gov.sandia.cognition.learning.algorithm.ensemble

Class BinaryBaggingLearner<InputType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner<InputType,OutputType,EnsembleType>

gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner<InputType,java.lang.Boolean,Evaluator<? super InputType,? extends java.lang.Boolean>,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>

gov.sandia.cognition.learning.algorithm.ensemble.BinaryBaggingLearner<InputType>

Type Parameters:

InputType - Input class of the Collection<InputOutputPairs> for the dataset, for example, something like Vector or String

All Implemented Interfaces:

AnytimeAlgorithm<WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>, IterativeAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>, BatchLearnerContainer<BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>>>, SupervisedBatchLearner<InputType,java.lang.Boolean,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-07-23",
            changesNeeded=false,
            comments={"Cleaned up javadoc a little bit with code annotations.","Otherwise, looks fine."})
public class BinaryBaggingLearner<InputType>
extends AbstractBaggingLearner<InputType,java.lang.Boolean,Evaluator<? super InputType,? extends java.lang.Boolean>,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>

The BinaryBaggingLearner implements the Bagging learning algorithm. At each step, the algorithm creates a "bag" of data by sampling from the given data with replacement. It then passes the bag of data to the given learner to learn a new binary categorizer, which it then adds to the ensemble. All learners are given an equal weight of 1.0.

Since:

2.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner

bag, dataInBag, dataList, DEFAULT_MAX_ITERATIONS, DEFAULT_PERCENT_TO_SAMPLE, ensemble, learner, percentToSample, random

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

Fields inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

maxIterations

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
BinaryBaggingLearner() Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner) Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner, int maxIterations) Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner, int maxIterations, double percentToSample, java.util.Random random) Creates a new instance of BinaryBaggingLearner.
BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner, int maxIterations, java.util.Random random) Creates a new instance of BinaryBaggingLearner.

Method Summary

Modifier and Type	Method and Description
protected void	addEnsembleMember(Evaluator<? super InputType,? extends java.lang.Boolean> member) Adds a new member to the ensemble.
protected WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>	createInitialEnsemble() Create the initial, empty ensemble for the algorithm to use.

Methods inherited from class gov.sandia.cognition.learning.algorithm.ensemble.AbstractBaggingLearner

cleanupAlgorithm, fillBag, getBag, getDataInBag, getDataList, getEnsemble, getLearner, getPercentToSample, getRandom, getResult, initializeAlgorithm, setBag, setDataInBag, setDataList, setEnsemble, setLearner, setPercentToSample, setRandom, step

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

clone, getData, getKeepGoing, learn, setData, setKeepGoing, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm

getMaxIterations, isResultValid, setMaxIterations

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.learning.algorithm.BatchLearner

learn

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid

Constructor Detail

BinaryBaggingLearner

public BinaryBaggingLearner()

Creates a new instance of BinaryBaggingLearner.

BinaryBaggingLearner

public BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner)

Creates a new instance of BinaryBaggingLearner.

Parameters:

learner - The learner to use to create the categorizer on each iteration.

BinaryBaggingLearner

public BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner,
                            int maxIterations)

Creates a new instance of BinaryBaggingLearner.

Parameters:

learner - The learner to use to create the categorizer on each iteration.

maxIterations - The maximum number of iterations to run for, which is also the number of learners to create.

BinaryBaggingLearner

public BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner,
                            int maxIterations,
                            java.util.Random random)

Creates a new instance of BinaryBaggingLearner.

Parameters:

learner - The learner to use to create the categorizer on each iteration.

maxIterations - The maximum number of iterations to run for, which is also the number of learners to create.

random - The random number generator to use.

BinaryBaggingLearner

public BinaryBaggingLearner(BatchLearner<? super java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,? extends Evaluator<? super InputType,? extends java.lang.Boolean>> learner,
                            int maxIterations,
                            double percentToSample,
                            java.util.Random random)

Creates a new instance of BinaryBaggingLearner.

Parameters:

learner - The learner to use to create the ensemble member on each iteration.

maxIterations - The maximum number of iterations to run for, which is also the number of learners to create.

percentToSample - The percentage of the total size of the data to sample on each iteration. Must be positive.

random - The random number generator to use.

Method Detail

createInitialEnsemble

protected WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>> createInitialEnsemble()

Description copied from class: AbstractBaggingLearner

Create the initial, empty ensemble for the algorithm to use.

Specified by:

createInitialEnsemble in class AbstractBaggingLearner<InputType,java.lang.Boolean,Evaluator<? super InputType,? extends java.lang.Boolean>,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>

Returns:

A new ensemble for the algorithm to use.

addEnsembleMember

protected void addEnsembleMember(Evaluator<? super InputType,? extends java.lang.Boolean> member)

Description copied from class: AbstractBaggingLearner

Adds a new member to the ensemble.

Specified by:

addEnsembleMember in class AbstractBaggingLearner<InputType,java.lang.Boolean,Evaluator<? super InputType,? extends java.lang.Boolean>,WeightedBinaryEnsemble<InputType,Evaluator<? super InputType,? extends java.lang.Boolean>>>

Parameters:

member - The new member to add to the ensemble.