Class CauchyDistribution

java.lang.Object
org.apache.commons.math3.distribution.AbstractRealDistribution
org.apache.commons.math3.distribution.CauchyDistribution
All Implemented Interfaces:
Serializable, RealDistribution
public class CauchyDistribution extends AbstractRealDistribution
Implementation of the Cauchy distribution.
Since:
1.1 (changed to concrete class in 3.0)
See Also:

  • Field Details

  • Constructor Details

    • CauchyDistribution

      public CauchyDistribution()
      Creates a Cauchy distribution with the median equal to zero and scale equal to one.

    • CauchyDistribution

      public CauchyDistribution(double median, double scale)
      Creates a Cauchy distribution using the given median and scale.

      Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see AbstractRealDistribution.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:
      median - Median for this distribution.
      scale - Scale parameter for this distribution.

    • CauchyDistribution

      public CauchyDistribution(double median, double scale, double inverseCumAccuracy)
      Creates a Cauchy distribution using the given median and scale.

      Note: this constructor will implicitly create an instance of Well19937c as random generator to be used for sampling only (see AbstractRealDistribution.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:
      median - Median for this distribution.
      scale - Scale parameter for this distribution.
      inverseCumAccuracy - Maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY).
      Throws:
      NotStrictlyPositiveException - if scale <= 0.
      Since:
      2.1

    • CauchyDistribution

      public CauchyDistribution(RandomGenerator rng, double median, double scale)
      Creates a Cauchy distribution.
      Parameters:
      rng - Random number generator.
      median - Median for this distribution.
      scale - Scale parameter for this distribution.
      Throws:
      NotStrictlyPositiveException - if scale <= 0.
      Since:
      3.3

    • CauchyDistribution

      public CauchyDistribution(RandomGenerator rng, double median, double scale, double inverseCumAccuracy)
      Creates a Cauchy distribution.
      Parameters:
      rng - Random number generator.
      median - Median for this distribution.
      scale - Scale parameter for this distribution.
      inverseCumAccuracy - Maximum absolute error in inverse cumulative probability estimates (defaults to DEFAULT_INVERSE_ABSOLUTE_ACCURACY).
      Throws:
      NotStrictlyPositiveException - if scale <= 0.
      Since:
      3.1

  • Method Details

    • 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

    • getMedian

      public double getMedian()
      Access the median.
      Returns:
      the median for this distribution.

    • getScale

      public double getScale()
      Access the scale parameter.
      Returns:
      the scale parameter for this distribution.

    • density

      public double density(double x)
      Returns the probability density function (PDF) of this distribution evaluated at the specified point x. In general, the PDF is the derivative of the CDF. If the derivative does not exist at x, then an appropriate replacement should be returned, e.g. Double.POSITIVE_INFINITY, Double.NaN, or the limit inferior or limit superior of the difference quotient.
      Parameters:
      x - the point at which the PDF is evaluated
      Returns:
      the value of the probability density function at point 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 Returns Double.NEGATIVE_INFINITY when p == 0 and Double.POSITIVE_INFINITY when p == 1.
      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

    • getSolverAbsoluteAccuracy

      protected double getSolverAbsoluteAccuracy()
      Returns the solver absolute accuracy for inverse cumulative computation. You can override this method in order to use a Brent solver with an absolute accuracy different from the default.
      Overrides:
      getSolverAbsoluteAccuracy in class AbstractRealDistribution
      Returns:
      the maximum absolute error in inverse cumulative probability estimates

    • getNumericalMean

      public double getNumericalMean()
      Use this method to get the numerical value of the mean of this distribution. The mean is always undefined no matter the parameters.
      Returns:
      mean (always Double.NaN)

    • getNumericalVariance

      public double getNumericalVariance()
      Use this method to get the numerical value of the variance of this distribution. The variance is always undefined no matter the parameters.
      Returns:
      variance (always Double.NaN)

    • 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}.

      The lower bound of the support is always negative infinity no matter the parameters.
      Returns:
      lower bound of the support (always Double.NEGATIVE_INFINITY)

    • 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}.

      The upper bound of the support is always positive infinity no matter the parameters.
      Returns:
      upper bound of the support (always Double.POSITIVE_INFINITY)

    • 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.
      Returns:
      true if the lower bound of support is finite and the density function returns a non-NaN, non-infinite value there

    • 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.
      Returns:
      true if the upper bound of support is finite and the density function returns a non-NaN, non-infinite value there

    • 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