1505 lines
42 KiB
C++
1505 lines
42 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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// Author: Hadi Arbabi<marbabi@cs.odu.edu>
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//
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#include <iostream>
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#include <math.h>
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#include <stdlib.h>
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#include <sys/time.h> // for gettimeofday
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#include <unistd.h>
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#include <iostream>
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#include <sys/types.h>
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#include <sys/stat.h>
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#include <fcntl.h>
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#include <sstream>
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#include "assert.h"
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#include "config.h"
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#include "integer.h"
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#include "random-variable.h"
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#include "rng-stream.h"
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#include "fatal-error.h"
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using namespace std;
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namespace ns3{
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//-----------------------------------------------------------------------------
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// Seed Manager
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//-----------------------------------------------------------------------------
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uint32_t SeedManager::GetSeed()
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{
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uint32_t s[6];
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RngStream::GetPackageSeed (s);
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NS_ASSERT(
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s[0] == s[1] &&
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s[0] == s[2] &&
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s[0] == s[3] &&
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s[0] == s[4] &&
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s[0] == s[5]
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);
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return s[0];
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}
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void SeedManager::SetSeed(uint32_t seed)
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{
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Config::SetGlobal("RngSeed", IntegerValue(seed));
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}
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void SeedManager::SetRun(uint32_t run)
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{
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Config::SetGlobal("RngRun", IntegerValue(run));
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}
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uint32_t SeedManager::GetRun()
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{
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return RngStream::GetPackageRun ();
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}
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bool SeedManager::CheckSeed (uint32_t seed)
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{
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return RngStream::CheckSeed(seed);
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}
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// RandomVariableBase methods
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class RandomVariableBase
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{
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public:
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RandomVariableBase ();
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RandomVariableBase (const RandomVariableBase &o);
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virtual ~RandomVariableBase();
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virtual double GetValue() = 0;
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virtual uint32_t GetInteger();
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virtual RandomVariableBase* Copy(void) const = 0;
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protected:
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RngStream* m_generator; //underlying generator being wrapped
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};
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RandomVariableBase::RandomVariableBase()
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: m_generator(NULL)
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{
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}
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RandomVariableBase::RandomVariableBase(const RandomVariableBase& r)
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:m_generator(0)
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{
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if (r.m_generator)
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{
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m_generator = new RngStream(*r.m_generator);
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}
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}
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RandomVariableBase::~RandomVariableBase()
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{
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delete m_generator;
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}
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uint32_t RandomVariableBase::GetInteger()
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{
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return (uint32_t)GetValue();
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}
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//-------------------------------------------------------
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RandomVariable::RandomVariable()
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: m_variable (0)
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{}
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RandomVariable::RandomVariable(const RandomVariable&o)
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: m_variable (o.m_variable->Copy ())
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{}
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RandomVariable::RandomVariable (const RandomVariableBase &variable)
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: m_variable (variable.Copy ())
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{}
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RandomVariable &
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RandomVariable::operator = (const RandomVariable &o)
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{
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if (&o == this)
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{
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return *this;
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}
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delete m_variable;
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m_variable = o.m_variable->Copy ();
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return *this;
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}
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RandomVariable::~RandomVariable()
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{
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delete m_variable;
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}
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double
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RandomVariable::GetValue (void) const
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{
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return m_variable->GetValue ();
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}
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uint32_t
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RandomVariable::GetInteger (void) const
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{
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return m_variable->GetInteger ();
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}
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RandomVariableBase *
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RandomVariable::Peek (void) const
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{
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return m_variable;
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}
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ATTRIBUTE_VALUE_IMPLEMENT (RandomVariable);
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ATTRIBUTE_CHECKER_IMPLEMENT (RandomVariable);
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// UniformVariableImpl
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class UniformVariableImpl : public RandomVariableBase {
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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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UniformVariableImpl();
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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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UniformVariableImpl(double s, double l);
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UniformVariableImpl(const UniformVariableImpl& c);
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double GetMin (void) const;
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double GetMax (void) const;
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/**
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* \return A value between low and high values specified by the constructor
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*/
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virtual double GetValue();
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/**
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* \return A value between low and high values specified by parameters
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*/
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virtual double GetValue(double s, double l);
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virtual RandomVariableBase* Copy(void) const;
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private:
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double m_min;
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double m_max;
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};
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UniformVariableImpl::UniformVariableImpl()
