Package org.cloudbus.cloudsim.distributions

Class NormalDistr

java.lang.Object
org.apache.commons.math3.distribution.AbstractRealDistribution
org.apache.commons.math3.distribution.NormalDistribution
org.cloudbus.cloudsim.distributions.NormalDistr
All Implemented Interfaces:
Serializable, org.apache.commons.math3.distribution.RealDistribution, ContinuousDistribution, StatisticalDistribution
public class NormalDistr
extends org.apache.commons.math3.distribution.NormalDistribution
implements ContinuousDistribution
A Pseudo-Random Number Generator following the Normal (Gaussian) distribution.
Author:
Manoel Campos da Silva Filho
See Also:
Serialized Form

  • Field Summary

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

    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.cloudbus.cloudsim.distributions.ContinuousDistribution

    NULL

  • Constructor Summary

    Constructors
    Constructor Description
    NormalDistr​(double mean, double standardDeviation)
    Creates a Normal (Gaussian) Pseudo-Random Number Generator (RNG) using the current time as seed.
    NormalDistr​(double mean, double standardDeviation, long seed)
    Creates a Normal (Gaussian) Pseudo-Random Number Generator (RNG).
    NormalDistr​(double mean, double standardDeviation, long seed, org.apache.commons.math3.random.RandomGenerator rng)
    Creates a Normal (Gaussian) Pseudo-Random Number Generator (RNG).

  • Method Summary

    Modifier and Type Method Description
    long getSeed()
    Gets the seed used to initialize the generator
    boolean isApplyAntitheticVariates()
    Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique in order 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)  
    NormalDistr setApplyAntitheticVariates​(boolean applyAntitheticVariates)
    Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique in order to reduce variance of experiments using the generated numbers.

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

    cumulativeProbability, cumulativeProbability, density, getMean, getNumericalMean, getNumericalVariance, getSolverAbsoluteAccuracy, getStandardDeviation, getSupportLowerBound, getSupportUpperBound, inverseCumulativeProbability, isSupportConnected, isSupportLowerBoundInclusive, isSupportUpperBoundInclusive, logDensity, probability, sample

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

    probability, sample

    Methods inherited from class java.lang.Object

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

    Methods inherited from interface org.cloudbus.cloudsim.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

    • NormalDistr

      public NormalDistr​(double mean, double standardDeviation)
      Creates a Normal (Gaussian) Pseudo-Random Number Generator (RNG) using the current time as 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:
      mean - the mean for the distribution.
      standardDeviation - the standard deviation for the distribution.
      See Also:
      NormalDistr(double, double, long, RandomGenerator)

    • NormalDistr

      public NormalDistr​(double mean, double standardDeviation, long seed)
      Creates a Normal (Gaussian) Pseudo-Random Number Generator (RNG).

      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:
      mean - the mean for the distribution.
      standardDeviation - the standard deviation for the distribution.
      seed - the seed to be used.
      See Also:
      NormalDistr(double, double, long, RandomGenerator)

    • NormalDistr

      public NormalDistr​(double mean, double standardDeviation, long seed, org.apache.commons.math3.random.RandomGenerator rng)
      Creates a Normal (Gaussian) Pseudo-Random Number Generator (RNG).
      Parameters:
      mean - the mean for the distribution.
      standardDeviation - the standard deviation for the distribution.
      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

    • getSeed

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

    • 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

    • isApplyAntitheticVariates

      public boolean isApplyAntitheticVariates()
      Description copied from interface: StatisticalDistribution
      Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique in order 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 to it work, one 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 the developer 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 that return 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:
      StatisticalDistribution.setApplyAntitheticVariates(boolean)

    • setApplyAntitheticVariates

      public NormalDistr setApplyAntitheticVariates​(boolean applyAntitheticVariates)
      Description copied from interface: StatisticalDistribution
      Indicates if the Pseudo-Random Number Generator (RNG) applies the Antithetic Variates Technique in order 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:
      StatisticalDistribution.isApplyAntitheticVariates()

    • 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