.
void	preIteration() Called just before a training iteration.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

DEFAULT_MIN_IMPROVEMENT

public static final double DEFAULT_MIN_IMPROVEMENT

The default minimum improvement before we switch to the alternate training method.

See Also:

Constant Field Values

DEFAULT_TOLERATE_CYCLES

public static final int DEFAULT_TOLERATE_CYCLES

The default number of cycles to tolerate bad improvement for.

See Also:

Constant Field Values

DEFAULT_ALTERNATE_CYCLES

public static final int DEFAULT_ALTERNATE_CYCLES

The default number of cycles to use the alternate training for.

See Also:

Constant Field Values

Constructor Detail

HybridStrategy

public HybridStrategy(MLTrain altTrain)

Construct a hybrid strategy with the default minimum improvement and toleration cycles.

Parameters:

altTrain - The alternative training strategy.

HybridStrategy

public HybridStrategy(MLTrain altTrain,
                      double minImprovement,
                      int tolerateMinImprovement,
                      int alternateCycles)

Create a hybrid strategy.

Parameters:

altTrain - The alternate training algorithm.

minImprovement - The minimum improvement to switch algorithms.

tolerateMinImprovement - The number of cycles to tolerate the minimum improvement for.

alternateCycles - How many cycles should the alternate training algorithm be used for.

Method Detail

init

public void init(MLTrain train)

Initialize this strategy.

Specified by:

init in interface Strategy

Parameters:

train - The training algorithm.

postIteration

public void postIteration()

Called just after a training iteration.

Specified by:

postIteration in interface Strategy

preIteration

public void preIteration()

Called just before a training iteration.

Specified by:

preIteration in interface Strategy