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: m_min(0), m_max(1.0) { }
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UniformVariableImpl::UniformVariableImpl(double s, double l)
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: m_min(s), m_max(l) { }
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UniformVariableImpl::UniformVariableImpl(const UniformVariableImpl& c)
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: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max) { }
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double
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UniformVariableImpl::GetMin (void) const
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{
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return m_min;
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}
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double
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UniformVariableImpl::GetMax (void) const
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{
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return m_max;
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}
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double UniformVariableImpl::GetValue()
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{
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if(!m_generator)
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{
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m_generator = new RngStream();
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}
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return m_min + m_generator->RandU01() * (m_max - m_min);
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}
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double UniformVariableImpl::GetValue(double s, double l)
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{
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if(!m_generator)
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{
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m_generator = new RngStream();
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}
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return s + m_generator->RandU01() * (l-s);
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}
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RandomVariableBase* UniformVariableImpl::Copy() const
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{
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return new UniformVariableImpl(*this);
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}
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UniformVariable::UniformVariable()
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: RandomVariable (UniformVariableImpl ())
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{}
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UniformVariable::UniformVariable(double s, double l)
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: RandomVariable (UniformVariableImpl (s, l))
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{}
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double UniformVariable::GetValue(void) const
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{
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return this->RandomVariable::GetValue ();
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}
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double UniformVariable::GetValue(double s, double l)
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{
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return ((UniformVariableImpl*)Peek())->GetValue(s,l);
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}
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uint32_t UniformVariable::GetInteger (uint32_t s, uint32_t l)
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{
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NS_ASSERT(s <= l);
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return static_cast<uint32_t>( GetValue(s, l+1) );
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}
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// ConstantVariableImpl methods
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class ConstantVariableImpl : public RandomVariableBase {
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public:
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/**
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* Construct a ConstantVariableImpl RNG that returns zero every sample
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*/
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ConstantVariableImpl();
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/**
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* Construct a ConstantVariableImpl 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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ConstantVariableImpl(double c);
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ConstantVariableImpl(const ConstantVariableImpl& 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 NewConstant(double c);
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/**
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* \return The constant value specified
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*/
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virtual double GetValue();
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virtual uint32_t GetInteger();
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virtual RandomVariableBase* Copy(void) const;
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private:
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double m_const;
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};
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ConstantVariableImpl::ConstantVariableImpl()
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: m_const(0) { }
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ConstantVariableImpl::ConstantVariableImpl(double c)
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: m_const(c) { };
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ConstantVariableImpl::ConstantVariableImpl(const ConstantVariableImpl& c)
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: RandomVariableBase(c), m_const(c.m_const) { }
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void ConstantVariableImpl::NewConstant(double c)
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{ m_const = c;}
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double ConstantVariableImpl::GetValue()
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{
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return m_const;
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}
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uint32_t ConstantVariableImpl::GetInteger()
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{
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return (uint32_t)m_const;
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}
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RandomVariableBase* ConstantVariableImpl::Copy() const
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{
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return new ConstantVariableImpl(*this);
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}
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ConstantVariable::ConstantVariable()
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: RandomVariable (ConstantVariableImpl ())
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{}
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ConstantVariable::ConstantVariable(double c)
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: RandomVariable (ConstantVariableImpl (c))
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{}
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void
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ConstantVariable::SetConstant(double c)
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{
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*this = ConstantVariable (c);
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}
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// SequentialVariableImpl methods
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class SequentialVariableImpl : public RandomVariableBase {
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public:
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/**
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* \brief Constructor for the SequentialVariableImpl RNG.
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*
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* The four parameters define the sequence. For example
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* SequentialVariableImpl(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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SequentialVariableImpl(double f, double l, double i = 1, uint32_t c = 1);
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/**
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* \brief Constructor for the SequentialVariableImpl 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 RandomVariableBase 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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SequentialVariableImpl(double f, double l, const RandomVariable& i, uint32_t c = 1);
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SequentialVariableImpl(const SequentialVariableImpl& c);
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~SequentialVariableImpl();
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/**
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* \return The next value in the Sequence
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*/
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virtual double GetValue();
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virtual RandomVariableBase* Copy(void) const;
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private:
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double m_min;
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double m_max;
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RandomVariable m_increment;
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uint32_t m_consecutive;
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double m_current;
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uint32_t m_currentConsecutive;
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};
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SequentialVariableImpl::SequentialVariableImpl(double f, double l, double i, uint32_t c)
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: m_min(f), m_max(l), m_increment(ConstantVariable(i)), m_consecutive(c),
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m_current(f), m_currentConsecutive(0)
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{}
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SequentialVariableImpl::SequentialVariableImpl(double f, double l, const RandomVariable& i, uint32_t c)
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: m_min(f), m_max(l), m_increment(i), m_consecutive(c),
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m_current(f), m_currentConsecutive(0)
