Class WeibullDistr

java.lang.Object
org.apache.commons.math3.distribution.AbstractRealDistribution
org.apache.commons.math3.distribution.WeibullDistribution
org.cloudsimplus.distributions.WeibullDistr
All Implemented Interfaces:
Serializable, org.apache.commons.math3.distribution.RealDistribution, ContinuousDistribution, StatisticalDistribution
public class WeibullDistr extends org.apache.commons.math3.distribution.WeibullDistribution implements ContinuousDistribution
A Pseudo-Random Number Generator following the Weibull distribution.
Since:
CloudSim Toolkit 1.0
Author:
Marcos Dias de Assuncao, Manoel Campos da Silva Filho
See Also:

  • Field Summary

    Fields inherited from class org.apache.commons.math3.distribution.WeibullDistribution

    DEFAULT_INVERSE_ABSOLUTE_ACCURACY

    Fields inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution

    random, randomData, SOLVER_DEFAULT_ABSOLUTE_ACCURACY

    Fields inherited from interface org.cloudsimplus.distributions.ContinuousDistribution

    NULL

  • Constructor Summary

    Constructors
    Constructor
    Description
    WeibullDistr(double alpha, double beta)
    Creates a Weibull Pseudo-Random Number Generator (PRNG) using a given seed.
    WeibullDistr(double alpha, double beta, long seed)
    Creates a Weibull Pseudo-Random Number Generator (PRNG).
    WeibullDistr(double alpha, double beta, long seed, org.apache.commons.math3.random.RandomGenerator rng)
    Creates a Weibull Pseudo-Random Number Generator (PRNG).

  • Method Summary

    Modifier and Type
    Method
    Description
    long
    getSeed()
     
    boolean
    isApplyAntitheticVariates()
    Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique to reduce variance of experiments using the generated numbers.
    double
    originalSample()
    Generate a new pseudo random number directly from the RealDistribution.sample() method.
    void
    reseedRandomGenerator(long seed)
     
    WeibullDistr
    setApplyAntitheticVariates(boolean applyAntitheticVariates)
    Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique to reduce variance of experiments using the generated numbers.

    Methods inherited from class org.apache.commons.math3.distribution.WeibullDistribution

    calculateNumericalMean, calculateNumericalVariance, cumulativeProbability, density, getNumericalMean, getNumericalVariance, getScale, getShape, getSolverAbsoluteAccuracy, getSupportLowerBound, getSupportUpperBound, inverseCumulativeProbability, isSupportConnected, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, logDensity

    Methods inherited from class org.apache.commons.math3.distribution.AbstractRealDistribution

    cumulativeProbability, probability, probability, sample, sample

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface org.cloudsimplus.distributions.ContinuousDistribution

    sample

    Methods inherited from interface org.apache.commons.math3.distribution.RealDistribution

    cumulativeProbability, cumulativeProbability, density, getNumericalMean, getNumericalVariance, getSupportLowerBound, getSupportUpperBound, inverseCumulativeProbability, isSupportConnected, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, probability, sample

  • Constructor Details

    • WeibullDistr

      public WeibullDistr(double alpha, double beta)
      Creates a Weibull Pseudo-Random Number Generator (PRNG) using a given seed.

      Internally, it relies on the JDKRandomGenerator, a wrapper for the Random class that doesn't have high-quality randomness properties but is very fast.

      Parameters:
      alpha - the alpha distribution parameter
      beta - the beta distribution parameter
      See Also:

    • WeibullDistr

      public WeibullDistr(double alpha, double beta, long seed)
      Creates a Weibull Pseudo-Random Number Generator (PRNG).

      Internally, it relies on the JDKRandomGenerator, a wrapper for the Random class that doesn't have high-quality randomness properties but is very fast.

      Parameters:
      alpha - the alpha distribution parameter
      beta - the beta distribution parameter
      seed - the seed
      See Also:

    • WeibullDistr

      public WeibullDistr(double alpha, double beta, long seed, org.apache.commons.math3.random.RandomGenerator rng)
      Creates a Weibull Pseudo-Random Number Generator (PRNG).
      Parameters:
      alpha - the alpha distribution parameter
      beta - the beta distribution parameter
      seed - the seed already used to initialize the Pseudo-Random Number Generator
      rng - the actual Pseudo-Random Number Generator that will be the base to generate random numbers following a continuous distribution.

  • Method Details

    • reseedRandomGenerator

      public void reseedRandomGenerator(long seed)
      Specified by:
      reseedRandomGenerator in interface org.apache.commons.math3.distribution.RealDistribution
      Overrides:
      reseedRandomGenerator in class org.apache.commons.math3.distribution.AbstractRealDistribution

    • getSeed

      public long getSeed()
      Specified by:
      getSeed in interface StatisticalDistribution
      Returns:
      the seed used to initialize the generator

    • isApplyAntitheticVariates

      public boolean isApplyAntitheticVariates()
      Description copied from interface: StatisticalDistribution
      Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique to reduce variance of experiments using the generated numbers.

      This technique doesn't work for all the cases. However, in the cases it can be applied, in order for it to work, you have to perform some actions. Consider an experiment that has to run "n" times. The first half of these experiments has to use the seeds you want. However, the second half of the experiments have to set the applyAntitheticVariates attribute to true and use the seeds of the first half of experiments.

      Thus, the first half of experiments are run using PRNGs, returning random numbers as U(0, 1)[seed_1], ..., U(0, 1)[seed_n]. The second half of experiments then uses the seeds of the first half of experiments, returning random numbers as 1 - U(0, 1)[seed_1], ..., 1 - U(0, 1)[seed_n].
      Specified by:
      isApplyAntitheticVariates in interface StatisticalDistribution
      Returns:
      true if the technique is applied, false otherwise
      See Also:

    • setApplyAntitheticVariates

      public WeibullDistr setApplyAntitheticVariates(boolean applyAntitheticVariates)
      Description copied from interface: StatisticalDistribution
      Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique to reduce variance of experiments using the generated numbers.
      Specified by:
      setApplyAntitheticVariates in interface StatisticalDistribution
      Parameters:
      applyAntitheticVariates - true if the technique is to be applied, false otherwise
      See Also:

    • originalSample

      public double originalSample()
      Description copied from interface: StatisticalDistribution
      Generate a new pseudo random number directly from the RealDistribution.sample() method. This way, the Antithetic Variates Technique is ignored if enabled.

      Usually you shouldn't call this method but StatisticalDistribution.sample() instead.

      Specified by:
      originalSample in interface StatisticalDistribution
      Returns:
      the next pseudo random number in the sequence, following the implemented distribution, ignoring the Antithetic Variates Technique if enabled