gov.sandia.cognition.statistics.bayesian

Class AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.math.AbstractScalarFunction<java.lang.Double>

gov.sandia.cognition.math.AbstractUnivariateScalarFunction

gov.sandia.cognition.statistics.bayesian.AdaptiveRejectionSampling.LogEvaluator<EvaluatorType>

Type Parameters:

EvaluatorType - Type of Evaluator to wrap

All Implemented Interfaces:

Evaluator<java.lang.Double,java.lang.Double>, ScalarFunction<java.lang.Double>, UnivariateScalarFunction, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

AdaptiveRejectionSampling.PDFLogEvaluator

Enclosing class:

AdaptiveRejectionSampling

public abstract static class AdaptiveRejectionSampling.LogEvaluator<EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double>>
extends AbstractUnivariateScalarFunction

Wraps an Evaluator and takes the natural logarithm of the evaluate method

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected EvaluatorType	function Evaluator to wrap and compute the natural logarithm of.

Constructor Summary

Constructors

Constructor and Description
LogEvaluator(EvaluatorType function) Creates a new instance of LogEvaluator

Method Summary

Modifier and Type	Method and Description
AdaptiveRejectionSampling.LogEvaluator<EvaluatorType>	clone() This makes public the clone method on the Object class and removes the exception that it throws.
double	evaluate(double input) Produces a double output for the given double input
EvaluatorType	getFunction() Getter for function
void	setFunction(EvaluatorType function) Setter for function

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.math.UnivariateScalarFunction

evaluate, evaluateAsDouble

Field Detail

function

protected EvaluatorType extends Evaluator<java.lang.Double,java.lang.Double> function

Evaluator to wrap and compute the natural logarithm of.

Constructor Detail

LogEvaluator

public LogEvaluator(EvaluatorType function)

Creates a new instance of LogEvaluator

Parameters:

function - Evaluator to wrap and compute the natural logarithm of.

Method Detail

clone

public AdaptiveRejectionSampling.LogEvaluator<EvaluatorType> 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 AbstractCloneableSerializable

Returns:

A clone of this object.

getFunction

public EvaluatorType getFunction()

Getter for function

Returns:

Evaluator to wrap and compute the natural logarithm of.

setFunction

public void setFunction(EvaluatorType function)

Setter for function

Parameters:

function - Evaluator to wrap and compute the natural logarithm of.

evaluate

public double evaluate(double input)

Description copied from interface: UnivariateScalarFunction

Produces a double output for the given double input

Parameters:

input - Input to the Evaluator

Returns:

output at the given input