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{}
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SequentialVariableImpl::SequentialVariableImpl(const SequentialVariableImpl& c)
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: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max),
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m_increment(c.m_increment), m_consecutive(c.m_consecutive),
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m_current(c.m_current), m_currentConsecutive(c.m_currentConsecutive)
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{}
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SequentialVariableImpl::~SequentialVariableImpl()
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{}
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double SequentialVariableImpl::GetValue()
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{ // Return a sequential series of values
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double r = m_current;
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if (++m_currentConsecutive == m_consecutive)
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{ // Time to advance to next
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m_currentConsecutive = 0;
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m_current += m_increment.GetValue();
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if (m_current >= m_max)
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m_current = m_min + (m_current - m_max);
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}
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return r;
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}
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RandomVariableBase* SequentialVariableImpl::Copy() const
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{
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return new SequentialVariableImpl(*this);
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}
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SequentialVariable::SequentialVariable(double f, double l, double i, uint32_t c)
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: RandomVariable (SequentialVariableImpl (f, l, i, c))
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{}
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SequentialVariable::SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c)
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: RandomVariable (SequentialVariableImpl (f, l, i, c))
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{}
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// ExponentialVariableImpl methods
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class ExponentialVariableImpl : public RandomVariableBase {
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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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ExponentialVariableImpl();
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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 ExponentialVariableImpl(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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ExponentialVariableImpl(double m, double b);
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ExponentialVariableImpl(const ExponentialVariableImpl& c);
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/**
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* \return A random value from this exponential distribution
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*/
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virtual double GetValue();
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virtual RandomVariableBase* Copy(void) const;
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private:
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double m_mean; // Mean value of RV
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double m_bound; // Upper bound on value (if non-zero)
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};
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ExponentialVariableImpl::ExponentialVariableImpl()
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: m_mean(1.0), m_bound(0) { }
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ExponentialVariableImpl::ExponentialVariableImpl(double m)
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: m_mean(m), m_bound(0) { }
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ExponentialVariableImpl::ExponentialVariableImpl(double m, double b)
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: m_mean(m), m_bound(b) { }
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ExponentialVariableImpl::ExponentialVariableImpl(const ExponentialVariableImpl& c)
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: RandomVariableBase(c), m_mean(c.m_mean), m_bound(c.m_bound) { }
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double ExponentialVariableImpl::GetValue()
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{
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if(!m_generator)
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{
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m_generator = new RngStream();
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}
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while(1)
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{
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double r = -m_mean*log(m_generator->RandU01());
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if (m_bound == 0 || r <= m_bound) return r;
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//otherwise, try again
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}
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}
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RandomVariableBase* ExponentialVariableImpl::Copy() const
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{
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return new ExponentialVariableImpl(*this);
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}
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ExponentialVariable::ExponentialVariable()
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: RandomVariable (ExponentialVariableImpl ())
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{}
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ExponentialVariable::ExponentialVariable(double m)
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: RandomVariable (ExponentialVariableImpl (m))
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{}
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ExponentialVariable::ExponentialVariable(double m, double b)
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: RandomVariable (ExponentialVariableImpl (m, b))
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{}
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// ParetoVariableImpl methods
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class ParetoVariableImpl : public RandomVariableBase {
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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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ParetoVariableImpl();
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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 ParetoVariableImpl(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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ParetoVariableImpl(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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ParetoVariableImpl(double m, double s, double b);
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ParetoVariableImpl(const ParetoVariableImpl& c);
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/**
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* \return A random value from this Pareto distribution
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*/
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virtual double GetValue();
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virtual RandomVariableBase* Copy() const;
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private:
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double m_mean; // Mean value of RV
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double m_shape; // Shape parameter
|
|
double m_bound; // Upper bound on value (if non-zero)
|
|
};
|
|
|
|
ParetoVariableImpl::ParetoVariableImpl()
|
|
: m_mean(1.0), m_shape(1.5), m_bound(0) { }
|
|
|
|
ParetoVariableImpl::ParetoVariableImpl(double m)
|
|
: m_mean(m), m_shape(1.5), m_bound(0) { }
|
|
|
|
ParetoVariableImpl::ParetoVariableImpl(double m, double s)
|
|
: m_mean(m), m_shape(s), m_bound(0) { }
|
|
|
|
ParetoVariableImpl::ParetoVariableImpl(double m, double s, double b)
|
|
: m_mean(m), m_shape(s), m_bound(b) { }
|
|
|
|
ParetoVariableImpl::ParetoVariableImpl(const ParetoVariableImpl& c)
|
|
: RandomVariableBase(c), m_mean(c.m_mean), m_shape(c.m_shape),
|
|
m_bound(c.m_bound) { }
|
|
|
|
double ParetoVariableImpl::GetValue()
|
|
{
|
|
if(!m_generator)
|
|
{
|
|
m_generator = new RngStream();
|
|
}
|
|
double scale = m_mean * ( m_shape - 1.0) / m_shape;
|
|
while(1)
|
|
{
|
|
double r = (scale * ( 1.0 / pow(m_generator->RandU01(), 1.0 / m_shape)));
|
|
if (m_bound == 0 || r <= m_bound) return r;
|
|
//otherwise, try again
|
|
}
|
|
}
|
|
|
|
RandomVariableBase* ParetoVariableImpl::Copy() const
|
|
{
|
|
return new ParetoVariableImpl(*this);
|
|
}
|
|
|
|
ParetoVariable::ParetoVariable ()
|
|
: RandomVariable (ParetoVariableImpl ())
|
|
{}
|
|
ParetoVariable::ParetoVariable(double m)
|
|
: RandomVariable (ParetoVariableImpl (m))
|
|
{}
|
|
ParetoVariable::ParetoVariable(double m, double s)
|
|
: RandomVariable (ParetoVariableImpl (m, s))
|
|
{}
|
|
ParetoVariable::ParetoVariable(double m, double s, double b)
|
|
: RandomVariable (ParetoVariableImpl (m, s, b))
|
|
{}
|
|
|
|
//-----------------------------------------------------------------------------
|
|
//-----------------------------------------------------------------------------
|
|
// WeibullVariableImpl methods
|
|
|
|
class WeibullVariableImpl : public RandomVariableBase {
|
|
public:
|
|
/**
|
|
* Constructs a weibull random variable with a mean
|
|
* value of 1.0 and a shape (alpha) parameter of 1
|
|
*/
|
|
WeibullVariableImpl();
|
|
|
|
|
|
/**
|
|
* Constructs a weibull random variable with the specified mean
|
|
* value and a shape (alpha) parameter of 1.5.
|
|
* \param m mean value of the distribution
|
|
*/
|
|
WeibullVariableImpl(double m) ;
|
|
|
|
/**
|
|
* Constructs a weibull random variable with the specified mean
|
|
* value and a shape (alpha).
|
|
* \param m Mean value for the distribution.
|
|
* \param s Shape (alpha) parameter for the distribution.
|
|
*/
|
|
WeibullVariableImpl(double m, double s);
|
|
|
|
/**
|
|
* \brief Constructs a weibull random variable with the specified mean
|
|
* \brief value, shape (alpha), and upper bound.
|
|
* Since WeibullVariableImpl distributions can theoretically return unbounded values,
|
|
* it is sometimes usefull to specify a fixed upper limit. Note however
|
|
* that when the upper limit is specified, the true mean of the distribution
|
|
* is slightly smaller than the mean value specified.
|
|
* \param m Mean value for the distribution.
|
|
* \param s Shape (alpha) parameter for the distribution.
