org.encog.ensemble

Class Ensemble

java.lang.Object

org.encog.ensemble.Ensemble

Direct Known Subclasses:

AdaBoost, Bagging, Dropout, Stacking

public abstract class Ensemble
extends java.lang.Object

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
class	Ensemble.NotPossibleInThisMethod
class	Ensemble.TrainingAborted

Field Summary

Fields

Modifier and Type	Field and Description
protected EnsembleAggregator	aggregator
protected MLDataSet	aggregatorDataSet
protected EnsembleDataSetFactory	dataSetFactory
protected java.util.ArrayList<EnsembleML>	members
protected EnsembleMLMethodFactory	mlFactory
protected EnsembleTrainFactory	trainFactory

Constructor Summary

Constructors

Constructor and Description
Ensemble()

Method Summary

Modifier and Type	Method and Description
void	addMember(EnsembleML newMember) Add a member to the ensemble
void	addNewMember()
MLData	compute(MLData input) Compute the output for a specific input
EnsembleML	generateNewMember()
EnsembleAggregator	getAggregator()
EnsembleML	getMember(int memberNumber) Extract a specific MLMethod
abstract EnsembleTypes.ProblemType	getProblemType() Return what type of problem this Ensemble is solving
MLDataSet	getTrainingSet(int setNumber) Extract a specific training set from the Ensemble
abstract void	initMembers() Initialise ensemble components
void	initMembersBySplits(int splits)
void	retrainAggregator()
void	setAggregator(EnsembleAggregator aggregator) Sets the ensemble aggregation method
void	setTrainingData(MLDataSet data) Set which training data to base the training on
void	setTrainingDataFactory(EnsembleDataSetFactory dataSetFactory) Set which dataSetFactory to use to create the correct tranining sets
void	setTrainingMethod(EnsembleTrainFactory newTrainFactory) Set the training method to use for this ensemble
void	train(double targetError, double selectionError, EnsembleDataSet testset) Train the ensemble to a target accuracy
void	train(double targetError, double selectionError, EnsembleDataSet selectionSet, boolean verbose)
void	train(double targetError, double selectionError, int maxIterations, EnsembleDataSet testset)
void	train(double targetError, double selectionError, int maxIterations, int maxLoops, EnsembleDataSet selectionSet, boolean verbose) Train the ensemble to a target accuracy
void	trainMember(EnsembleML current, double targetError, double selectionError, EnsembleDataSet selectionSet, boolean verbose)
void	trainMember(EnsembleML current, double targetError, double selectionError, int maxIterations, int maxLoops, EnsembleDataSet selectionSet, boolean verbose)
void	trainMember(int index, double targetError, double selectionError, EnsembleDataSet selectionSet, boolean verbose)
void	trainMember(int index, double targetError, double selectionError, int maxIterations, EnsembleDataSet selectionSet, boolean verbose)

Methods inherited from class java.lang.Object

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

Field Detail

dataSetFactory

protected EnsembleDataSetFactory dataSetFactory

trainFactory

protected EnsembleTrainFactory trainFactory

aggregator

protected EnsembleAggregator aggregator

members

protected java.util.ArrayList<EnsembleML> members

mlFactory

protected EnsembleMLMethodFactory mlFactory

aggregatorDataSet

protected MLDataSet aggregatorDataSet

Constructor Detail

Ensemble

public Ensemble()

Method Detail

initMembers

public abstract void initMembers()

Initialise ensemble components

generateNewMember

public EnsembleML generateNewMember()

addNewMember

public void addNewMember()

initMembersBySplits

public void initMembersBySplits(int splits)

setTrainingMethod

public void setTrainingMethod(EnsembleTrainFactory newTrainFactory)

Set the training method to use for this ensemble

Parameters:

newTrainFactory - The training factory.

setTrainingData

public void setTrainingData(MLDataSet data)

Set which training data to base the training on

Parameters:

data - The training data.

