677 lines
23 KiB
C++
677 lines
23 KiB
C++
/* -*- Mode:C++; c-file-style:"gnu"; indent-tabs-mode:nil; -*- */
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//
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// Copyright (c) 2006 Georgia Tech Research Corporation
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//
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// This program is free software; you can redistribute it and/or modify
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// it under the terms of the GNU General Public License version 2 as
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// published by the Free Software Foundation;
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//
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// This program is distributed in the hope that it will be useful,
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// but WITHOUT ANY WARRANTY; without even the implied warranty of
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// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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// GNU General Public License for more details.
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//
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// You should have received a copy of the GNU General Public License
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// along with this program; if not, write to the Free Software
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// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
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//
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// Author: Rajib Bhattacharjea<raj.b@gatech.edu>
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//
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#ifndef __random_variable_h__
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#define __random_variable_h__
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#include <vector>
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#include <algorithm>
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#include <stdint.h>
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#include <istream>
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#include <ostream>
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#include "attribute.h"
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#include "attribute-helper.h"
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/**
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* \ingroup core
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* \defgroup randomvariable Random Variable Distributions
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*
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*/
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namespace ns3{
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class RandomVariableBase;
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/**
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* \brief The basic RNG for NS-3.
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* \ingroup randomvariable
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*
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* Note: The underlying random number generation method used
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* by NS-3 is the RngStream code by Pierre L'Ecuyer at
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* the University of Montreal.
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*
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* NS-3 has a rich set of random number generators.
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* Class RandomVariable defines the base class functionalty
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* required for all random number generators. By default, the underlying
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* generator is seeded with the time of day, and then deterministically
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* creates a sequence of seeds for each subsequent generator that is created.
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* The rest of the documentation outlines how to change this behavior.
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*/
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class RandomVariable
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{
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public:
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RandomVariable();
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RandomVariable(const RandomVariable&o);
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RandomVariable &operator = (const RandomVariable &o);
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~RandomVariable();
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/**
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* \brief Returns a random double from the underlying distribution
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* \return A floating point random value
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*/
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double GetValue (void) const;
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/**
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* \brief Returns a random integer integer from the underlying distribution
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* \return Integer cast of ::GetValue()
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*/
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uint32_t GetInteger (void) const;
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/**
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* \brief Get the internal state of the RNG
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*
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* This function is for power users who understand the inner workings
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* of the underlying RngStream method used. It returns the internal
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* state of the RNG via the input parameter.
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* \param seed Output parameter; gets overwritten with the internal state of
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* of the RNG.
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*/
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void GetSeed(uint32_t seed[6]) const;
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/**
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* \brief Set seeding behavior
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*
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* Specify whether the POSIX device /dev/random is to
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* be used for seeding. When this is used, the underlying
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* generator is seeded with data from /dev/random instead of
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* being seeded based upon the time of day. For this to be effective,
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* it must be called before the creation of the first instance of a
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* RandomVariable or subclass. Example:
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* \code
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* RandomVariable::UseDevRandom();
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* UniformVariable x(2,3); //these are seeded randomly
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* ExponentialVariable y(120); //etc
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* \endcode
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* \param udr True if /dev/random desired.
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*/
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static void UseDevRandom(bool udr = true);
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/**
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* \brief Use the global seed to force precisely reproducible results.
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*
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* It is often desirable to create a simulation that uses random
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* numbers, while at the same time is completely reproducible.
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* Specifying this set of six random seeds initializes the
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* random number generator with the specified seed.
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* Once this is set, all generators will produce fixed output
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* from run to run. This is because each time a new generator is created,
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* the underlying RngStream deterministically creates a new seed based upon
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* the old one, hence a "stream" of RNGs. Example:
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* \code
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* RandomVariable::UseGlobalSeed(...);
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* UniformVariable x(2,3); //these will give the same output everytime
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* ExponentialVariable y(120); //as long as the seed stays the same
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* \endcode
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* \param s0
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* \param s1
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* \param s2
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* \param s3
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* \param s4
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* \param s5
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* \return True if seed is valid.