|
|
* \param b Upper limit on returned values
|
|
*/
|
|
WeibullVariableImpl(double m, double s, double b);
|
|
|
|
WeibullVariableImpl(const WeibullVariableImpl& c);
|
|
|
|
/**
|
|
* \return A random value from this Weibull distribution
|
|
*/
|
|
virtual double GetValue();
|
|
virtual RandomVariableBase* Copy(void) const;
|
|
|
|
private:
|
|
double m_mean; // Mean value of RV
|
|
double m_alpha; // Shape parameter
|
|
double m_bound; // Upper bound on value (if non-zero)
|
|
};
|
|
|
|
WeibullVariableImpl::WeibullVariableImpl() : m_mean(1.0), m_alpha(1), m_bound(0) { }
|
|
WeibullVariableImpl::WeibullVariableImpl(double m)
|
|
: m_mean(m), m_alpha(1), m_bound(0) { }
|
|
WeibullVariableImpl::WeibullVariableImpl(double m, double s)
|
|
: m_mean(m), m_alpha(s), m_bound(0) { }
|
|
WeibullVariableImpl::WeibullVariableImpl(double m, double s, double b)
|
|
: m_mean(m), m_alpha(s), m_bound(b) { };
|
|
WeibullVariableImpl::WeibullVariableImpl(const WeibullVariableImpl& c)
|
|
: RandomVariableBase(c), m_mean(c.m_mean), m_alpha(c.m_alpha),
|
|
m_bound(c.m_bound) { }
|
|
|
|
double WeibullVariableImpl::GetValue()
|
|
{
|
|
if(!m_generator)
|
|
{
|
|
m_generator = new RngStream();
|
|
}
|
|
double exponent = 1.0 / m_alpha;
|
|
while(1)
|
|
{
|
|
double r = m_mean * pow( -log(m_generator->RandU01()), exponent);
|
|
if (m_bound == 0 || r <= m_bound) return r;
|
|
//otherwise, try again
|
|
}
|
|
}
|
|
|
|
RandomVariableBase* WeibullVariableImpl::Copy() const
|
|
{
|
|
return new WeibullVariableImpl(*this);
|
|
}
|
|
|
|
WeibullVariable::WeibullVariable()
|
|
: RandomVariable (WeibullVariableImpl ())
|
|
{}
|
|
WeibullVariable::WeibullVariable(double m)
|
|
: RandomVariable (WeibullVariableImpl (m))
|
|
{}
|
|
WeibullVariable::WeibullVariable(double m, double s)
|
|
: RandomVariable (WeibullVariableImpl (m, s))
|
|
{}
|
|
WeibullVariable::WeibullVariable(double m, double s, double b)
|
|
: RandomVariable (WeibullVariableImpl (m, s, b))
|
|
{}
|
|
|
|
//-----------------------------------------------------------------------------
|
|
//-----------------------------------------------------------------------------
|
|
// NormalVariableImpl methods
|
|
|
|
class NormalVariableImpl : public RandomVariableBase { // Normally Distributed random var
|
|
|
|
public:
|
|
static const double INFINITE_VALUE;
|
|
/**
|
|
* Constructs an normal random variable with a mean
|
|
* value of 0 and variance of 1.
|
|
*/
|
|
NormalVariableImpl();
|
|
|
|
/**
|
|
* \brief Construct a normal random variable with specified mean and variance
|
|
* \param m Mean value
|
|
* \param v Variance
|
|
* \param b Bound. The NormalVariableImpl is bounded within +-bound of the mean.
|
|
*/
|
|
NormalVariableImpl(double m, double v, double b = INFINITE_VALUE);
|
|
|
|
NormalVariableImpl(const NormalVariableImpl& c);
|
|
|
|
/**
|
|
* \return A value from this normal distribution
|
|
*/
|
|
virtual double GetValue();
|
|
virtual RandomVariableBase* Copy(void) const;
|
|
|
|
double GetMean (void) const;
|
|
double GetVariance (void) const;
|
|
double GetBound (void) const;
|
|
|
|
private:
|
|
double m_mean; // Mean value of RV
|
|
double m_variance; // Mean value of RV
|
|
double m_bound; // Bound on value's difference from the mean (absolute value)
|
|
bool m_nextValid; // True if next valid
|
|
double m_next; // The algorithm produces two values at a time
|
|
static bool m_static_nextValid;
|
|
static double m_static_next;
|
|
};
|
|
|
|
bool NormalVariableImpl::m_static_nextValid = false;
|
|
double NormalVariableImpl::m_static_next;
|
|
const double NormalVariableImpl::INFINITE_VALUE = 1e307;
|
|
|
|
NormalVariableImpl::NormalVariableImpl()
|
|
: m_mean(0.0), m_variance(1.0), m_bound(INFINITE_VALUE), m_nextValid(false){}
|
|
|
|
NormalVariableImpl::NormalVariableImpl(double m, double v, double b/*=INFINITE_VALUE*/)
|
|
: m_mean(m), m_variance(v), m_bound(b), m_nextValid(false) { }
|
|
|
|
NormalVariableImpl::NormalVariableImpl(const NormalVariableImpl& c)
|
|
: RandomVariableBase(c), m_mean(c.m_mean), m_variance(c.m_variance),
|
|
m_bound(c.m_bound) { }
|
|
|
|
double NormalVariableImpl::GetValue()
