gov.sandia.cognition.statistics.method

Class DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper<DistributionType,FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper>>

gov.sandia.cognition.statistics.method.DistributionParameterEstimator<DataType,DistributionType>

Type Parameters:

DataType - Type of data generated by the distribution

DistributionType - Type of distribution to estimate the parameters of.

All Implemented Interfaces:

AnytimeAlgorithm<DistributionType>, IterativeAlgorithm, IterativeAlgorithmListener, MeasurablePerformanceAlgorithm, StoppableAlgorithm, BatchLearner<java.util.Collection<? extends DataType>,DistributionType>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class DistributionParameterEstimator<DataType,DistributionType extends ClosedFormDistribution<? extends DataType>>
extends AnytimeAlgorithmWrapper<DistributionType,FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper>>
implements BatchLearner<java.util.Collection<? extends DataType>,DistributionType>, MeasurablePerformanceAlgorithm

A method of estimating the parameters of a distribution using an arbitrary CostFunction and FunctionMinimizer algorithm.

Since:

3.1

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
protected class	DistributionParameterEstimator.DistributionWrapper Maps the parameters of a Distribution and a CostFunction into a Vector/Double Evaluator.

Field Summary

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

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
DistributionParameterEstimator(DistributionType distribution, CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction) Creates a new instance of DistributionParameterEstimator
DistributionParameterEstimator(DistributionType distribution, CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction, FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper> algorithm) Creates a new instance of DistributionParameterEstimator

Method Summary

Modifier and Type	Method and Description
DistributionParameterEstimator<DataType,DistributionType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
NamedValue<? extends java.lang.Number>	getPerformance() Gets the name-value pair that describes the current performance of the algorithm.
DistributionType	getResult() Gets the current result of the algorithm.
DistributionType	learn(java.util.Collection<? extends DataType> minimizationParameters) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

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

algorithmEnded, algorithmStarted, getAlgorithm, getIteration, getMaxIterations, isResultValid, readResolve, setAlgorithm, setMaxIterations, stepEnded, stepStarted, stop

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

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, 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.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, removeIterativeAlgorithmListener

Constructor Detail

DistributionParameterEstimator

public DistributionParameterEstimator(DistributionType distribution,
                                      CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction)

Creates a new instance of DistributionParameterEstimator

Parameters:

distribution - Distribution to estimate the parameters of

costFunction - Cost function to use in the minimization procedure

DistributionParameterEstimator

public DistributionParameterEstimator(DistributionType distribution,
                                      CostFunction<? super DistributionType,java.util.Collection<? extends DataType>> costFunction,
                                      FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper> algorithm)

Creates a new instance of DistributionParameterEstimator

Parameters:

distribution - Distribution to estimate the parameters of

costFunction - Cost function to use in the minimization procedure

algorithm - Minimization algorithm to use, such as FunctionMinimizerBFGS, FunctionMinimizerDirectionSetPowell, etc.

Method Detail

clone

public DistributionParameterEstimator<DataType,DistributionType> 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 AnytimeAlgorithmWrapper<DistributionType extends ClosedFormDistribution<? extends DataType>,FunctionMinimizer<Vector,java.lang.Double,? super DistributionParameterEstimator.DistributionWrapper>>

Returns:

A clone of this object.

learn

public DistributionType learn(java.util.Collection<? extends DataType> minimizationParameters)

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>,DistributionType extends ClosedFormDistribution<? extends DataType>>

Parameters:

minimizationParameters - 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.

getResult

public DistributionType getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<DistributionType extends ClosedFormDistribution<? extends DataType>>

Returns:

Current result of the algorithm.

getPerformance

public NamedValue<? extends java.lang.Number> 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.