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*/
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static void UseGlobalSeed(uint32_t s0, uint32_t s1, uint32_t s2,
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uint32_t s3, uint32_t s4, uint32_t s5);
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/**
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* \brief Set the run number of this simulation
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*
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* These RNGs have the ability to give independent sets of trials for a fixed
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* global seed. For example, suppose one sets up a simulation with
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* RandomVariables with a given global seed. Suppose the user wanted to
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* retry the same simulation with different random values for validity,
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* statistical rigor, etc. The user could either change the global seed and
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* re-run the simulation, or could use this facility to increment all of the
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* RNGs to a next substream state. This predictably advances the internal
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* state of all RandomVariables n steps. This should be called immediately
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* after the global seed is set, and before the creation of any
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* RandomVariables. For example:
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* \code
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* RandomVariable::UseGlobalSeed(1,2,3,4,5,6);
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* int N = atol(argv[1]); //read in run number from command line
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* RandomVariable::SetRunNumber(N);
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* UniformVariable x(0,10);
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* ExponentialVariable y(2902);
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* \endcode
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* In this example, N could successivly be equal to 1,2,3, etc. and the user
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* would continue to get independent runs out of the single simulation. For
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* this simple example, the following might work:
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* \code
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* ./simulation 0
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* ...Results for run 0:...
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*
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* ./simulation 1
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* ...Results for run 1:...
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* \endcode
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*/
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static void SetRunNumber(uint32_t n);
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RandomVariable (Attribute value);
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operator Attribute () const;
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private:
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friend std::ostream &operator << (std::ostream &os, const RandomVariable &var);
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friend std::istream &operator >> (std::istream &os, RandomVariable &var);
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RandomVariableBase *m_variable;
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protected:
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RandomVariable (const RandomVariableBase &variable);
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RandomVariableBase *Peek (void) const;
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};
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/**
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* \brief The uniform distribution RNG for NS-3.
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed uniform distribution. It also supports the generation of
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* single random numbers from various uniform distributions.
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*
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* The low end of the range is always included and the high end
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* of the range is always excluded.
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* \code
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* UniformVariable x(0,10);
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* x.GetValue(); //will always return numbers [0,10)
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* UniformVariable::GetSingleValue(100,1000); //returns a value [100,1000)
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* \endcode
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*/
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class UniformVariable : public RandomVariable
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{
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public:
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/**
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* Creates a uniform random number generator in the
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* range [0.0 .. 1.0).
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*/
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UniformVariable();
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/**
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* Creates a uniform random number generator with the specified range
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* \param s Low end of the range
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* \param l High end of the range
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*/
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UniformVariable(double s, double l);
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public:
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/**
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* \param s Low end of the range
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* \param l High end of the range
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* \return A uniformly distributed random number between s and l
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*/
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static double GetSingleValue(double s, double l);
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};
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/**
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* \brief A random variable that returns a constant
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* \ingroup randomvariable
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*
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* Class ConstantVariable defines a random number generator that
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* returns the same value every sample.
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*/
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class ConstantVariable : public RandomVariable {
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public:
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/**
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* Construct a ConstantVariable RNG that returns zero every sample
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*/
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ConstantVariable();
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/**
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* Construct a ConstantVariable RNG that returns the specified value
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* every sample.
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* \param c Unchanging value for this RNG.
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*/
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ConstantVariable(double c);
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/**
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* \brief Specify a new constant RNG for this generator.
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* \param c New constant value for this RNG.
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*/
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void SetConstant(double c);
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};
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/**
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* \brief Return a sequential list of values
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* \ingroup randomvariable
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*
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* Class SequentialVariable defines a random number generator that
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* returns a sequential sequence. The sequence monotonically
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* increases for a period, then wraps around to the low value
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* and begins monotonicaly increasing again.
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*/
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class SequentialVariable : public RandomVariable
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{
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public:
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/**
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* \brief Constructor for the SequentialVariable RNG.
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*
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* The four parameters define the sequence. For example
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* SequentialVariable(0,5,1,2) creates a RNG that has the sequence
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* 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 0, 0 ...
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* \param f First value of the sequence.
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* \param l One more than the last value of the sequence.
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* \param i Increment between sequence values
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* \param c Number of times each member of the sequence is repeated
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*/
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SequentialVariable(double f, double l, double i = 1, uint32_t c = 1);
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/**
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* \brief Constructor for the SequentialVariable RNG.
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*
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* Differs from the first only in that the increment parameter is a
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* random variable
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* \param f First value of the sequence.
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* \param l One more than the last value of the sequence.
