gov.sandia.cognition.statistics.distribution

Class UniformIntegerDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

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

gov.sandia.cognition.statistics.AbstractClosedFormIntegerDistribution

gov.sandia.cognition.statistics.distribution.UniformIntegerDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDistribution<java.lang.Number>, ClosedFormDiscreteUnivariateDistribution<java.lang.Number>, ClosedFormDistribution<java.lang.Number>, ClosedFormUnivariateDistribution<java.lang.Number>, ComputableDistribution<java.lang.Number>, DiscreteDistribution<java.lang.Number>, Distribution<java.lang.Number>, DistributionWithMean<java.lang.Number>, EstimableDistribution<java.lang.Number,UniformIntegerDistribution>, IntegerDistribution, UnivariateDistribution<java.lang.Number>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

UniformIntegerDistribution.CDF, UniformIntegerDistribution.PMF

@PublicationReference(author="Wikipedia",
                      title="Uniform distribution (discrete)",
                      type=WebPage,
                      year=2015,
                      url="https://en.wikipedia.org/wiki/Uniform_distribution_(discrete)")
public class UniformIntegerDistribution
extends AbstractClosedFormIntegerDistribution
implements ClosedFormDiscreteUnivariateDistribution<java.lang.Number>, EstimableDistribution<java.lang.Number,UniformIntegerDistribution>

Contains the (very simple) definition of a continuous Uniform distribution, parameterized between the minimum and maximum bounds. The uniform distribution has equal mass on all of the integers between the two bounds, and including them. For example, if the bounds were (10, 14) then the values 10, 11, 12, 13, and 14 would all have probability 1/5 of being sampled.

Since:

3.4.3

Author:

Justin Basilico

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	UniformIntegerDistribution.CDF Implements the cumulative distribution function for the discrete uniform distribution.
static class	UniformIntegerDistribution.MaximumLikelihoodEstimator Implements a maximum likelihood estimator for the discrete uniform distribution.
static class	UniformIntegerDistribution.PMF Probability mass function of a discrete uniform distribution.

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_MAX_SUPPORT The default maximum support is 0.
static int	DEFAULT_MIN_SUPPORT The default minimum support is 0.

Constructor Summary

Constructors

Constructor and Description
UniformIntegerDistribution() Creates a new UniformIntegerDistribution with default parameters.
UniformIntegerDistribution(int minSupport, int maxSupport) Creates a new UniformIntegerDistribution with the given bounds.
UniformIntegerDistribution(UniformIntegerDistribution other) Creates a new UniformIntegerDistribution that is a copy of the given other distribution.

Method Summary

Modifier and Type	Method and Description
UniformIntegerDistribution	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.
UniformIntegerDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
java.util.Set<? extends java.lang.Number>	getDomain() Returns an object that allows an iteration through the domain (x-axis, independent variable) of the Distribution
int	getDomainSize() Gets the size of the domain.
UniformIntegerDistribution.MaximumLikelihoodEstimator	getEstimator() Gets an estimator associated with this distribution.
java.lang.Integer	getMaxSupport() Gets the minimum support (domain or input) of the distribution.
java.lang.Number	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.Integer	getMinSupport() Gets the minimum support (domain or input) of the distribution.
UniformIntegerDistribution.PMF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
double	getVariance() Gets the variance of the distribution.
int	sampleAsInt(java.util.Random random) Draws a single random sample from the distribution as an int.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super java.lang.Number> output) Draws multiple random samples from the distribution and puts the result into the given collection.
void	setMaxSupport(int maxSupport) Sets the maximum support.
void	setMinSupport(int minSupport) Sets the minimum support.

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

sampleAsInts, 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_MIN_SUPPORT

public static final int DEFAULT_MIN_SUPPORT

The default minimum support is 0.

See Also:

Constant Field Values

DEFAULT_MAX_SUPPORT

public static final int DEFAULT_MAX_SUPPORT

The default maximum support is 0.

