Package org.apache.commons.math4.distribution

Class EnumeratedIntegerDistribution

java.lang.Object

org.apache.commons.math4.distribution.AbstractIntegerDistribution

org.apache.commons.math4.distribution.EnumeratedIntegerDistribution

All Implemented Interfaces:

java.io.Serializable, IntegerDistribution, DiscreteDistribution

public class EnumeratedIntegerDistribution
extends AbstractIntegerDistribution

Implementation of an integer-valued EnumeratedDistribution.

Values with zero-probability are allowed but they do not extend the support.
Duplicate values are allowed. Probabilities of duplicate values are combined when computing cumulative probabilities and statistics.

Since:

3.2

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from interface org.apache.commons.statistics.distribution.DiscreteDistribution

DiscreteDistribution.Sampler

Field Summary

Fields

Modifier and Type	Field	Description
protected EnumeratedDistribution<java.lang.Integer>	innerDistribution	EnumeratedDistribution instance (using the Integer wrapper) used to generate the pmf.

Constructor Summary

Constructors

Constructor	Description
EnumeratedIntegerDistribution(int[] data)	Create a discrete integer-valued distribution from the input data.
EnumeratedIntegerDistribution(int[] singletons, double[] probabilities)	Create a discrete distribution.

Method Summary

Modifier and Type	Method	Description
DiscreteDistribution.Sampler	createSampler(UniformRandomProvider rng)	Creates a sampler.
double	cumulativeProbability(int x)	For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x).
double	getMean()	Gets the mean of this distribution.
int	getSupportLowerBound()	Gets the lower bound of the support.
int	getSupportUpperBound()	Gets the upper bound of the support.
double	getVariance()	Gets the variance of this distribution.
boolean	isSupportConnected()	Indicates whether the support is connected, i.e.
double	probability(int x)	For a random variable X whose values are distributed according to this distribution, this method returns P(X = x).

Methods inherited from class org.apache.commons.math4.distribution.AbstractIntegerDistribution

inverseCumulativeProbability, logProbability, probability, sample, solveInverseCumulativeProbability

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

innerDistribution

protected final EnumeratedDistribution<java.lang.Integer> innerDistribution

EnumeratedDistribution instance (using the Integer wrapper) used to generate the pmf.

Constructor Details

EnumeratedIntegerDistribution

public EnumeratedIntegerDistribution(int[] singletons, double[] probabilities) throws DimensionMismatchException, NotPositiveException, MathArithmeticException, NotFiniteNumberException, NotANumberException

Create a discrete distribution.

Parameters:

singletons - array of random variable values.

probabilities - array of probabilities.

Throws:

DimensionMismatchException - if singletons.length != probabilities.length

NotPositiveException - if any of the probabilities are negative.

NotFiniteNumberException - if any of the probabilities are infinite.

NotANumberException - if any of the probabilities are NaN.

MathArithmeticException - all of the probabilities are 0.

EnumeratedIntegerDistribution

public EnumeratedIntegerDistribution(int[] data)

Create a discrete integer-valued distribution from the input data. Values are assigned mass based on their frequency.

Parameters:

data - input dataset

Method Details

probability

public double probability(int x)

For a random variable X whose values are distributed according to this distribution, this method returns P(X = x). In other words, this method represents the probability mass function (PMF) for the distribution.

Parameters:

x - Point at which the PMF is evaluated.

Returns:

the value of the probability mass function at x.

cumulativeProbability

public double cumulativeProbability(int x)

For a random variable X whose values are distributed according to this distribution, this method returns P(X <= x). In other, words, this method represents the (cumulative) distribution function (CDF) for this distribution.

Parameters:

x - Point at which the CDF is evaluated.

Returns:

the probability that a random variable with this distribution takes a value less than or equal to x.

getMean

public double getMean()

Gets the mean of this distribution.

Returns:

sum(singletons[i] * probabilities[i])

getVariance

public double getVariance()

Gets the variance of this distribution.

Returns:

sum((singletons[i] - mean) ^ 2 * probabilities[i])

getSupportLowerBound

public int getSupportLowerBound()

Gets the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0), i.e. inf {x in Z | P(X <= x) > 0}. By convention, Integer.MIN_VALUE should be substituted for negative infinity. Returns the lowest value with non-zero probability.

Returns:

the lowest value with non-zero probability.

getSupportUpperBound

public int getSupportUpperBound()

Gets the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1), i.e. inf {x in R | P(X <= x) = 1}. By convention, Integer.MAX_VALUE should be substituted for positive infinity. Returns the highest value with non-zero probability.

Returns:

the highest value with non-zero probability.

isSupportConnected

public boolean isSupportConnected()

Indicates whether the support is connected, i.e. whether all integers between the lower and upper bound of the support are included in the support. The support of this distribution is connected.

Returns:

true

createSampler

public DiscreteDistribution.Sampler createSampler(UniformRandomProvider rng)

Creates a sampler.

Specified by:

createSampler in interface DiscreteDistribution

Overrides:

createSampler in class AbstractIntegerDistribution

Parameters:

rng - Generator of uniformly distributed numbers.

Returns:

a sampler that produces random numbers according this distribution.