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* \param i Reference to a RandomVariable for the sequence increment
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* \param c Number of times each member of the sequence is repeated
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*/
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SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c = 1);
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};
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/**
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* \brief Exponentially Distributed random var
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed exponential distribution. It also supports the generation of
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* single random numbers from various exponential distributions.
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*
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* The probability density function of an exponential variable
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* is defined over the interval [0, +inf) as:
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* \f$ \alpha e^{-\alpha x} \f$
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* where \f$ \alpha = \frac{1}{mean} \f$
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*
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* The bounded version is defined over the internal [0,+inf) as:
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* \f$ \left\{ \begin{array}{cl} \alpha e^{-\alpha x} & x < bound \\ bound & x > bound \end{array}\right. \f$
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*
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* \code
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* ExponentialVariable x(3.14);
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* x.GetValue(); //will always return with mean 3.14
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* ExponentialVariable::GetSingleValue(20.1); //returns with mean 20.1
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* ExponentialVariable::GetSingleValue(108); //returns with mean 108
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* \endcode
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*
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*/
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class ExponentialVariable : public RandomVariable
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{
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public:
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/**
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* Constructs an exponential random variable with a mean
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* value of 1.0.
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*/
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ExponentialVariable();
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/**
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* \brief Constructs an exponential random variable with a specified mean
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* \param m Mean value for the random variable
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*/
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explicit ExponentialVariable(double m);
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/**
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* \brief Constructs an exponential random variable with spefified
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* \brief mean and upper limit.
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*
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* Since exponential distributions can theoretically return unbounded values,
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* it is sometimes useful to specify a fixed upper limit. Note however when
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* the upper limit is specified, the true mean of the distribution is
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* slightly smaller than the mean value specified.
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* \param m Mean value of the random variable
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* \param b Upper bound on returned values
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*/
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ExponentialVariable(double m, double b);
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/**
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* \param m The mean of the distribution from which the return value is drawn
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* \param b The upper bound value desired, beyond which values get clipped
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* \return A random number from an exponential distribution with mean m
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*/
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static double GetSingleValue(double m, double b=0);
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};
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/**
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* \brief ParetoVariable distributed random var
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed pareto distribution. It also supports the generation of
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* single random numbers from various pareto distributions.
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*
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* The probability density function is defined over the range [\f$x_m\f$,+inf) as:
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* \f$ k \frac{x_m^k}{x^{k+1}}\f$ where \f$x_m > 0\f$ is called the location
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* parameter and \f$ k > 0\f$ is called the pareto index or shape.
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*
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* The parameter \f$ x_m \f$ can be infered from the mean and the parameter \f$ k \f$
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* with the equation \f$ x_m = mean \frac{k-1}{k}, k > 1\f$.
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*
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* \code
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* ParetoVariable x(3.14);
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* x.GetValue(); //will always return with mean 3.14
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* ParetoVariable::GetSingleValue(20.1); //returns with mean 20.1
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* ParetoVariable::GetSingleValue(108); //returns with mean 108
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* \endcode
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*/
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class ParetoVariable : public RandomVariable
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{
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public:
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/**
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* Constructs a pareto random variable with a mean of 1 and a shape
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* parameter of 1.5
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*/
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ParetoVariable ();
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/**
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* Constructs a pareto random variable with specified mean and shape
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* parameter of 1.5
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* \param m Mean value of the distribution
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*/
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explicit ParetoVariable(double m);
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/**
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* Constructs a pareto random variable with the specified mean value and
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* shape parameter.
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* \param m Mean value of the distribution
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* \param s Shape parameter for the distribution
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*/
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ParetoVariable(double m, double s);
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/**
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* \brief Constructs a pareto random variable with the specified mean
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* \brief value, shape (alpha), and upper bound.
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*
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* Since pareto distributions can theoretically return unbounded values,
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* it is sometimes useful to specify a fixed upper limit. Note however
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* when the upper limit is specified, the true mean of the distribution
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* is slightly smaller than the mean value specified.
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* \param m Mean value
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* \param s Shape parameter
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* \param b Upper limit on returned values
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*/
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ParetoVariable(double m, double s, double b);
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/**
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* \param m The mean value of the distribution from which the return value
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* is drawn.
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* \param s The shape parameter of the distribution from which the return
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* value is drawn.
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* \param b The upper bound to which to restrict return values
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* \return A random number from a Pareto distribution with mean m and shape
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* parameter s.
