org.encog.ml.bayesian.training

Class TrainBayesian

java.lang.Object

org.encog.ml.train.BasicTraining

org.encog.ml.bayesian.training.TrainBayesian

All Implemented Interfaces:

MLTrain

public class TrainBayesian
extends BasicTraining

Train a Bayesian network.

Constructor Summary

Constructors

Constructor and Description
TrainBayesian(BayesianNetwork theNetwork, MLDataSet theData, int theMaximumParents) Construct a Bayesian trainer.
TrainBayesian(BayesianNetwork theNetwork, MLDataSet theData, int theMaximumParents, BayesianInit theInit, BayesSearch theSearch, BayesEstimator theEstimator) Construct a Bayesian trainer.

Method Summary

Modifier and Type	Method and Description
boolean	canContinue()
BayesianInit	getInitNetwork()
int	getMaximumParents()
MLMethod	getMethod() Get the current best machine learning method from the training.
BayesianNetwork	getNetwork()
BayesSearch	getSearch()
boolean	isTrainingDone()
void	iteration() Perform one iteration of training.
TrainingContinuation	pause() Pause the training to continue later.
void	resume(TrainingContinuation state) Resume training.
void	setInitNetwork(BayesianInit initNetwork) Set the network init method.

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

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

Constructor Detail

TrainBayesian

public TrainBayesian(BayesianNetwork theNetwork,
                     MLDataSet theData,
                     int theMaximumParents)

Construct a Bayesian trainer. Use K2 to search, and the SimpleEstimator to estimate probability. Init as Naive Bayes

Parameters:

theNetwork - The network to train.

theData - The data to train.

theMaximumParents - The max number of parents.

TrainBayesian

public TrainBayesian(BayesianNetwork theNetwork,
                     MLDataSet theData,
                     int theMaximumParents,
                     BayesianInit theInit,
                     BayesSearch theSearch,
                     BayesEstimator theEstimator)

Construct a Bayesian trainer.

Parameters:

theNetwork - The network to train.

theData - The data to train with.

theMaximumParents - The maximum number of parents.

theInit - How to init the new Bayes network.

theSearch - The search method.

theEstimator - The estimation mehod.

Method Detail

isTrainingDone

public boolean isTrainingDone()

Specified by:

isTrainingDone in interface MLTrain

Overrides:

isTrainingDone in class BasicTraining

Returns:

True if training can progress no further.

iteration

public void iteration()

Perform one iteration of training.

canContinue

public boolean canContinue()

Returns:

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

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.

getMethod

public MLMethod getMethod()

Get the current best machine learning method from the training.

Returns:

The best machine learning method.

getNetwork

public BayesianNetwork getNetwork()

Returns:

the network

getMaximumParents

public int getMaximumParents()

Returns:

the maximumParents

getSearch

public BayesSearch getSearch()

Returns:

The search method.

getInitNetwork

public BayesianInit getInitNetwork()

Returns:

The init method.

setInitNetwork

public void setInitNetwork(BayesianInit initNetwork)

Set the network init method.

Parameters:

initNetwork - The init method.