|
|
{
|
|
if(!m_generator)
|
|
{
|
|
m_generator = new RngStream();
|
|
}
|
|
if (m_nextValid)
|
|
{ // use previously generated
|
|
m_nextValid = false;
|
|
return m_next;
|
|
}
|
|
while(1)
|
|
{ // See Simulation Modeling and Analysis p. 466 (Averill Law)
|
|
// for algorithm; basically a Box-Muller transform:
|
|
// http://en.wikipedia.org/wiki/Box-Muller_transform
|
|
double u1 = m_generator->RandU01();
|
|
double u2 = m_generator->RandU01();;
|
|
double v1 = 2 * u1 - 1;
|
|
double v2 = 2 * u2 - 1;
|
|
double w = v1 * v1 + v2 * v2;
|
|
if (w <= 1.0)
|
|
{ // Got good pair
|
|
double y = sqrt((-2 * log(w))/w);
|
|
m_next = m_mean + v2 * y * sqrt(m_variance);
|
|
//if next is in bounds, it is valid
|
|
m_nextValid = fabs(m_next-m_mean) <= m_bound;
|
|
double x1 = m_mean + v1 * y * sqrt(m_variance);
|
|
//if x1 is in bounds, return it
|
|
if (fabs(x1-m_mean) <= m_bound)
|
|
{
|
|
return x1;
|
|
}
|
|
//otherwise try and return m_next if it is valid
|
|
else if (m_nextValid)
|
|
{
|
|
m_nextValid = false;
|
|
return m_next;
|
|
}
|
|
//otherwise, just run this loop again
|
|
}
|
|
}
|
|
}
|
|
|
|
RandomVariableBase* NormalVariableImpl::Copy() const
|
|
{
|
|
return new NormalVariableImpl(*this);
|
|
}
|
|
|
|
double
|
|
NormalVariableImpl::GetMean (void) const
|
|
{
|
|
return m_mean;
|
|
}
|
|
|
|
double
|
|
NormalVariableImpl::GetVariance (void) const
|
|
{
|
|
return m_variance;
|
|
}
|
|
|
|
double
|
|
NormalVariableImpl::GetBound (void) const
|
|
{
|
|
return m_bound;
|
|
}
|
|
|
|
NormalVariable::NormalVariable()
|
|
: RandomVariable (NormalVariableImpl ())
|
|
{}
|
|
NormalVariable::NormalVariable(double m, double v)
|
|
: RandomVariable (NormalVariableImpl (m, v))
|
|
{}
|
|
NormalVariable::NormalVariable(double m, double v, double b)
|
|
: RandomVariable (NormalVariableImpl (m, v, b))
|
|
{}
|
|
|
|
//-----------------------------------------------------------------------------
|
|
//-----------------------------------------------------------------------------
|
|
class EmpiricalVariableImpl : public RandomVariableBase {
|
|
public:
|
|
/**
|
|
* Constructor for the EmpiricalVariableImpl random variables.
|
|
*/
|
|
explicit EmpiricalVariableImpl();
|
|
|
|
virtual ~EmpiricalVariableImpl();
|
|
EmpiricalVariableImpl(const EmpiricalVariableImpl& c);
|
|
/**
|
|
* \return A value from this empirical distribution
|
|
*/
|
|
virtual double GetValue();
|
|
virtual RandomVariableBase* Copy(void) const;
|
|
/**
|
|
* \brief Specifies a point in the empirical distribution
|
|
* \param v The function value for this point
|
|
* \param c Probability that the function is less than or equal to v
|
|
*/
|
|
virtual void CDF(double v, double c); // Value, prob <= Value
|
|
|
|
private:
|
|
class ValueCDF {
|
|
public:
|
|
ValueCDF();
|
|
ValueCDF(double v, double c);
|
|
ValueCDF(const ValueCDF& c);
|
|
double value;
|
|
double cdf;
|
|
};
|
|
virtual void Validate(); // Insure non-decreasing emiprical values
|
|
virtual double Interpolate(double, double, double, double, double);
|
|
bool validated; // True if non-decreasing validated
|
|
std::vector<ValueCDF> emp; // Empicical CDF
|
|
};
|
|
|
|
|
|
// ValueCDF methods
|
|
EmpiricalVariableImpl::ValueCDF::ValueCDF()
|
|
: value(0.0), cdf(0.0){ }
|
|
EmpiricalVariableImpl::ValueCDF::ValueCDF(double v, double c)
|
|
: value(v), cdf(c) { }
|
|
EmpiricalVariableImpl::ValueCDF::ValueCDF(const ValueCDF& c)
|
|
: value(c.value), cdf(c.cdf) { }
|
|
|
|
//-----------------------------------------------------------------------------
|
|
//-----------------------------------------------------------------------------
|
|
// EmpiricalVariableImpl methods
|
|
EmpiricalVariableImpl::EmpiricalVariableImpl()
|
|
: validated(false) { }
|
|
|
|
EmpiricalVariableImpl::EmpiricalVariableImpl(const EmpiricalVariableImpl& c)
|
|
: RandomVariableBase(c), validated(c.validated), emp(c.emp) { }
|
|
|
|
EmpiricalVariableImpl::~EmpiricalVariableImpl() { }
|
|
|
|
double EmpiricalVariableImpl::GetValue()
|
|
{ // Return a value from the empirical distribution
|
|
// This code based (loosely) on code by Bruce Mah (Thanks Bruce!)