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*/
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static double GetSingleValue(double m, double s, double b=0);
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};
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/**
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* \brief WeibullVariable distributed random var
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed weibull distribution. It also supports the generation of
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* single random numbers from various weibull distributions.
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*
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* The probability density function is defined over the interval [0, +inf]
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* as: \f$ \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k} \f$
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* where \f$ k > 0\f$ is the shape parameter and \f$ \lambda > 0\f$ is the scale parameter. The
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* specified mean is related to the scale and shape parameters by the following relation:
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* \f$ mean = \lambda\Gamma\left(1+\frac{1}{k}\right) \f$ where \f$ \Gamma \f$ is the Gamma function.
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*/
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class WeibullVariable : public RandomVariable {
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public:
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/**
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* Constructs a weibull random variable with a mean
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* value of 1.0 and a shape (alpha) parameter of 1
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*/
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WeibullVariable();
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/**
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* Constructs a weibull random variable with the specified mean
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* value and a shape (alpha) parameter of 1.5.
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* \param m mean value of the distribution
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*/
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WeibullVariable(double m) ;
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/**
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* Constructs a weibull random variable with the specified mean
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* value and a shape (alpha).
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* \param m Mean value for the distribution.
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* \param s Shape (alpha) parameter for the distribution.
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*/
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WeibullVariable(double m, double s);
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/**
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* \brief Constructs a weibull random variable with the specified mean
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* \brief value, shape (alpha), and upper bound.
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* Since WeibullVariable distributions can theoretically return unbounded values,
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* it is sometimes usefull to specify a fixed upper limit. Note however
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* that when the upper limit is specified, the true mean of the distribution
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* is slightly smaller than the mean value specified.
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* \param m Mean value for the distribution.
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* \param s Shape (alpha) parameter for the distribution.
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* \param b Upper limit on returned values
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*/
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WeibullVariable(double m, double s, double b);
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/**
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* \param m Mean value for the distribution.
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* \param s Shape (alpha) parameter for the distribution.
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* \param b Upper limit on returned values
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* \return Random number from a distribution specified by m,s, and b
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*/
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static double GetSingleValue(double m, double s, double b=0);
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};
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/**
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* \brief Class NormalVariable defines a random variable with a
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* normal (Gaussian) distribution.
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* \ingroup randomvariable
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*
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* This class supports the creation of objects that return random numbers
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* from a fixed normal distribution. It also supports the generation of
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* single random numbers from various normal distributions.
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*
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* The density probability function is defined over the interval (-inf,+inf)
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* as: \f$ \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{s\sigma^2}}\f$
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* where \f$ mean = \mu \f$ and \f$ variance = \sigma^2 \f$
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*
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*/
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class NormalVariable : public RandomVariable
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{
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public:
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static const double INFINITE_VALUE;
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/**
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* Constructs an normal random variable with a mean
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* value of 0 and variance of 1.
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*/
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NormalVariable();
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/**
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* \brief Construct a normal random variable with specified mean and variance
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* \param m Mean value
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* \param v Variance
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* \param b Bound. The NormalVariable is bounded within +-bound.
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*/
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NormalVariable(double m, double v, double b = INFINITE_VALUE);
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/**
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* \param m Mean value
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* \param v Variance
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* \param b Bound. The NormalVariable is bounded within +-bound.
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* \return A random number from a distribution specified by m,v, and b.
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*/
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static double GetSingleValue(double m, double v, double b = INFINITE_VALUE);
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};
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/**
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* \brief EmpiricalVariable distribution random var
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* \ingroup randomvariable
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*
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* Defines a random variable that has a specified, empirical
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* distribution. The distribution is specified by a
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* series of calls to the CDF member function, specifying a
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* value and the probability that the function value is less than
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* the specified value. When values are requested,
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* a uniform random variable is used to select a probabililty,
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* and the return value is interpreted linerarly between the
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* two appropriate points in the CDF. The method is known
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* as inverse transform sampling:
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* (http://en.wikipedia.org/wiki/Inverse_transform_sampling).
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*/
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class EmpiricalVariable : public RandomVariable {
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public:
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/**
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* Constructor for the EmpiricalVariable random variables.
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*/
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explicit EmpiricalVariable();
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/**
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* \brief Specifies a point in the empirical distribution
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* \param v The function value for this point
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* \param c Probability that the function is less than or equal to v
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*/
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void CDF(double v, double c); // Value, prob <= Value
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protected:
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EmpiricalVariable (const RandomVariableBase &variable);
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};
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/**
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* \brief Integer-based empirical distribution
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* \ingroup randomvariable
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*
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* Defines an empirical distribution where all values are integers.
