gov.sandia.cognition.statistics.distribution

Class UniformDistribution

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.UniformDistribution

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

Direct Known Subclasses:

UniformDistribution.CDF, UniformDistribution.PDF

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

Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	UniformDistribution.CDF Cumulative Distribution Function of a uniform
static class	UniformDistribution.MaximumLikelihoodEstimator Maximum Likelihood Estimator of a uniform distribution.
static class	UniformDistribution.PDF Probability density function of a Uniform Distribution

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_MAX Default max, 1.0.
static double	DEFAULT_MIN Default min, 0.0.

Constructor Summary

Constructors

Constructor and Description
UniformDistribution() Creates a new instance of UniformDistribution
UniformDistribution(double minSupport, double maxSupport) Creates a new instance of UniformDistribution
UniformDistribution(UniformDistribution other) Copy constructor

Method Summary

Modifier and Type	Method and Description
UniformDistribution	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.
UniformDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
UniformDistribution.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.
UniformDistribution.PDF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getVariance() Gets the variance of the distribution.
java.lang.Double	sample(java.util.Random random) Draws a single random sample from the distribution.
void	sampleInto(java.util.Random random, double[] output, int start, int length) Samples values from this distribution as an array of doubles.
void	setMaxSupport(double maxSupport) Setter for maxSupport
void	setMinSupport(double minSupport) Setter for minSupport

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

getMean, sampleAsDouble, sampleAsDoubles, sampleInto

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

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, sampleInto

Field Detail

DEFAULT_MIN

public static final double DEFAULT_MIN

Default min, 0.0.

See Also:

Constant Field Values

DEFAULT_MAX

public static final double DEFAULT_MAX

Default max, 1.0.

See Also:

Constant Field Values

Constructor Detail

UniformDistribution

public UniformDistribution()

Creates a new instance of UniformDistribution

UniformDistribution

public UniformDistribution(double minSupport,
                           double maxSupport)

Creates a new instance of UniformDistribution

Parameters:

minSupport - Minimum x bound on the distribution

maxSupport - Maximum bound on the distribution

UniformDistribution

public UniformDistribution(UniformDistribution other)

Copy constructor

Parameters:

other - UniformDistribution to copy

Method Detail

clone

public UniformDistribution 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.

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.

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.

sample

public java.lang.Double sample(java.util.Random random)

Description copied from interface: Distribution

Draws a single random sample from the distribution.

Specified by:

sample in interface Distribution<java.lang.Double>

Overrides:

sample in class AbstractDistribution<java.lang.Double>

Parameters:

random - Random-number generator to use in order to generate random numbers.

Returns:

Sample drawn according to 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.

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.

setMinSupport

public void setMinSupport(double minSupport)

Setter for minSupport

Parameters:

minSupport - Minimum x bound on the distribution

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.

setMaxSupport

public void setMaxSupport(double maxSupport)

Setter for maxSupport

Parameters:

maxSupport - Maximum x bound on the 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.

getCDF

public UniformDistribution.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.

getProbabilityFunction

public UniformDistribution.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.

getEstimator

public UniformDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

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

Returns:

A distribution estimator associated for this distribution.