org.encog.ml.svm.training

Class SVMTrain

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.ml.svm.training.SVMTrain

All Implemented Interfaces:

MLTrain

public class SVMTrain
extends BasicTraining

Provides training for Support Vector Machine networks.

Constructor Summary

Constructors

Constructor and Description
SVMTrain(SVM method, MLDataSet dataSet) Construct a trainer for an SVM network.

Method Summary

Modifier and Type	Method and Description
boolean	canContinue()
double	getC()
int	getFold()
double	getGamma()
MLMethod	getMethod() Get the current best machine learning method from the training.
svm_problem	getProblem()
boolean	isTrainingDone()
void	iteration() Perform either a train or a cross validation.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
void	setC(double theC) Set the constant C.
void	setFold(int theFold) Set the number of folds.
void	setGamma(double theGamma) Set the gamma.

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

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

Methods inherited from class java.lang.Object

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

Constructor Detail

SVMTrain

public SVMTrain(SVM method,
                MLDataSet dataSet)

Construct a trainer for an SVM network.

Parameters:

method - The network to train.

dataSet - The training data for this network.

Method Detail

canContinue

public boolean canContinue()

Returns:

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

getC

public double getC()

Returns:

The constant C.

getFold

public int getFold()

Returns:

the fold

getGamma

public double getGamma()

Returns:

The gamma.

getMethod

public MLMethod getMethod()

Get the current best machine learning method from the training.

Returns:

The best machine learning method.

getProblem

public svm_problem getProblem()

Returns:

The problem being trained.

isTrainingDone

public boolean isTrainingDone()

Specified by:

isTrainingDone in interface MLTrain

Overrides:

isTrainingDone in class BasicTraining

Returns:

True if the training is done.

iteration

public void iteration()

Perform either a train or a cross validation. If the folds property is greater than 1 then cross validation will be done. Cross validation does not produce a usable model, but it does set the error. If you are cross validating try C and Gamma values until you have a good error rate. Then use those values to train, producing the final model.

pause

public final 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.

setC

public void setC(double theC)

Set the constant C.

Parameters:

theC - The constant C.

setFold

public void setFold(int theFold)

Set the number of folds.

Parameters:

theFold - the fold to set.

setGamma

public void setGamma(double theGamma)

Set the gamma.

Parameters:

theGamma - The new gamma.