Package org.hipparchus.distribution.continuous

Class EnumeratedRealDistribution

java.lang.Object
org.hipparchus.distribution.continuous.AbstractRealDistribution
org.hipparchus.distribution.continuous.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.

See Also:

  • Constructor Details

    • 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

    • EnumeratedRealDistribution

      public EnumeratedRealDistribution(double[] singletons, double[] probabilities) throws MathIllegalArgumentException
      Create a discrete real-valued distribution using the given probability mass function enumeration.
      Parameters:
      singletons - array of random variable values.
      probabilities - array of probabilities.
      Throws:
      MathIllegalArgumentException - if singletons.length != probabilities.length
      MathIllegalArgumentException - if any of the probabilities are negative.
      MathIllegalArgumentException - if any of the probabilities are NaN.
      MathIllegalArgumentException - if any of the probabilities are infinite.

  • 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.

      Note that if x1 and x2 satisfy x1.equals(x2), or both are null, then probability(x1) = probability(x2).

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

    • 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 MathIllegalArgumentException
      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(X<=x) >= p} for 0 < p <= 1,
      • inf{x in R | P(X<=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:
      MathIllegalArgumentException - 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 <= 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.

    • 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

    • getPmf

      public List<Pair<Double,Double>> getPmf()
      Return the probability mass function as a list of (value, probability) pairs.
      Returns:
      the probability mass function.