|
|
if(!m_generator)
|
|
{
|
|
m_generator = new RngStream();
|
|
}
|
|
if (emp.size() == 0)
|
|
{
|
|
return 0.0; // HuH? No empirical data
|
|
}
|
|
if (!validated)
|
|
{
|
|
Validate(); // Insure in non-decreasing
|
|
}
|
|
double r = m_generator->RandU01();
|
|
if (r <= emp.front().cdf)
|
|
{
|
|
return emp.front().value; // Less than first
|
|
}
|
|
if (r >= emp.back().cdf)
|
|
{
|
|
return emp.back().value; // Greater than last
|
|
}
|
|
// Binary search
|
|
std::vector<ValueCDF>::size_type bottom = 0;
|
|
std::vector<ValueCDF>::size_type top = emp.size() - 1;
|
|
while(1)
|
|
{
|
|
std::vector<ValueCDF>::size_type c = (top + bottom) / 2;
|
|
if (r >= emp[c].cdf && r < emp[c+1].cdf)
|
|
{ // Found it
|
|
return Interpolate(emp[c].cdf, emp[c+1].cdf,
|
|
emp[c].value, emp[c+1].value,
|
|
r);
|
|
}
|
|
// Not here, adjust bounds
|
|
if (r < emp[c].cdf)
|
|
{
|
|
top = c - 1;
|
|
}
|
|
else
|
|
{
|
|
bottom = c + 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
RandomVariableBase* EmpiricalVariableImpl::Copy() const
|
|
{
|
|
return new EmpiricalVariableImpl(*this);
|
|
}
|
|
|
|
void EmpiricalVariableImpl::CDF(double v, double c)
|
|
{ // Add a new empirical datapoint to the empirical cdf
|
|
// NOTE. These MUST be inserted in non-decreasing order
|
|
emp.push_back(ValueCDF(v, c));
|
|
}
|
|
|
|
void EmpiricalVariableImpl::Validate()
|
|
{
|
|
ValueCDF prior;
|
|
for (std::vector<ValueCDF>::size_type i = 0; i < emp.size(); ++i)
|
|
{
|
|
ValueCDF& current = emp[i];
|
|
if (current.value < prior.value || current.cdf < prior.cdf)
|
|
{ // Error
|
|
cerr << "Empirical Dist error,"
|
|
<< " current value " << current.value
|
|
<< " prior value " << prior.value
|
|
<< " current cdf " << current.cdf
|
|
<< " prior cdf " << prior.cdf << endl;
|
|
NS_FATAL_ERROR("Empirical Dist error");
|
|
}
|
|
prior = current;
|
|
}
|
|
validated = true;
|
|
}
|
|
|
|
double EmpiricalVariableImpl::Interpolate(double c1, double c2,
|
|
double v1, double v2, double r)
|
|
{ // Interpolate random value in range [v1..v2) based on [c1 .. r .. c2)
|
|
return (v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
|
|
}
|
|
|
|
EmpiricalVariable::EmpiricalVariable()
|
|
: RandomVariable (EmpiricalVariableImpl ())
|
|
{}
|
|
EmpiricalVariable::EmpiricalVariable (const RandomVariableBase &variable)
|
|
: RandomVariable (variable)
|
|
{}
|
|
void
|
|
EmpiricalVariable::CDF(double v, double c)
|
|
{
|
|
EmpiricalVariableImpl *impl = dynamic_cast<EmpiricalVariableImpl *> (Peek ());
|
|
NS_ASSERT (impl);
|
|
impl->CDF (v, c);
|
|
}
|
|
|
|
|
|
//-----------------------------------------------------------------------------
|
|
//-----------------------------------------------------------------------------
|
|
// IntegerValue EmpiricalVariableImpl methods
|
|
class IntEmpiricalVariableImpl : public EmpiricalVariableImpl {
|
|
public:
|
|
|
|
IntEmpiricalVariableImpl();
|
|
|
|
virtual RandomVariableBase* Copy(void) const;
|
|
/**
|
|
* \return An integer value from this empirical distribution
|
|
*/
|
|
virtual uint32_t GetInteger();
|
|
private:
|
|
virtual double Interpolate(double, double, double, double, double);
|
|
};
|
|
|
|
|
|
IntEmpiricalVariableImpl::IntEmpiricalVariableImpl() { }
|
|
|
|
uint32_t IntEmpiricalVariableImpl::GetInteger()
|
|
{
|
|
return (uint32_t)GetValue();
|
|
}
|
|
|
|
RandomVariableBase* IntEmpiricalVariableImpl::Copy() const
|
|
{
|
|
return new IntEmpiricalVariableImpl(*this);
|
|
}
|
|
|
|
double IntEmpiricalVariableImpl::Interpolate(double c1, double c2,
|
|
double v1, double v2, double r)
|
|
{ // Interpolate random value in range [v1..v2) based on [c1 .. r .. c2)
|
|
return ceil(v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
|
|
}
|
|
|
|
IntEmpiricalVariable::IntEmpiricalVariable()
|
|
: EmpiricalVariable (IntEmpiricalVariableImpl ())
|
|
{}
|
|
|
|
//-----------------------------------------------------------------------------
|
|
//-----------------------------------------------------------------------------
|
|
// DeterministicVariableImpl
|
|
class DeterministicVariableImpl : public RandomVariableBase
|
|
{
|
|
|
|
public:
|
|
/**
|
|
* \brief Constructor
|
|
*
|
|
* Creates a generator that returns successive elements of the d array
|
|
* on successive calls to ::Value(). Note that the d pointer is copied
|
|
* for use by the generator (shallow-copy), not its contents, so the
|
|
* contents of the array d points to have to remain unchanged for the use
|
|
* of DeterministicVariableImpl to be meaningful.
