org.encog.neural.networks.training.lma
Class LevenbergMarquardtTraining

java.lang.Object
  org.encog.ml.train.BasicTraining
      org.encog.neural.networks.training.lma.LevenbergMarquardtTraining

All Implemented Interfaces:

MLTrain

public class LevenbergMarquardtTraining
extends BasicTraining

Trains a neural network using a Levenberg Marquardt algorithm (LMA). This training technique is based on the mathematical technique of the same name. http://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm The LMA training technique has some important limitations that you should be aware of, before using it. Only neural networks that have a single output neuron can be used with this training technique. The entire training set must be loaded into memory. Because of this an Indexable training set must be used. However, despite these limitations, the LMA training technique can be a very effective training method. References: - http://www-alg.ist.hokudai.ac.jp/~jan/alpha.pdf - http://www.inference.phy.cam.ac.uk/mackay/Bayes_FAQ.html ---------------------------------------------------------------- This implementation of the Levenberg Marquardt algorithm is based heavily on code published in an article by Cesar Roberto de Souza. The original article can be found here: http://crsouza.blogspot.com/2009/11/neural-network-learning-by-levenberg_18.html Portions of this class are under the following copyright/license. Copyright 2009 by Cesar Roberto de Souza, Released under the LGPL.

Field Summary

static double	LAMBDA_MAX The max amount for the LAMBDA.
static int	NUM_POINTS Number of points for finite difference.
static double	SCALE_LAMBDA The amount to scale the lambda by.

Constructor Summary

LevenbergMarquardtTraining(BasicNetwork network, MLDataSet training)
Construct the LMA object.

Method Summary

void	calculateHessian() Calculate the Hessian matrix.
boolean	canContinue()
MLMethod	getMethod() Get the current best machine learning method from the training.
void	iteration() Perform one iteration.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
static double	trace(double[][] m) Return the sum of the diagonal.
void	updateWeights() Update the weights.

Methods inherited from class org.encog.ml.train.BasicTraining

addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, isTrainingDone, iteration, postIteration, preIteration, setError, setIteration, setTraining

Methods inherited from class java.lang.Object

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

Field Detail

SCALE_LAMBDA

public static final double SCALE_LAMBDA

The amount to scale the lambda by.

See Also:

Constant Field Values

LAMBDA_MAX

public static final double LAMBDA_MAX

The max amount for the LAMBDA.

See Also:

Constant Field Values

NUM_POINTS

public static final int NUM_POINTS

Number of points for finite difference.

See Also:

Constant Field Values

Constructor Detail

LevenbergMarquardtTraining

public LevenbergMarquardtTraining(BasicNetwork network,
                                  MLDataSet training)

Construct the LMA object.

Parameters:

network - The network to train. Must have a single output neuron.

training - The training data to use. Must be indexable.

Method Detail

trace

public static double trace(double[][] m)

Return the sum of the diagonal.

Parameters:

m - The matrix to sum.

Returns:

The trace of the matrix.

calculateHessian

public void calculateHessian()

Calculate the Hessian matrix.

canContinue

public boolean canContinue()

Returns:

True if the training can be paused, and later continued.

getMethod

public MLMethod getMethod()

Description copied from interface: MLTrain

Get the current best machine learning method from the training.

Returns:

The trained network.

iteration

public void iteration()

Perform one iteration.

pause

public TrainingContinuation pause()

Pause the training to continue later.

Returns:

A training continuation object.

resume

public void resume(TrainingContinuation state)

Resume training.

Parameters:

state - The training continuation object to use to continue.

updateWeights

public void updateWeights()

Update the weights.