gov.sandia.cognition.statistics.distribution

Class ScalarMixtureDensityModel.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 java.lang.Double>,ScalarMixtureDensityModel>

gov.sandia.cognition.statistics.distribution.ScalarMixtureDensityModel.EMLearner

All Implemented Interfaces:

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

Enclosing class:

ScalarMixtureDensityModel

public static class ScalarMixtureDensityModel.EMLearner
extends AbstractAnytimeBatchLearner<java.util.Collection<? extends java.lang.Double>,ScalarMixtureDensityModel>
implements Randomized, DistributionEstimator<java.lang.Double,ScalarMixtureDensityModel>, MeasurablePerformanceAlgorithm

An EM learner that estimates a mixture model from data

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 numClusters, DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution> learner, java.util.Random random) Creates a new instance of EMLearner
EMLearner(java.util.Random random) Default constructor
EMLearner(java.util.Random random, java.util.Collection<? extends DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution>> learners) 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.
java.util.Collection<? extends DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution>>	getLearners() Getter for learners
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.
ScalarMixtureDensityModel	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	setLearners(java.util.Collection<? extends DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution>> learners) Setter for learners
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)

Default constructor

Parameters:

random - Random number generator

EMLearner

public EMLearner(int numClusters,
                 DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution> learner,
                 java.util.Random random)

Creates a new instance of EMLearner

Parameters:

numClusters - Number of components to estimate

learner - Learner used for each component

random - Random number generator

EMLearner

public EMLearner(java.util.Random random,
                 java.util.Collection<? extends DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution>> learners)

Creates a new instance of EMLearner

Parameters:

learners - Learner used for each component

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 java.lang.Double>,ScalarMixtureDensityModel>

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 java.lang.Double>,ScalarMixtureDensityModel>

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 java.lang.Double>,ScalarMixtureDensityModel>

getResult

public ScalarMixtureDensityModel getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<ScalarMixtureDensityModel>

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.

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.

getLearners

public java.util.Collection<? extends DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution>> getLearners()

Getter for learners

Returns:

Collection of learners used to create each component.

setLearners

public void setLearners(java.util.Collection<? extends DistributionWeightedEstimator<java.lang.Double,? extends SmoothUnivariateDistribution>> learners)

Setter for learners

Parameters:

learners - Collection of learners used to create each component.

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