gov.sandia.cognition.statistics.distribution

Class MixtureOfGaussians.EMLearner

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 Vector>,MixtureOfGaussians.PDF>

gov.sandia.cognition.statistics.distribution.MixtureOfGaussians.EMLearner

All Implemented Interfaces:

AnytimeAlgorithm<MixtureOfGaussians.PDF>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends Vector>,MixtureOfGaussians.PDF>, BatchLearner<java.util.Collection<? extends Vector>,MixtureOfGaussians.PDF>, DistributionEstimator<Vector,MixtureOfGaussians.PDF>, CloneableSerializable, Randomized, java.io.Serializable, java.lang.Cloneable

Enclosing class:

MixtureOfGaussians

@PublicationReference(author="Jaakkola",
                      title="Estimating mixtures: the EM-algorithm",
                      type=Misc,
                      year=2007,
                      url="http://courses.csail.mit.edu/6.867/lectures/notes-em2.pdf")
public static class MixtureOfGaussians.EMLearner
extends AbstractAnytimeBatchLearner<java.util.Collection<? extends Vector>,MixtureOfGaussians.PDF>
implements Randomized, DistributionEstimator<Vector,MixtureOfGaussians.PDF>, MeasurablePerformanceAlgorithm

An Expectation-Maximization based "soft" assignment learner.

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_MAX_ITERATIONS Default max iterations, 100.
static double	DEFAULT_TOLERANCE Default tolerance, 1.0E-5.
static java.lang.String	PERFORMANCE_NAME Name of the performance measurement, "Assignment Change".
protected java.util.Random	random Random number generator.

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
EMLearner(int distributionCount, MultivariateGaussian.WeightedMaximumLikelihoodEstimator learner, java.util.Random random) Creates a new instance of EMLearner
EMLearner(int distributionCount, java.util.Random random) Creates a new instance of EMLearner
EMLearner(java.util.Random random) Creates a new instance of EMLearner

Method Summary

Modifier and Type	Method and Description
protected void	cleanupAlgorithm() Called to clean up the learning algorithm's state after learning has finished.
double	getAssignmentChanged() Gets the total assignment change from the last completed step of the algorithm.
NamedValue<java.lang.Double>	getPerformance() Gets the name-value pair that describes the current performance of the algorithm.
java.util.Random	getRandom() Gets the random number generator used by this object.
MixtureOfGaussians.PDF	getResult() Gets the current result of the algorithm.
double	getTolerance() Getter for tolerance
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
void	setRandom(java.util.Random random) Sets the random number generator used by this object.
void	setTolerance(double tolerance) Setter for tolerance
protected boolean	step() Called to take a single step of the learning algorithm.

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

Field Detail

PERFORMANCE_NAME

public static final java.lang.String PERFORMANCE_NAME

Name of the performance measurement, "Assignment Change".

See Also:

Constant Field Values

DEFAULT_MAX_ITERATIONS

public static final int DEFAULT_MAX_ITERATIONS

Default max iterations, 100.

See Also:

Constant Field Values

DEFAULT_TOLERANCE

public static final double DEFAULT_TOLERANCE

Default tolerance, 1.0E-5.

See Also:

Constant Field Values

random

protected java.util.Random random

Random number generator.

Constructor Detail

EMLearner

public EMLearner(java.util.Random random)

Creates a new instance of EMLearner

Parameters:

random - Random number generator

EMLearner

public EMLearner(int distributionCount,
                 java.util.Random random)

Creates a new instance of EMLearner

Parameters:

distributionCount - Number of distributions in the mixture

random - Random number generator

EMLearner

public EMLearner(int distributionCount,
                 MultivariateGaussian.WeightedMaximumLikelihoodEstimator learner,
                 java.util.Random random)

Creates a new instance of EMLearner

Parameters:

distributionCount - Number of distributions in the mixture

learner - Learner used to reestimate the components

random - Random number generator

Method Detail

initializeAlgorithm

protected boolean initializeAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.

Specified by:

initializeAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends Vector>,MixtureOfGaussians.PDF>

Returns:

True if the learning algorithm can be run and false if it cannot.

step

protected boolean step()

Description copied from class: AbstractAnytimeBatchLearner

Called to take a single step of the learning algorithm.

Specified by:

step in class AbstractAnytimeBatchLearner<java.util.Collection<? extends Vector>,MixtureOfGaussians.PDF>

Returns:

True if another step can be taken and false it the algorithm should halt.

cleanupAlgorithm

protected void cleanupAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to clean up the learning algorithm's state after learning has finished.

Specified by:

cleanupAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends Vector>,MixtureOfGaussians.PDF>

getResult

public MixtureOfGaussians.PDF getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<MixtureOfGaussians.PDF>

Returns:

Current result of the algorithm.

getPerformance

public NamedValue<java.lang.Double> getPerformance()

Description copied from interface: MeasurablePerformanceAlgorithm

Gets the name-value pair that describes the current performance of the algorithm. For most algorithms, this is the value that they are attempting to optimize.

Specified by:

getPerformance in interface MeasurablePerformanceAlgorithm

Returns:

The name-value pair that describes the current performance of the algorithm.

getTolerance

public double getTolerance()

Getter for tolerance

Returns:

Tolerance before stopping, must be greater than or equal to 0

setTolerance

public void setTolerance(double tolerance)

Setter for tolerance

Parameters:

tolerance - Tolerance before stopping, must be greater than or equal to 0

getRandom

public java.util.Random getRandom()

Description copied from interface: Randomized

Gets the random number generator used by this object.

Specified by:

getRandom in interface Randomized

Returns:

The random number generator used by this object.

setRandom

public void setRandom(java.util.Random random)

Description copied from interface: Randomized

Sets the random number generator used by this object.

Specified by:

setRandom in interface Randomized

Parameters:

random - The random number generator for this object to use.

getAssignmentChanged

public double getAssignmentChanged()

Gets the total assignment change from the last completed step of the algorithm.

Returns:

The assignment changed from the last completed step.