gov.sandia.cognition.statistics.method

Class MaximumLikelihoodDistributionEstimator<DataType>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractParallelAlgorithm

gov.sandia.cognition.statistics.method.MaximumLikelihoodDistributionEstimator<DataType>

Type Parameters:

DataType - Type of data generated by the distributions.

All Implemented Interfaces:

ParallelAlgorithm, BatchLearner<java.util.Collection<? extends DataType>,ClosedFormComputableDistribution<DataType>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class MaximumLikelihoodDistributionEstimator<DataType>
extends AbstractParallelAlgorithm
implements BatchLearner<java.util.Collection<? extends DataType>,ClosedFormComputableDistribution<DataType>>

Estimates the most-likely distribution, and corresponding parameters, of that generated the given data from a pre-determined collection of candidate parameteric distributions.

Since:

3.1

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MaximumLikelihoodDistributionEstimator.DistributionEstimationTask<DataType> Estimates the optimal parameters of a single distribution

Constructor Summary

Constructors

Constructor and Description
MaximumLikelihoodDistributionEstimator() Creates a new instance of MaximumLikelihoodDistributionEstimator
MaximumLikelihoodDistributionEstimator(java.util.Collection<? extends ClosedFormComputableDistribution<DataType>> distributions) Creates a new instance of MaximumLikelihoodDistributionEstimator

Method Summary

Modifier and Type	Method and Description
MaximumLikelihoodDistributionEstimator<DataType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
static SmoothUnivariateDistribution	estimateContinuousDistribution(java.util.Collection<java.lang.Double> data) Estimates a continuous distribution.
static ClosedFormDiscreteUnivariateDistribution	estimateDiscreteDistribution(java.util.Collection<? extends java.lang.Number> data) Estimates a discrete distribution.
protected static <DistributionType extends ClosedFormComputableDistribution<?>> java.util.LinkedList<DistributionType>	getDistributionClasses(java.lang.Class<? extends DistributionType> baseDistribution) Gets the distribution classes for the given base distribution.
java.util.Collection<? extends ClosedFormComputableDistribution<DataType>>	getDistributions() Getter for distributions
ClosedFormComputableDistribution<DataType>	learn(java.util.Collection<? extends DataType> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setDistributions(java.util.Collection<? extends ClosedFormComputableDistribution<DataType>> distributions) Setter for distributions

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

getNumThreads, getThreadPool, setThreadPool

Methods inherited from class java.lang.Object

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

Constructor Detail

MaximumLikelihoodDistributionEstimator

public MaximumLikelihoodDistributionEstimator()

Creates a new instance of MaximumLikelihoodDistributionEstimator

MaximumLikelihoodDistributionEstimator

public MaximumLikelihoodDistributionEstimator(java.util.Collection<? extends ClosedFormComputableDistribution<DataType>> distributions)

Creates a new instance of MaximumLikelihoodDistributionEstimator

Parameters:

distributions - Collection of Distributions to estimate the optimal parameters of

Method Detail

clone

public MaximumLikelihoodDistributionEstimator<DataType> clone()

Description copied from class: AbstractCloneableSerializable

This makes public the clone method on the Object class and removes the exception that it throws. Its default behavior is to automatically create a clone of the exact type of object that the clone is called on and to copy all primitives but to keep all references, which means it is a shallow copy. Extensions of this class may want to override this method (but call super.clone() to implement a "smart copy". That is, to target the most common use case for creating a copy of the object. Because of the default behavior being a shallow copy, extending classes only need to handle fields that need to have a deeper copy (or those that need to be reset). Some of the methods in ObjectUtil may be helpful in implementing a custom clone method. Note: The contract of this method is that you must use super.clone() as the basis for your implementation.

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractParallelAlgorithm

Returns:

A clone of this object.

getDistributions

public java.util.Collection<? extends ClosedFormComputableDistribution<DataType>> getDistributions()

Getter for distributions

Returns:

Collection of Distributions to estimate the optimal parameters of

setDistributions

public void setDistributions(java.util.Collection<? extends ClosedFormComputableDistribution<DataType>> distributions)

Setter for distributions

Parameters:

distributions - Collection of Distributions to estimate the optimal parameters of

learn

public ClosedFormComputableDistribution<DataType> learn(java.util.Collection<? extends DataType> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<java.util.Collection<? extends DataType>,ClosedFormComputableDistribution<DataType>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.

estimateContinuousDistribution

public static SmoothUnivariateDistribution estimateContinuousDistribution(java.util.Collection<java.lang.Double> data)
                                                                   throws java.lang.Exception

Estimates a continuous distribution.

Parameters:

data - The data to estimate a distribution for.

Returns:

The estimated distribution.

Throws:

java.lang.Exception - If there is an error in the estimation.

estimateDiscreteDistribution

public static ClosedFormDiscreteUnivariateDistribution estimateDiscreteDistribution(java.util.Collection<? extends java.lang.Number> data)
                                                                             throws java.lang.Exception

Estimates a discrete distribution.

Parameters:

data - The data to estimate a distribution for.

Returns:

The estimated distribution.

Throws:

java.lang.Exception - If there is an error in the estimation.

getDistributionClasses

protected static <DistributionType extends ClosedFormComputableDistribution<?>> java.util.LinkedList<DistributionType> getDistributionClasses(java.lang.Class<? extends DistributionType> baseDistribution)
                                                                                                                                       throws java.lang.ClassNotFoundException,
                                                                                                                                              java.io.IOException,
                                                                                                                                              java.lang.InstantiationException,
                                                                                                                                              java.lang.IllegalAccessException

Gets the distribution classes for the given base distribution.

Type Parameters:

DistributionType - The type of distribution.

Parameters:

baseDistribution - The class of the base distribution.

Returns:

The list of implementations of that distribution in the statistics distribution package.

Throws:

java.lang.ClassNotFoundException

java.io.IOException

java.lang.InstantiationException

java.lang.IllegalAccessException