Package 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
See Also:

  • Field Details

  • Constructor Details

    • EnumeratedRealDistribution

      public EnumeratedRealDistribution(double[] singletons, double[] probabilities) throws DimensionMismatchException, NotPositiveException, MathArithmeticException, NotFiniteNumberException, NotANumberException
      Create a discrete real-valued distribution using the given probability mass function enumeration.

      Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see sample() and AbstractRealDistribution.sample(int)). In case no sampling is needed for the created distribution, it is advised to pass null as random generator via the appropriate constructors to avoid the additional initialisation overhead.

      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 real-valued 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.

    • EnumeratedRealDistribution

      public EnumeratedRealDistribution(RandomGenerator rng, double[] data)
      Create a discrete real-valued distribution from the input data. Values are assigned mass based on their frequency.
      Parameters:
      rng - random number generator used for sampling
      data - input dataset
      Since:
      3.6

    • EnumeratedRealDistribution

      public EnumeratedRealDistribution(double[] data)
      Create a discrete real-valued distribution from the input data. Values are assigned mass based on their frequency. For example, [0,1,1,2] as input creates a distribution with values 0, 1 and 2 having probability masses 0.25, 0.5 and 0.25 respectively,
      Parameters:
      data - input dataset
      Since:
      3.6

  • Method Details

    • 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

    • inverseCumulativeProbability

      public double inverseCumulativeProbability(double p) throws OutOfRangeException
      Computes the quantile function of this distribution. For a random variable X distributed according to this distribution, the returned value is
      • inf{x in R | P(Xinvalid input: '<'=x) >= p} for 0 < p <= 1,
      • inf{x in R | P(Xinvalid input: '<'=x) > 0} for p = 0.
      The default implementation returns
      Specified by:
      inverseCumulativeProbability in interface RealDistribution
      Overrides:
      inverseCumulativeProbability in class AbstractRealDistribution
      Parameters:
      p - the cumulative probability
      Returns:
      the smallest p-quantile of this distribution (largest 0-quantile for p = 0)
      Throws:
      OutOfRangeException - if p < 0 or p > 1

    • 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 invalid input: '<'= 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 invalid input: '<'= 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.