org.apache.commons.math3.distribution
Class EnumeratedRealDistribution

java.lang.Object
  org.apache.commons.math3.distribution.AbstractRealDistribution
      org.apache.commons.math3.distribution.EnumeratedRealDistribution

All Implemented Interfaces:

Serializable, RealDistribution

public class EnumeratedRealDistribution
extends AbstractRealDistribution

Implementation of a real-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

Version:

$Id: EnumeratedRealDistribution.java 1456769 2013-03-15 04:51:34Z psteitz $

See Also:

Serialized Form

innerDistribution

protected final EnumeratedDistribution<Double> innerDistribution

EnumeratedDistribution (using the Double wrapper) used to generate the pmf.

EnumeratedRealDistribution

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

Create a discrete distribution using the given probability mass function enumeration.

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.

EnumeratedRealDistribution

public EnumeratedRealDistribution(RandomGenerator rng,
                                  double[] singletons,
                                  double[] probabilities)
                           throws DimensionMismatchException,
                                  NotPositiveException,
                                  MathArithmeticException,
                                  NotFiniteNumberException,
                                  NotANumberException

Create a discrete distribution using the given random number generator and probability mass function enumeration.

Parameters:

rng - random number generator.

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.

probability

public double probability(double 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.

Specified by:

probability in interface RealDistribution

Overrides:

probability in class AbstractRealDistribution

Parameters:

x - the point at which the PMF is evaluated

Returns:

zero.

density

public double density(double 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 point x

cumulativeProbability

public double cumulativeProbability(double 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 double 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 R | P(X <= x) > 0}.

Returns the lowest value with non-zero probability.

Returns:

the lowest value with non-zero probability.

getSupportUpperBound

public double 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.

isSupportLowerBoundInclusive

public boolean isSupportLowerBoundInclusive()

Whether or not the lower bound of support is in the domain of the density function. Returns true iff getSupporLowerBound() is finite and density(getSupportLowerBound()) returns a non-NaN, non-infinite value. The support of this distribution includes the lower bound.

Returns:

true

isSupportUpperBoundInclusive

public boolean isSupportUpperBoundInclusive()

Whether or not the upper bound of support is in the domain of the density function. Returns true iff getSupportUpperBound() is finite and density(getSupportUpperBound()) returns a non-NaN, non-infinite value. The support of this distribution includes the upper bound.

Returns:

true

isSupportConnected

public boolean isSupportConnected()

Use this method to get information about whether the support is connected, i.e. whether all values between the lower and upper bound of the support are included in the support. The support of this distribution is connected.

Returns:

true

sample

public double sample()

Generate a random value sampled from this distribution. The default implementation uses the inversion method.

Specified by:

sample in interface RealDistribution

Overrides:

sample in class AbstractRealDistribution

Returns:

a random value.