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/*
* CloudSim Plus: A modern, highly-extensible and easier-to-use Framework for
* Modeling and Simulation of Cloud Computing Infrastructures and Services.
* http://cloudsimplus.org
*
* Copyright (C) 2015-2021 Universidade da Beira Interior (UBI, Portugal) and
* the Instituto Federal de Educação Ciência e Tecnologia do Tocantins (IFTO, Brazil).
*
* This file is part of CloudSim Plus.
*
* CloudSim Plus is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* CloudSim Plus is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with CloudSim Plus. If not, see Usually you shouldn't call this method but {@link #sample()}
* instead.
*
* @return the next pseudo random number in the sequence, following the
* implemented distribution, ignoring the
* {@link #isApplyAntitheticVariates() Antithetic Variates Technique}
* if enabled
*/
double originalSample();
/**
* Generate a new pseudo random number.
* If the {@link #isApplyAntitheticVariates() Antithetic Variates Technique} is enabled,
* the returned value is manipulated to try reducing variance or generated random numbers.
* Check the provided link for details.
*
* @return the next pseudo random number in the sequence, following the
* implemented distribution.
*/
default double sample() {
return isApplyAntitheticVariates() ? 1 - originalSample() : originalSample();
}
/**
* Gets the seed used to initialize the generator
* @return
*/
long getSeed();
/**
* Instantiates a {@link Well19937c} as the default
* {@link RandomGenerator Pseudo-Random Number Generator}
* (PRNG) used by {@code ContinuousDistribution}.
*
* {@link Well19937c} is the PRNG used by {@link RealDistribution}
* implementations of the {@link org.apache.commons.math3}.
* Classes in such a library are used internally by
* {@code ContinuousDistribution} implementations to provide
* PRNGs following some statistical distributions.
*
*
*
* Despite the classes from {@link org.apache.commons.math3}
* use the same {@link RandomGenerator} defined here,
* providing a {@link RandomGenerator} when instantiate a {@code ContinuousDistribution}
* allow the researcher to define any PRNG by calling the appropriate
* {@code ContinuousDistribution} constructor.
* For instance, the {@link UniformDistr#UniformDistr(long, RandomGenerator)}
* constructor enables providing a different PRNG, while
* the {@link UniformDistr#UniformDistr(long)} uses the PRNG instantiated here.
*
*
* By calling a constructor that accepts a {@link RandomGenerator},
* the researcher may provide a different PRNG with either higher performance
* or better statistical properties
* (it's difficult to have both properties on the same PRNG).
*
* @param seed the seed to set
*/
static RandomGenerator newDefaultGen(final long seed){
return new Well19937c(seed);
}
static long defaultSeed(){
return System.nanoTime();
}
/**
* 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].
*
* @return true if the technique is applied, false otherwise
* @see #setApplyAntitheticVariates(boolean)
*/
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.
*
* @param applyAntitheticVariates true if the technique is to be applied, false otherwise
* @see #isApplyAntitheticVariates()
*/
StatisticalDistribution setApplyAntitheticVariates(boolean applyAntitheticVariates);
}