gov.sandia.cognition.statistics.distribution

Class ChiSquareDistribution

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.ChiSquareDistribution

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:

ChiSquareDistribution.CDF, ChiSquareDistribution.PDF

@PublicationReference(author="Wikipedia",
                      title="Chi-square distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Chi-square_distribution")
public class ChiSquareDistribution
extends AbstractClosedFormSmoothUnivariateDistribution

Describes a Chi-Square Distribution. The Chi-Square distribution occurs when Y = X1^2 + X2^2 + ... + Xn^2. In this case, Y will be a Chi-Square Random Variable with "n" degrees of freedom, iff the Xi are independent Gaussian Random Variables. The chi-square distribution is a member of the Gamma-distribution family.

Since:

2.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	ChiSquareDistribution.CDF Cumulative Distribution Function (CDF) of a Chi-Square Distribution
static class	ChiSquareDistribution.PDF PDF of the Chi-Square distribution

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_DEGREES_OF_FREEDOM Default degrees of freedom, 2.0.

Constructor Summary

Constructors

Constructor and Description
ChiSquareDistribution() Default constructor.
ChiSquareDistribution(ChiSquareDistribution other) Copy constructor
ChiSquareDistribution(double degreesOfFreedom) Creates a new instance of ChiSquareDistribution

Method Summary

Modifier and Type	Method and Description
ChiSquareDistribution	clone() This makes public the clone method on the Object class and removes the exception that it throws.
void	convertFromVector(Vector parameters) Sets the parameter of the chi-square PDF
Vector	convertToVector() Returns the parameter of the chi-square PDF
ChiSquareDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
double	getDegreesOfFreedom() Getter for degrees of freedom
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.
ChiSquareDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getVariance() Gets the variance of the distribution.
static java.util.ArrayList<java.lang.Double>	sample(double degreesOfFreedom, java.util.Random random, int numSamples) Samples from a Chi-Square distribution with the given degrees of freedom
static double[]	sampleAsDoubles(double degreesOfFreedom, java.util.Random random, int numSamples) Samples from a Chi-Square distribution with the given degrees of freedom
void	sampleInto(java.util.Random random, double[] output, int start, int length) Samples values from this distribution as an array of doubles.
void	setDegreesOfFreedom(double degreesOfFreedom) Setter for degrees of freedom

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

getMean, sampleAsDouble, 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_DEGREES_OF_FREEDOM

public static final double DEFAULT_DEGREES_OF_FREEDOM

Default degrees of freedom, 2.0.

See Also:

Constant Field Values

Constructor Detail

ChiSquareDistribution

public ChiSquareDistribution()

Default constructor.

ChiSquareDistribution

public ChiSquareDistribution(double degreesOfFreedom)

Creates a new instance of ChiSquareDistribution

Parameters:

degreesOfFreedom - Number of degrees of freedom in the distribution, must be greater than 0.0

ChiSquareDistribution

public ChiSquareDistribution(ChiSquareDistribution other)

Copy constructor

Parameters:

other - ChiSquareDistribution to copy

Method Detail

clone

public ChiSquareDistribution 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.

getDegreesOfFreedom

public double getDegreesOfFreedom()

Getter for degrees of freedom

Returns:

Number of degrees of freedom in the distribution, must be greater than 0.0

setDegreesOfFreedom

public void setDegreesOfFreedom(double degreesOfFreedom)

Setter for degrees of freedom

Parameters:

degreesOfFreedom - Number of degrees of freedom in the distribution, must be greater than 0.0

getMeanAsDouble

public double getMeanAsDouble()

Description copied from interface: UnivariateDistribution

Gets the mean of the distribution as a double.

Returns:

The mean as a double.

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.

convertToVector

public Vector convertToVector()

Returns the parameter of the chi-square PDF

Returns:

1-dimensional Vector containing the degrees of freedom

convertFromVector

public void convertFromVector(Vector parameters)

Sets the parameter of the chi-square PDF

Parameters:

parameters - 1-dimensional Vector containing the degrees of freedom

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.

sampleAsDoubles

public static double[] sampleAsDoubles(double degreesOfFreedom,
                                       java.util.Random random,
                                       int numSamples)

Samples from a Chi-Square distribution with the given degrees of freedom

Parameters:

degreesOfFreedom - Degrees of freedom of the Chi-Square distribution

random - Random number generator

numSamples - Number of samples to generate

Returns:

Samples from the GammaDistribution using the Chi-Square DOFs.

sample

public static java.util.ArrayList<java.lang.Double> sample(double degreesOfFreedom,
                                                           java.util.Random random,
                                                           int numSamples)

Samples from a Chi-Square distribution with the given degrees of freedom

Parameters:

degreesOfFreedom - Degrees of freedom of the Chi-Square distribution

random - Random number generator

numSamples - Number of samples to generate

Returns:

Samples from the GammaDistribution using the Chi-Square DOFs.

getCDF

public ChiSquareDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Returns:

CDF of the scalar distribution.

getProbabilityFunction

public ChiSquareDistribution.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.