|
|
* \param d Pointer to array of random values to return in sequence
|
|
* \param c Number of values in the array
|
|
*/
|
|
explicit DeterministicVariableImpl(double* d, uint32_t c);
|
|
|
|
virtual ~DeterministicVariableImpl();
|
|
/**
|
|
* \return The next value in the deterministic sequence
|
|
*/
|
|
virtual double GetValue();
|
|
virtual RandomVariableBase* Copy(void) const;
|
|
private:
|
|
uint32_t count;
|
|
uint32_t next;
|
|
double* data;
|
|
};
|
|
|
|
DeterministicVariableImpl::DeterministicVariableImpl(double* d, uint32_t c)
|
|
: count(c), next(c), data(d)
|
|
{ // Nothing else needed
|
|
}
|
|
|
|
DeterministicVariableImpl::~DeterministicVariableImpl() { }
|
|
|
|
double DeterministicVariableImpl::GetValue()
|
|
{
|
|
if (next == count)
|
|
{
|
|
next = 0;
|
|
}
|
|
return data[next++];
|
|
}
|
|
|
|
RandomVariableBase* DeterministicVariableImpl::Copy() const
|
|
{
|
|
return new DeterministicVariableImpl(*this);
|
|
}
|
|
|
|
DeterministicVariable::DeterministicVariable(double* d, uint32_t c)
|
|
: RandomVariable (DeterministicVariableImpl (d, c))
|
|
{}
|
|
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// LogNormalVariableImpl
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class LogNormalVariableImpl : public RandomVariableBase {
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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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LogNormalVariableImpl (double mu, double sigma);
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/**
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* \return A random value from this distribution
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*/
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virtual double GetValue ();
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virtual RandomVariableBase* Copy(void) const;
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private:
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double m_mu;
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double m_sigma;
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};
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RandomVariableBase* LogNormalVariableImpl::Copy () const
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{
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return new LogNormalVariableImpl (m_mu, m_sigma);
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}
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LogNormalVariableImpl::LogNormalVariableImpl (double mu, double sigma)
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:m_mu(mu), m_sigma(sigma)
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{
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}
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// The code from this function was adapted from the GNU Scientific
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// Library 1.8:
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/* randist/lognormal.c
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*
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* Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough
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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 as published by
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* the Free Software Foundation; either version 2 of the License, or (at
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* your option) any later version.
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*
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* This program is distributed in the hope that it will be useful, but
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* WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* 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., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
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*/
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/* The lognormal distribution has the form
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p(x) dx = 1/(x * sqrt(2 pi sigma^2)) exp(-(ln(x) - zeta)^2/2 sigma^2) dx
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for x > 0. Lognormal random numbers are the exponentials of
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gaussian random numbers */
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double
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LogNormalVariableImpl::GetValue ()
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{
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if(!m_generator)
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{
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m_generator = new RngStream();
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}
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double u, v, r2, normal, z;
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do
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{
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/* choose x,y in uniform square (-1,-1) to (+1,+1) */
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u = -1 + 2 * m_generator->RandU01 ();
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v = -1 + 2 * m_generator->RandU01 ();
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/* see if it is in the unit circle */
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r2 = u * u + v * v;
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}
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while (r2 > 1.0 || r2 == 0);
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normal = u * sqrt (-2.0 * log (r2) / r2);
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z = exp (m_sigma * normal + m_mu);
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return z;
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}
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LogNormalVariable::LogNormalVariable (double mu, double sigma)
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: RandomVariable (LogNormalVariableImpl (mu, sigma))
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{}
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//-----------------------------------------------------------------------------
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//-----------------------------------------------------------------------------
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// TriangularVariableImpl methods
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class TriangularVariableImpl : public RandomVariableBase {
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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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TriangularVariableImpl();
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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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TriangularVariableImpl(double s, double l, double mean);
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TriangularVariableImpl(const TriangularVariableImpl& c);
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/**
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* \return A value from this distribution
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*/
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virtual double GetValue();
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virtual RandomVariableBase* Copy(void) const;
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private:
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double m_min;
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double m_max;
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double m_mode; //easier to work with the mode internally instead of the mean
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//they are related by the simple: mean = (min+max+mode)/3
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};
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TriangularVariableImpl::TriangularVariableImpl()
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: m_min(0), m_max(1), m_mode(0.5) { }
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TriangularVariableImpl::TriangularVariableImpl(double s, double l, double mean)
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: m_min(s), m_max(l), m_mode(3.0*mean-s-l) { }
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TriangularVariableImpl::TriangularVariableImpl(const TriangularVariableImpl& c)
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: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max), m_mode(c.m_mode) { }
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double TriangularVariableImpl::GetValue()
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{
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if(!m_generator)
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{
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m_generator = new RngStream();
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}
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double u = m_generator->RandU01();
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if(u <= (m_mode - m_min) / (m_max - m_min) )
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{
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return m_min + sqrt(u * (m_max - m_min) * (m_mode - m_min) );
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}
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else
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{
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return m_max - sqrt( (1-u) * (m_max - m_min) * (m_max - m_mode) );
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}
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}
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RandomVariableBase* TriangularVariableImpl::Copy() const
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{
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return new TriangularVariableImpl(*this);
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}
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TriangularVariable::TriangularVariable()
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: RandomVariable (TriangularVariableImpl ())
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{}
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TriangularVariable::TriangularVariable(double s, double l, double mean)
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: RandomVariable (TriangularVariableImpl (s,l,mean))
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{}
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std::ostream &operator << (std::ostream &os, const RandomVariable &var)
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{
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RandomVariableBase *base = var.Peek ();
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ConstantVariableImpl *constant = dynamic_cast<ConstantVariableImpl *> (base);
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if (constant != 0)
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{
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os << "Constant:" << constant->GetValue ();
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return os;
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}
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UniformVariableImpl *uniform = dynamic_cast<UniformVariableImpl *> (base);
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if (uniform != 0)
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{
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os << "Uniform:" << uniform->GetMin () << ":" << uniform->GetMax ();
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return os;
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}
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NormalVariableImpl *normal = dynamic_cast<NormalVariableImpl *> (base);
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if (normal != 0)
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{
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os << "Normal:" << normal->GetMean () << ":" << normal->GetVariance ();
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double bound = normal->GetBound ();
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if (bound != NormalVariableImpl::INFINITE_VALUE)
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{
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os << ":" << bound;
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}
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return os;
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}
