Package org.apache.commons.math3.distribution

Class AbstractIntegerDistribution

java.lang.Object
org.apache.commons.math3.distribution.AbstractIntegerDistribution
All Implemented Interfaces:
Serializable, IntegerDistribution
Direct Known Subclasses:
BinomialDistribution, EnumeratedIntegerDistribution, GeometricDistribution, HypergeometricDistribution, PascalDistribution, PoissonDistribution, UniformIntegerDistribution, ZipfDistribution
public abstract class AbstractIntegerDistribution extends Object implements IntegerDistribution, Serializable
Base class for integer-valued discrete distributions. Default implementations are provided for some of the methods that do not vary from distribution to distribution.
See Also:

  • Field Details

    • randomData

      @Deprecated protected final RandomDataImpl randomData
      Deprecated.
      As of 3.1, to be removed in 4.0. Please use the random instance variable instead.
      RandomData instance used to generate samples from the distribution.

    • random

      protected final RandomGenerator random
      RNG instance used to generate samples from the distribution.
      Since:
      3.1

  • Constructor Details

  • Method Details

    • cumulativeProbability

      public double cumulativeProbability(int x0, int x1) throws NumberIsTooLargeException
      For a random variable X whose values are distributed according to this distribution, this method returns P(x0 < X <= x1). The default implementation uses the identity

      P(x0 < X <= x1) = P(X <= x1) - P(X <= x0)

      Specified by:
      cumulativeProbability in interface IntegerDistribution
      Parameters:
      x0 - the exclusive lower bound
      x1 - the inclusive upper bound
      Returns:
      the probability that a random variable with this distribution will take a value between x0 and x1, excluding the lower and including the upper endpoint
      Throws:
      NumberIsTooLargeException - if x0 > x1

    • inverseCumulativeProbability

      public int 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 Z | P(Xinvalid input: '<'=x) >= p} for 0 < p <= 1,
      • inf{x in Z | P(Xinvalid input: '<'=x) > 0} for p = 0.
      If the result exceeds the range of the data type int, then Integer.MIN_VALUE or Integer.MAX_VALUE is returned. The default implementation returns
      Specified by:
      inverseCumulativeProbability in interface IntegerDistribution
      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

    • solveInverseCumulativeProbability

      protected int solveInverseCumulativeProbability(double p, int lower, int upper)
      This is a utility function used by inverseCumulativeProbability(double). It assumes 0 < p < 1 and that the inverse cumulative probability lies in the bracket (lower, upper]. The implementation does simple bisection to find the smallest p-quantile inf{x in Z | P(Xinvalid input: '<'=x) >= p}.
      Parameters:
      p - the cumulative probability
      lower - a value satisfying cumulativeProbability(lower) < p
      upper - a value satisfying p <= cumulativeProbability(upper)
      Returns:
      the smallest p-quantile of this distribution

    • reseedRandomGenerator

      public void reseedRandomGenerator(long seed)
      Reseed the random generator used to generate samples.
      Specified by:
      reseedRandomGenerator in interface IntegerDistribution
      Parameters:
      seed - the new seed

    • sample

      public int sample()
      Generate a random value sampled from this distribution. The default implementation uses the inversion method.
      Specified by:
      sample in interface IntegerDistribution
      Returns:
      a random value

    • sample

      public int[] sample(int sampleSize)
      Generate a random sample from the distribution. The default implementation generates the sample by calling sample() in a loop.
      Specified by:
      sample in interface IntegerDistribution
      Parameters:
      sampleSize - the number of random values to generate
      Returns:
      an array representing the random sample

    • logProbability

      public double logProbability(int x)
      For a random variable X whose values are distributed according to this distribution, this method returns log(P(X = x)), where log is the natural logarithm. In other words, this method represents the logarithm of the probability mass function (PMF) for the distribution. Note that due to the floating point precision and under/overflow issues, this method will for some distributions be more precise and faster than computing the logarithm of IntegerDistribution.probability(int).

      The default implementation simply computes the logarithm of probability(x).

      Parameters:
      x - the point at which the PMF is evaluated
      Returns:
      the logarithm of the value of the probability mass function at x