gov.sandia.cognition.statistics.distribution

Class BinomialDistribution

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.BinomialDistribution

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,BinomialDistribution>, IntegerDistribution, UnivariateDistribution<java.lang.Number>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

BinomialDistribution.CDF, BinomialDistribution.PMF

@PublicationReference(author="Wikipedia",
                      title="Binomial distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Binomial_distribution")
public class BinomialDistribution
extends AbstractClosedFormIntegerDistribution
implements ClosedFormDiscreteUnivariateDistribution<java.lang.Number>, EstimableDistribution<java.lang.Number,BinomialDistribution>

Binomial distribution, which is a collection of Bernoulli trials

Since:

2.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	BinomialDistribution.CDF CDF of the Binomial distribution, which is the probability of getting up to "x" successes in "N" trials with a Bernoulli probability of "p"
static class	BinomialDistribution.MaximumLikelihoodEstimator Maximum likelihood estimator of the distribution
static class	BinomialDistribution.PMF The Probability Mass Function of a binomial distribution.

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_N Default N, 1.
static double	DEFAULT_P Default p, 0.5.

Constructor Summary

Constructors

Constructor and Description
BinomialDistribution() Default constructor.
BinomialDistribution(BinomialDistribution other) Copy constructor
BinomialDistribution(int N, double p) Creates a new instance of BinomialDistribution

Method Summary

Modifier and Type	Method and Description
BinomialDistribution	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.
BinomialDistribution.CDF	getCDF() Gets the CDF of a scalar distribution.
IntegerSpan	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.
BinomialDistribution.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.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.Integer	getMinSupport() Gets the minimum support (domain or input) of the distribution.
int	getN() Getter for N
double	getP() Getter for p
BinomialDistribution.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[] output, int start, int length) Samples values from this distribution as an array of ints.
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	setN(int N) Setter for N
void	setP(double p) Setter for p

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

sampleAsInts

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_P

public static final double DEFAULT_P

Default p, 0.5.

See Also:

Constant Field Values

DEFAULT_N

public static final int DEFAULT_N

Default N, 1.

See Also:

Constant Field Values

Constructor Detail

BinomialDistribution

public BinomialDistribution()

Default constructor.

BinomialDistribution

public BinomialDistribution(int N,
                            double p)

Creates a new instance of BinomialDistribution

Parameters:

N - Total number of experiments, must be greater than zero

p - Probability of a positive outcome (Bernoulli probability), [0,1]

BinomialDistribution

public BinomialDistribution(BinomialDistribution other)

Copy constructor

Parameters:

other - BinomialDistribution to copy

Method Detail

clone

public BinomialDistribution 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.

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.Number>

Returns:

Mean of the distribution.

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()

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.

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.

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[] output,
                       int start,
                       int length)

Description copied from interface: IntegerDistribution

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

Specified by:

sampleInto in interface IntegerDistribution

Overrides:

sampleInto in class AbstractClosedFormIntegerDistribution

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.

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.

getN

public int getN()

Getter for N

Returns:

Total number of experiments, must be greater than zero

setN

public void setN(int N)

Setter for N

Parameters:

N - Total number of experiments, must be greater than zero

getP

public double getP()

Getter for p

Returns:

Probability of a positive outcome (Bernoulli probability), [0,1]

setP

public void setP(double p)

Setter for p

Parameters:

p - Probability of a positive outcome (Bernoulli probability), [0,1]

getDomain

public IntegerSpan 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.

getCDF

public BinomialDistribution.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.

getProbabilityFunction

public BinomialDistribution.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.

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.

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.

getEstimator

public BinomialDistribution.MaximumLikelihoodEstimator getEstimator()

Description copied from interface: EstimableDistribution

Gets an estimator associated with this distribution.

Specified by:

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

Returns:

A distribution estimator associated for this distribution.