gov.sandia.cognition.statistics.distribution

Class MultinomialDistribution

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

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

gov.sandia.cognition.statistics.distribution.MultinomialDistribution

All Implemented Interfaces:

Vectorizable, ClosedFormComputableDiscreteDistribution<Vector>, ClosedFormComputableDistribution<Vector>, ClosedFormDistribution<Vector>, ComputableDistribution<Vector>, DiscreteDistribution<Vector>, Distribution<Vector>, DistributionWithMean<Vector>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

Direct Known Subclasses:

MultinomialDistribution.PMF

@PublicationReference(author="Wikipedia",
                      title="Multinomial distribution",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Multinomial_distribution")
public class MultinomialDistribution
extends AbstractDistribution<Vector>
implements ClosedFormComputableDiscreteDistribution<Vector>

A multinomial distribution is the multivariate/multiclass generalization of the Binomial distribution. That is, the PMF returns the probability of from "numTrials" trials of selecting from "numClasses" classes with the probabilities p1,p2,...p_numClasses. In our class, the probability parameters must be positive but do not have to sum to one because we normalize by the L1 norm.

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	MultinomialDistribution.Domain Allows the iteration through the set of subsets.
static class	MultinomialDistribution.PMF Probability Mass Function of the Multinomial Distribution.

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_NUM_CLASSES Default number of classes (labels or parameters), 2.
static int	DEFAULT_NUM_TRIALS Default number of trials, 1.

Constructor Summary

Constructors

Constructor and Description
MultinomialDistribution() Creates a new instance of MultinomialDistribution
MultinomialDistribution(int numClasses, int numTrials) Creates a new instance of MultinomialDistribution
MultinomialDistribution(MultinomialDistribution other) Copy constructor
MultinomialDistribution(Vector parameters, int numTrials) Creates a new instance of MultinomialDistribution

Method Summary

Modifier and Type	Method and Description
MultinomialDistribution	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.
MultinomialDistribution.Domain	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.
Vector	getMean() Gets the arithmetic mean, or "first central moment" or "expectation", of the distribution.
int	getNumTrials() Getter for numTrials
Vector	getParameters() Getter for parameters
MultinomialDistribution.PMF	getProbabilityFunction() Gets the distribution function associated with this Distribution, either the PDF or PMF.
void	sampleInto(java.util.Random random, int sampleCount, java.util.Collection<? super Vector> output) Draws multiple random samples from the distribution and puts the result into the given collection.
void	setNumTrials(int numTrials) Setter for numTrials
void	setParameters(Vector parameters) Setter for parameters

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_NUM_CLASSES

public static final int DEFAULT_NUM_CLASSES

Default number of classes (labels or parameters), 2.

See Also:

Constant Field Values

DEFAULT_NUM_TRIALS

public static final int DEFAULT_NUM_TRIALS

Default number of trials, 1.

See Also:

Constant Field Values

Constructor Detail

MultinomialDistribution

public MultinomialDistribution()

Creates a new instance of MultinomialDistribution

MultinomialDistribution

public MultinomialDistribution(int numClasses,
                               int numTrials)

Creates a new instance of MultinomialDistribution

Parameters:

numClasses - Number of classes (labels or parameters) to use.

numTrials - Number of trials in the distribution, must be greater than 0.

MultinomialDistribution

public MultinomialDistribution(Vector parameters,
                               int numTrials)

Creates a new instance of MultinomialDistribution

Parameters:

numTrials - Number of trials in the distribution, must be greater than 0.

parameters - Parameters of the multinomial distribution, must be at least 2-dimensional and each element must be nonnegative.

MultinomialDistribution

public MultinomialDistribution(MultinomialDistribution other)

Copy constructor

Parameters:

other - MultinomialDistribution to copy

Method Detail

clone

public MultinomialDistribution 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 AbstractCloneableSerializable

Returns:

A clone of this object.

getParameters

public Vector getParameters()

Getter for parameters

Returns:

Parameters of the multinomial distribution, must be at least 2-dimensional and each element must be nonnegative.

setParameters

public void setParameters(Vector parameters)

Setter for parameters

Parameters:

parameters - Parameters of the multinomial distribution, must be at least 2-dimensional and each element must be nonnegative.

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.

getNumTrials

public int getNumTrials()

Getter for numTrials

Returns:

Number of trials in the distribution, must be greater than 0.

setNumTrials

public void setNumTrials(int numTrials)

Setter for numTrials

Parameters:

numTrials - Number of trials in the distribution, must be greater than 0.

getMean

public Vector 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<Vector>

Returns:

Mean of the distribution.

sampleInto

public void sampleInto(java.util.Random random,
                       int sampleCount,
                       java.util.Collection<? super Vector> 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<Vector>

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.

getDomain

public MultinomialDistribution.Domain 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<Vector>

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<Vector>

Returns:

The size of the domain.

getProbabilityFunction

public MultinomialDistribution.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<Vector>

Specified by:

getProbabilityFunction in interface DiscreteDistribution<Vector>

Returns:

Distribution function associated with this Distribution.