gov.sandia.cognition.statistics.distribution

Class ExponentialDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.statistics.AbstractDistribution<NumberType>

gov.sandia.cognition.statistics.AbstractClosedFormUnivariateDistribution<java.lang.Double>

gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution

gov.sandia.cognition.statistics.distribution.ExponentialDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<java.lang.Double>, ClosedFormDistribution<java.lang.Double>, ClosedFormUnivariateDistribution<java.lang.Double>, ComputableDistribution<java.lang.Double>, Distribution<java.lang.Double>, DistributionWithMean<java.lang.Double>, EstimableDistribution<java.lang.Double,ExponentialDistribution>, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

ExponentialDistribution.CDF, ExponentialDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Exponential distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Exponential_distribution")
public class ExponentialDistribution
extends AbstractClosedFormSmoothUnivariateDistribution
implements EstimableDistribution<java.lang.Double,ExponentialDistribution>

An Exponential distribution describes the time between events in a poisson process, resulting in a memoryless distribution.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ExponentialDistribution.CDF CDF of the ExponentialDistribution.
static class	ExponentialDistribution.MaximumLikelihoodEstimator Creates a ExponentialDistribution from data
static class	ExponentialDistribution.PDF PDF of the ExponentialDistribution.
static class	ExponentialDistribution.WeightedMaximumLikelihoodEstimator Creates a ExponentialDistribution from weighted data

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_RATE Default rate, 1.0.
protected double	rate Rate, or inverse scale, of the distribution, must be greater than zero.

Constructor Summary

Constructors

Constructor and Description
ExponentialDistribution() Creates a new instance of ExponentialDistribution
ExponentialDistribution(double rate) Creates a new instance of ExponentialDistribution
ExponentialDistribution(ExponentialDistribution other) Creates a new instance of ExponentialDistribution

Method Summary

Modifier and Type	Method and Description
ExponentialDistribution	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Converts the object from a Vector of parameters.
Vector	convertToVector() Converts the object to a vector.
ExponentialDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
ExponentialDistribution.MaximumLikelihoodEstimator	getEstimator() Gets an estimator associated with this distribution.
java.lang.Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
double	getMeanAsDouble() Gets the mean of the distribution as a double.
java.lang.Double	getMinSupport() Gets the minimum support (domain or input) of the distribution.
ExponentialDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getRate() Getter for rate.
double	getVariance() Gets the variance of the distribution.
double	sampleAsDouble(java.util.Random random) Samples a value from this distribution as a double.
void	sampleInto(java.util.Random random, double[] output, int start, int length) Samples values from this distribution as an array of doubles.
void	setRate(double rate) Setter for rate.
java.lang.String	toString()

Methods inherited from class gov.sandia.cognition.statistics.AbstractClosedFormSmoothUnivariateDistribution

getMean, sampleAsDoubles, sampleInto

Methods inherited from class gov.sandia.cognition.statistics.AbstractDistribution

sample, sample

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.statistics.Distribution

sample, sample, sampleInto

Field Detail

DEFAULT_RATE

public static final double DEFAULT_RATE

Default rate, 1.0.

See Also:

Constant Field Values

rate

protected double rate

Rate, or inverse scale, of the distribution, must be greater than zero.

Constructor Detail

ExponentialDistribution

public ExponentialDistribution()

Creates a new instance of ExponentialDistribution

ExponentialDistribution

public ExponentialDistribution(double rate)

Creates a new instance of ExponentialDistribution

Parameters:

rate - Rate, or inverse scale, of the distribution, must be greater than zero.

ExponentialDistribution

public ExponentialDistribution(ExponentialDistribution other)

Creates a new instance of ExponentialDistribution

Parameters:

other - ExponentialDistribution to copy.

Method Detail

clone

public ExponentialDistribution 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 Vectorizable

Specified by:

clone in interface CloneableSerializable

Overrides:

clone in class AbstractClosedFormUnivariateDistribution<java.lang.Double>

Returns:

A clone of this object.

getRate

public double getRate()

Getter for rate.

Returns:

Rate, or inverse scale, of the distribution, must be greater than zero.

setRate

public void setRate(double rate)

Setter for rate.

Parameters:

rate - Rate, or inverse scale, of the distribution, must be greater than zero.

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a double.

Specified by:

getMeanAsDouble in interface UnivariateDistribution<java.lang.Double>

Returns:

The mean as a double.

sampleAsDouble

@PublicationReference(author={"Christian P. Robert","George Casella"},
                      title="Monte Carlo Statistical Methods, Second Edition",
                      type=Book,
                      year=2004,
                      pages=39,
                      notes="Example 2.5")
public double sampleAsDouble(java.util.Random random)

Description copied from interface: SmoothUnivariateDistribution

Samples a value from this distribution as a double. This is a convenience method to potentially avoid boxing.

Specified by:

sampleAsDouble in interface SmoothUnivariateDistribution

Overrides:

sampleAsDouble in class AbstractClosedFormSmoothUnivariateDistribution

Parameters:

random - Random number generator to use.

Returns:

A value sampled from this distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       double[] output,
                       int start,
                       int length)

Description copied from interface: SmoothUnivariateDistribution

Samples values from this distribution as an array of doubles. This is a convenience method to potentially avoid boxing.

Specified by:

sampleInto in interface SmoothUnivariateDistribution

Parameters:

random - Random number generator to use.

output - The array to write the result into. Cannot be null.

start - The offset in the array to start writing at. Cannot be negative.

length - The number of values to sample. Cannot be negative.

getCDF

public ExponentialDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<java.lang.Double>

Specified by:

getCDF in interface SmoothUnivariateDistribution

Specified by:

getCDF in interface UnivariateDistribution<java.lang.Double>

Returns:

CDF of the scalar distribution.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

Specified by:

convertToVector in interface Vectorizable

Returns:

The Vector form of the object.

convertFromVector

public void convertFromVector(Vector parameters)

Description copied from interface: Vectorizable

Converts the object from a Vector of parameters.

Specified by:

convertFromVector in interface Vectorizable

Parameters:

parameters - The parameters to incorporate.

getVariance

public double getVariance()

Description copied from interface: UnivariateDistribution

Gets the variance of the distribution. This is sometimes called the second central moment by more pedantic people, which is equivalent to the square of the standard deviation.

Specified by:

getVariance in interface UnivariateDistribution<java.lang.Double>

Returns:

Variance of the distribution.

getProbabilityFunction

public ExponentialDistribution.PDF getProbabilityFunction()

Description copied from interface: ComputableDistribution

Gets the distribution function associated with this Distribution, either the PDF or PMF.

Specified by:

getProbabilityFunction in interface ComputableDistribution<java.lang.Double>

Specified by:

getProbabilityFunction in interface SmoothUnivariateDistribution

Returns:

Distribution function associated with this Distribution.

getMinSupport

public java.lang.Double getMinSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMinSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getMaxSupport

public java.lang.Double getMaxSupport()

Description copied from interface: UnivariateDistribution

Gets the minimum support (domain or input) of the distribution.

Specified by:

getMaxSupport in interface UnivariateDistribution<java.lang.Double>

Returns:

Minimum support.

getEstimator

public ExponentialDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<java.lang.Double,ExponentialDistribution>

Returns:

A distribution estimator associated for this distribution.

toString

public java.lang.String toString()

Overrides:

toString in class java.lang.Object