setTrainingDataFactory

public void setTrainingDataFactory(EnsembleDataSetFactory dataSetFactory)

Set which dataSetFactory to use to create the correct tranining sets

Parameters:

dataSetFactory - The data set factory.

trainMember

public void trainMember(int index,
                        double targetError,
                        double selectionError,
                        int maxIterations,
                        EnsembleDataSet selectionSet,
                        boolean verbose)
                 throws Ensemble.TrainingAborted

Throws:

Ensemble.TrainingAborted

trainMember

public void trainMember(EnsembleML current,
                        double targetError,
                        double selectionError,
                        int maxIterations,
                        int maxLoops,
                        EnsembleDataSet selectionSet,
                        boolean verbose)
                 throws Ensemble.TrainingAborted

Throws:

Ensemble.TrainingAborted

trainMember

public void trainMember(EnsembleML current,
                        double targetError,
                        double selectionError,
                        EnsembleDataSet selectionSet,
                        boolean verbose)
                 throws Ensemble.TrainingAborted

Throws:

Ensemble.TrainingAborted

trainMember

public void trainMember(int index,
                        double targetError,
                        double selectionError,
                        EnsembleDataSet selectionSet,
                        boolean verbose)
                 throws Ensemble.TrainingAborted

Throws:

Ensemble.TrainingAborted

retrainAggregator

public void retrainAggregator()

train

public void train(double targetError,
                  double selectionError,
                  int maxIterations,
                  int maxLoops,
                  EnsembleDataSet selectionSet,
                  boolean verbose)
           throws Ensemble.TrainingAborted

Train the ensemble to a target accuracy

Parameters:

targetError - The target error.

selectionError - The selection error.

maxIterations - Max iterations.

maxLoops - Max loops.

selectionSet - Selection set.

verbose - Verbose.

Throws:

Ensemble.TrainingAborted - Training was aborted.

train

public void train(double targetError,
                  double selectionError,
                  EnsembleDataSet selectionSet,
                  boolean verbose)
           throws Ensemble.TrainingAborted

Throws:

Ensemble.TrainingAborted

train

public void train(double targetError,
                  double selectionError,
                  EnsembleDataSet testset)
           throws Ensemble.TrainingAborted

Train the ensemble to a target accuracy

Parameters:

targetError - The target error.

selectionError - The selection error.

testset - The test set.

Throws:

Ensemble.TrainingAborted - Training aborted.

train

public void train(double targetError,
                  double selectionError,
                  int maxIterations,
                  EnsembleDataSet testset)
           throws Ensemble.TrainingAborted

Throws:

Ensemble.TrainingAborted

getTrainingSet

public MLDataSet getTrainingSet(int setNumber)

Extract a specific training set from the Ensemble

Parameters:

setNumber - The set number.

Returns:

The training set.

getMember

public EnsembleML getMember(int memberNumber)

Extract a specific MLMethod

Parameters:

memberNumber - The member number.

Returns:

The MLMethod.

addMember

public void addMember(EnsembleML newMember)
               throws Ensemble.NotPossibleInThisMethod

Add a member to the ensemble

Parameters:

newMember - The new member.

Throws:

Ensemble.NotPossibleInThisMethod - Not possible in this method.

compute

public MLData compute(MLData input)
               throws WeightedAveraging.WeightMismatchException

Compute the output for a specific input

Parameters:

input - The input.

Returns:

The data.

Throws:

WeightedAveraging.WeightMismatchException - Weight mismatch exception.

getAggregator

public EnsembleAggregator getAggregator()

Returns:

Returns the ensemble aggregation method

setAggregator

public void setAggregator(EnsembleAggregator aggregator)

Sets the ensemble aggregation method

Parameters:

aggregator - The aggregator.

getProblemType

public abstract EnsembleTypes.ProblemType getProblemType()

Return what type of problem this Ensemble is solving

Returns:

The problem type.