See Also:

Constant Field Values

Constructor Detail

UniformIntegerDistribution

public UniformIntegerDistribution()

Creates a new UniformIntegerDistribution with default parameters.

UniformIntegerDistribution

public UniformIntegerDistribution(int minSupport,
                                  int maxSupport)

Creates a new UniformIntegerDistribution with the given bounds.

Parameters:

minSupport - The minimum bound on the distribution (inclusive).

maxSupport - The maximum bound on the distribution (exclusive).

UniformIntegerDistribution

public UniformIntegerDistribution(UniformIntegerDistribution other)

Creates a new UniformIntegerDistribution that is a copy of the given other distribution.

Parameters:

other - The distribution to copy.

Method Detail

clone

public UniformIntegerDistribution 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.Number>

Returns:

A clone of this object.

getProbabilityFunction

public UniformIntegerDistribution.PMF 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.Number>

Specified by:

getProbabilityFunction in interface DiscreteDistribution<java.lang.Number>

Returns:

Distribution function associated with this Distribution.

getCDF

public UniformIntegerDistribution.CDF getCDF()

Description copied from interface: UnivariateDistribution

Gets the CDF of a scalar distribution.

Specified by:

getCDF in interface ClosedFormUnivariateDistribution<java.lang.Number>

Specified by:

getCDF in interface UnivariateDistribution<java.lang.Number>

Returns:

CDF of the scalar distribution.

getMean

public java.lang.Number 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.Number>

Returns:

Mean of 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.

getMinSupport

public java.lang.Integer getMinSupport()

Description copied from interface: UnivariateDistribution

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

Specified by:

getMinSupport in interface UnivariateDistribution<java.lang.Number>

Returns:

Minimum support.

setMinSupport

public void setMinSupport(int minSupport)

Sets the minimum support. It is the smallest value in the uniform distribution, and is inclusive. It should be less than (or equal to) the maximum support.

Parameters:

minSupport - The minimum support.

getMaxSupport

public java.lang.Integer getMaxSupport()

Description copied from interface: UnivariateDistribution

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

Specified by:

getMaxSupport in interface UnivariateDistribution<java.lang.Number>

Returns:

Minimum support.

setMaxSupport

public void setMaxSupport(int maxSupport)

Sets the maximum support. It is the largest value in the uniform distribution, and is inclusive. It should be greater than (or equal to) the minimum support.

Parameters:

maxSupport - The maximum support.

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.Number>

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.Number>

Returns:

Variance of the distribution.

getDomain

public java.util.Set<? extends java.lang.Number> getDomain()

Description copied from interface: DiscreteDistribution

Returns an object that allows an iteration through the domain (x-axis, independent variable) of the Distribution

Specified by:

getDomain in interface DiscreteDistribution<java.lang.Number>

Returns:

Collection that enumerates each value that the domain can take

getDomainSize

public int getDomainSize()

Description copied from interface: DiscreteDistribution

Gets the size of the domain.

Specified by:

getDomainSize in interface DiscreteDistribution<java.lang.Number>

Returns:

The size of the domain.

sampleAsInt

public int sampleAsInt(java.util.Random random)

Description copied from interface: IntegerDistribution

Draws a single random sample from the distribution as an int.

Specified by:

sampleAsInt in interface IntegerDistribution

Parameters:

random - The random number generator to use.

Returns:

A sample from the distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super java.lang.Number> output)

Description copied from interface: Distribution

Draws multiple random samples from the distribution and puts the result into the given collection.

Specified by:

sampleInto in interface Distribution<java.lang.Number>

Parameters:

random - Random number generator to use.

sampleCount - The number of samples to draw. Cannot be negative.

output - The collection to add the samples into.

getEstimator

public UniformIntegerDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

getEstimator in interface EstimableDistribution<java.lang.Number,UniformIntegerDistribution>

Returns:

A distribution estimator associated for this distribution.