gov.sandia.cognition.statistics.distribution

Class CauchyDistribution

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.CauchyDistribution

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>, SmoothUnivariateDistribution, UnivariateDistribution<java.lang.Double>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

CauchyDistribution.CDF, CauchyDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Cauchy distribution",
                      type=WebPage,
                      year=2010,
                      url="http://en.wikipedia.org/wiki/Cauchy_distribution")
public class CauchyDistribution
extends AbstractClosedFormSmoothUnivariateDistribution

A Cauchy Distribution is the ratio of two Gaussian Distributions, sometimes known as the Lorentz distribution. The mean is undefined and it has infinite variance.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	CauchyDistribution.CDF CDF of the CauchyDistribution.
static class	CauchyDistribution.PDF PDF of the CauchyDistribution.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_LOCATION Default location, 0.0.
static double	DEFAULT_SCALE Default scale, 1.0.
protected double	location Central location (also the median and mode) of the distribution.
protected double	scale Scale of the distribution, must be greater than zero.

Constructor Summary

Constructors

Constructor and Description
CauchyDistribution() Creates a new instance of CauchyDistribution
CauchyDistribution(CauchyDistribution other) Copy constructor
CauchyDistribution(double location, double scale) Creates a new instance of CauchyDistribution

Method Summary

Modifier and Type	Method and Description
CauchyDistribution	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.
CauchyDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
double	getLocation() Getter for location.
java.lang.Double	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
java.lang.Double	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", 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.
CauchyDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getScale() Getter for scale.
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	setLocation(double location) Setter for location
void	setScale(double scale) Setter for scale

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

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, toString, wait, wait, wait

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

sample, sample

Field Detail

DEFAULT_LOCATION

public static final double DEFAULT_LOCATION

Default location, 0.0.

See Also:

Constant Field Values

DEFAULT_SCALE

public static final double DEFAULT_SCALE

Default scale, 1.0.

See Also:

Constant Field Values

location

protected double location

Central location (also the median and mode) of the distribution.

scale

protected double scale

Scale of the distribution, must be greater than zero.

Constructor Detail

CauchyDistribution

public CauchyDistribution()

Creates a new instance of CauchyDistribution

CauchyDistribution

public CauchyDistribution(double location,
                          double scale)

Creates a new instance of CauchyDistribution

Parameters:

location - Central location (also the median and mode) of the distribution.

scale - Scale of the distribution, must be greater than zero.

CauchyDistribution

public CauchyDistribution(CauchyDistribution other)

Copy constructor

Parameters:

other - CauchyDistribution to copy.

Method Detail

clone

public CauchyDistribution 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.

getLocation

public double getLocation()

Getter for location.

Returns:

Central location (also the median and mode) of the distribution.

setLocation

public void setLocation(double location)

Setter for location

Parameters:

location - Central location (also the median and mode) of the distribution.

getScale

public double getScale()

Getter for scale.

Returns:

Scale of the distribution, must be greater than zero.

setScale

public void setScale(double scale)

Setter for scale

Parameters:

scale - Scale of the distribution, must be greater than zero.

getMean

public java.lang.Double getMean()

Description copied from interface: DistributionWithMean

Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.

Specified by:

getMean in interface DistributionWithMean<java.lang.Double>

Specified by:

getMean in interface SmoothUnivariateDistribution

Overrides:

getMean in class AbstractClosedFormSmoothUnivariateDistribution

Returns:

Mean of the distribution.

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a double.

Returns:

The mean as a double.

sampleAsDouble

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.

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 CauchyDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Returns:

CDF of the scalar distribution.

convertToVector

public Vector convertToVector()

Description copied from interface: Vectorizable

Converts the object to a vector.

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.

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.

Returns:

Variance of the distribution.

getProbabilityFunction

public CauchyDistribution.PDF getProbabilityFunction()

Description copied from interface: ComputableDistribution

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

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.

Returns:

Minimum support.

getMaxSupport

public java.lang.Double getMaxSupport()

Description copied from interface: UnivariateDistribution

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

Returns:

Minimum support.