org.apache.commons.math4.distribution

Class EnumeratedIntegerDistribution

• All Implemented Interfaces:
Serializable, IntegerDistribution

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
Serialized Form

• Nested classes/interfaces inherited from interface org.apache.commons.math4.distribution.IntegerDistribution

IntegerDistribution.Sampler
• Constructor Summary

Constructors
Constructor and 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

All Methods
Modifier and Type Method and Description
IntegerDistribution.Sampler createSampler(org.apache.commons.rng.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 getNumericalMean()
Use this method to get the numerical value of the mean of this distribution.
double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution.
int getSupportLowerBound()
Access the lower bound of the support.
int getSupportUpperBound()
Access the upper bound of the support.
boolean isSupportConnected()
Use this method to get information about 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
• Constructor Detail

• 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 Detail

• 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 - the 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 - the 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
• getNumericalMean

public double getNumericalMean()
Use this method to get the numerical value of the mean of this distribution.
Returns:
sum(singletons[i] * probabilities[i])
• getNumericalVariance

public double getNumericalVariance()
Use this method to get the numerical value of the variance of this distribution.
Returns:
sum((singletons[i] - mean) ^ 2 * probabilities[i])
• getSupportLowerBound

public int getSupportLowerBound()
Access the lower bound of the support. This method must return the same value as inverseCumulativeProbability(0). In other words, this method must return

inf {x in Z | P(X <= x) > 0}.

Returns the lowest value with non-zero probability.
Returns:
the lowest value with non-zero probability.
• getSupportUpperBound

public int getSupportUpperBound()
Access the upper bound of the support. This method must return the same value as inverseCumulativeProbability(1). In other words, this method must return

inf {x in R | P(X <= x) = 1}.

Returns the highest value with non-zero probability.
Returns:
the highest value with non-zero probability.
• isSupportConnected

public boolean isSupportConnected()
Use this method to get information about 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 IntegerDistribution.Sampler createSampler(org.apache.commons.rng.UniformRandomProvider rng)
Creates a sampler.
Specified by:
createSampler in interface IntegerDistribution
Overrides:
createSampler in class AbstractIntegerDistribution
Parameters:
rng - Generator of uniformly distributed numbers.
Returns:
a sampler that produces random numbers according this distribution.