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// XXX: support other distributions
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os.setstate (std::ios_base::badbit);
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return os;
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}
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std::istream &operator >> (std::istream &is, RandomVariable &var)
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{
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std::string value;
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is >> value;
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std::string::size_type tmp;
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tmp = value.find (":");
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if (tmp == std::string::npos)
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{
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is.setstate (std::ios_base::badbit);
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return is;
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}
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std::string type = value.substr (0, tmp);
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value = value.substr (tmp + 1, value.npos);
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if (type == "Constant")
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{
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istringstream iss (value);
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double constant;
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iss >> constant;
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var = ConstantVariable (constant);
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}
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else if (type == "Uniform")
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{
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if (value.size () == 0)
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{
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var = UniformVariable ();
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}
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else
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{
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tmp = value.find (":");
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if (tmp == value.npos)
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{
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NS_FATAL_ERROR ("bad Uniform value: " << value);
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}
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istringstream issA (value.substr (0, tmp));
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istringstream issB (value.substr (tmp + 1, value.npos));
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double a, b;
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issA >> a;
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issB >> b;
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var = UniformVariable (a, b);
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}
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}
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else if (type == "Normal")
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{
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if (value.size () == 0)
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{
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var = NormalVariable ();
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}
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else
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{
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tmp = value.find (":");
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if (tmp == value.npos)
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{
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NS_FATAL_ERROR ("bad Normal value: " << value);
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}
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std::string::size_type tmp2;
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std::string sub = value.substr (tmp + 1, value.npos);
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tmp2 = sub.find (":");
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if (tmp2 == value.npos)
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{
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istringstream issA (value.substr (0, tmp));
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istringstream issB (sub);
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double a, b;
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issA >> a;
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issB >> b;
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var = NormalVariable (a, b);
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}
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else
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{
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istringstream issA (value.substr (0, tmp));
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istringstream issB (sub.substr (0, tmp2));
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istringstream issC (sub.substr (tmp2 + 1, value.npos));
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double a, b, c;
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issA >> a;
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issB >> b;
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issC >> c;
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var = NormalVariable (a, b, c);
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}
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}
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}
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else
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{
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NS_FATAL_ERROR ("RandomVariable deserialization not implemented for " << type);
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// XXX: support other distributions.
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}
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return is;
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}
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}//namespace ns3
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#ifdef RUN_SELF_TESTS
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#include "test.h"
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#include <vector>
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namespace ns3 {
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class RandomVariableTest : public Test
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{
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public:
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RandomVariableTest () : Test ("RandomVariable") {}
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virtual bool RunTests (void)
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{
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bool result = true;
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const double desired_mean = 1.0;
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const double desired_stddev = 1.0;
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double tmp = log (1 + (desired_stddev/desired_mean)*(desired_stddev/desired_mean));
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double sigma = sqrt (tmp);
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double mu = log (desired_mean) - 0.5*tmp;
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// Test a custom lognormal instance
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{
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LogNormalVariable lognormal (mu, sigma);
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vector<double> samples;
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const int NSAMPLES = 10000;
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double sum = 0;
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for (int n = NSAMPLES; n; --n)
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{
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double value = lognormal.GetValue ();
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sum += value;
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samples.push_back (value);
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}
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double obtained_mean = sum / NSAMPLES;
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sum = 0;
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for (vector<double>::iterator iter = samples.begin (); iter != samples.end (); iter++)
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{
|
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double tmp = (*iter - obtained_mean);
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sum += tmp*tmp;
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}
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double obtained_stddev = sqrt (sum / (NSAMPLES - 1));
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if (not (obtained_mean/desired_mean > 0.90 and obtained_mean/desired_mean < 1.10))
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{
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result = false;
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Failure () << "Obtained lognormal mean value " << obtained_mean << ", expected " << desired_mean << std::endl;
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}
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if (not (obtained_stddev/desired_stddev > 0.90 and obtained_stddev/desired_stddev < 1.10))
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{
|
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result = false;
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Failure () << "Obtained lognormal stddev value " << obtained_stddev <<
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", expected " << desired_stddev << std::endl;
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}
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}
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|
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// Test attribute serialization
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{
|
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{
|
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RandomVariableValue val;
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val.DeserializeFromString ("Uniform:0.1:0.2", MakeRandomVariableChecker ());
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RandomVariable rng = val.Get ();
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NS_TEST_ASSERT_EQUAL (val.SerializeToString (MakeRandomVariableChecker ()), "Uniform:0.1:0.2");
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}
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{
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RandomVariableValue val;
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val.DeserializeFromString ("Normal:0.1:0.2", MakeRandomVariableChecker ());
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RandomVariable rng = val.Get ();
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NS_TEST_ASSERT_EQUAL (val.SerializeToString (MakeRandomVariableChecker ()), "Normal:0.1:0.2");
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}
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{
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RandomVariableValue val;
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val.DeserializeFromString ("Normal:0.1:0.2:0.15", MakeRandomVariableChecker ());
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RandomVariable rng = val.Get ();
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NS_TEST_ASSERT_EQUAL (val.SerializeToString (MakeRandomVariableChecker ()), "Normal:0.1:0.2:0.15");
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}
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}
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return result;
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}
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};
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static RandomVariableTest g_random_variable_tests;
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}//namespace ns3
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#endif /* RUN_SELF_TESTS */
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