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* Indentical to EmpiricalVariable, except that the inverse transform
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* sampling interpolation described in the EmpiricalVariable documentation
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* is modified to only return integers.
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*/
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class IntEmpiricalVariable : public EmpiricalVariable
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{
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public:
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IntEmpiricalVariable();
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};
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/**
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* \brief a non-random variable
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* \ingroup randomvariable
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*
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* Defines a random variable that has a specified, predetermined
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* sequence. This would be useful when trying to force
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* the RNG to return a known sequence, perhaps to
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* compare NS-3 to some other simulator
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*/
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class DeterministicVariable : public RandomVariable
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{
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public:
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/**
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* \brief Constructor
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*
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* Creates a generator that returns successive elements of the d array
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* on successive calls to ::Value(). Note that the d pointer is copied
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* for use by the generator (shallow-copy), not its contents, so the
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* contents of the array d points to have to remain unchanged for the use
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* of DeterministicVariable to be meaningful.
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* \param d Pointer to array of random values to return in sequence
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* \param c Number of values in the array
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*/
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explicit DeterministicVariable(double* d, uint32_t c);
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};
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/**
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* \brief Log-normal Distributed random var
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* \ingroup randomvariable
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*
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* LogNormalVariable defines a random variable with log-normal
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* distribution. If one takes the natural logarithm of random
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* variable following the log-normal distribution, the obtained values
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* follow a normal distribution.
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* This class supports the creation of objects that return random numbers
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* from a fixed lognormal distribution. It also supports the generation of
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* single random numbers from various lognormal distributions.
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*
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* The probability density function is defined over the interval [0,+inf) as:
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* \f$ \frac{1}{x\sigma\sqrt{2\pi}} e^{-\frac{(ln(x) - \mu)^2}{2\sigma^2}}\f$
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* where \f$ mean = e^{\mu+\frac{\sigma^2}{2}} \f$ and
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* \f$ variance = (e^{\sigma^2}-1)e^{2\mu+\sigma^2}\f$
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*
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* The \f$ \mu \f$ and \f$ \sigma \f$ parameters can be calculated from the mean
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* and standard deviation with the following equations:
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* \f$ \mu = ln(mean) - \frac{1}{2}ln\left(1+\frac{stddev}{mean^2}\right)\f$, and,
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* \f$ \sigma = \sqrt{ln\left(1+\frac{stddev}{mean^2}\right)}\f$
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*/
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class LogNormalVariable : public RandomVariable
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{
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public:
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/**
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* \param mu mu parameter of the lognormal distribution
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* \param sigma sigma parameter of the lognormal distribution
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*/
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LogNormalVariable (double mu, double sigma);
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/**
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* \param mu mu parameter of the underlying normal distribution
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* \param sigma sigma parameter of the underlying normal distribution
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* \return A random number from the distribution specified by mu and sigma
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*/
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static double GetSingleValue(double mu, double sigma);
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};
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/**
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* \brief Triangularly Distributed random var
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* \ingroup randomvariable
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*
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* This distribution is a triangular distribution. The probablility density
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* is in the shape of a triangle.
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*/
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class TriangularVariable : public RandomVariable
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{
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public:
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/**
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* Creates a triangle distribution random number generator in the
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* range [0.0 .. 1.0), with mean of 0.5
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*/
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TriangularVariable();
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/**
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* Creates a triangle distribution random number generator with the specified
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* range
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* \param s Low end of the range
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* \param l High end of the range
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* \param mean mean of the distribution
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*/
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TriangularVariable(double s, double l, double mean);
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/**
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* \param s Low end of the range
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* \param l High end of the range
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* \param mean mean of the distribution
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* \return A triangularly distributed random number between s and l
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*/
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static double GetSingleValue(double s, double l, double mean);
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};
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std::ostream &operator << (std::ostream &os, const RandomVariable &var);
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std::istream &operator >> (std::istream &os, RandomVariable &var);
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ATTRIBUTE_VALUE_DEFINE (RandomVariable);
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ATTRIBUTE_CHECKER_DEFINE (RandomVariable);
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ATTRIBUTE_ACCESSOR_DEFINE (RandomVariable);
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}//namespace ns3
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#endif
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