/* -*- Mode:C++; c-file-style:"gnu"; indent-tabs-mode:nil; -*- */
//
// Copyright (c) 2006 Georgia Tech Research Corporation
//
// This program is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License version 2 as
// published by the Free Software Foundation;
//
// This program is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with this program; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
//
// Author: Rajib Bhattacharjea<raj.b@gatech.edu>
// Author: Hadi Arbabi<marbabi@cs.odu.edu>
//
#include <iostream>
#include <math.h>
#include <stdlib.h>
#include <sys/time.h> // for gettimeofday
#include <unistd.h>
#include <iostream>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <sstream>
#include "assert.h"
#include "config.h"
#include "integer.h"
#include "random-variable.h"
#include "rng-stream.h"
#include "fatal-error.h"
using namespace std;
namespace ns3{
//-----------------------------------------------------------------------------
// Seed Manager
//-----------------------------------------------------------------------------
uint32_t SeedManager::GetSeed()
{
uint32_t s[6];
RngStream::GetPackageSeed (s);
NS_ASSERT(
s[0] == s[1] &&
s[0] == s[2] &&
s[0] == s[3] &&
s[0] == s[4] &&
s[0] == s[5]
);
return s[0];
}
void SeedManager::SetSeed(uint32_t seed)
{
Config::SetGlobal("RngSeed", IntegerValue(seed));
}
void SeedManager::SetRun(uint32_t run)
{
Config::SetGlobal("RngRun", IntegerValue(run));
}
uint32_t SeedManager::GetRun()
{
return RngStream::GetPackageRun ();
}
bool SeedManager::CheckSeed (uint32_t seed)
{
return RngStream::CheckSeed(seed);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// RandomVariableBase methods
class RandomVariableBase
{
public:
RandomVariableBase ();
RandomVariableBase (const RandomVariableBase &o);
virtual ~RandomVariableBase();
virtual double GetValue() = 0;
virtual uint32_t GetInteger();
virtual RandomVariableBase* Copy(void) const = 0;
protected:
RngStream* m_generator; //underlying generator being wrapped
};
RandomVariableBase::RandomVariableBase()
: m_generator(NULL)
{
}
RandomVariableBase::RandomVariableBase(const RandomVariableBase& r)
:m_generator(0)
{
if (r.m_generator)
{
m_generator = new RngStream(*r.m_generator);
}
}
RandomVariableBase::~RandomVariableBase()
{
delete m_generator;
}
uint32_t RandomVariableBase::GetInteger()
{
return (uint32_t)GetValue();
}
//-------------------------------------------------------
RandomVariable::RandomVariable()
: m_variable (0)
{}
RandomVariable::RandomVariable(const RandomVariable&o)
: m_variable (o.m_variable->Copy ())
{}
RandomVariable::RandomVariable (const RandomVariableBase &variable)
: m_variable (variable.Copy ())
{}
RandomVariable &
RandomVariable::operator = (const RandomVariable &o)
{
if (&o == this)
{
return *this;
}
delete m_variable;
m_variable = o.m_variable->Copy ();
return *this;
}
RandomVariable::~RandomVariable()
{
delete m_variable;
}
double
RandomVariable::GetValue (void) const
{
return m_variable->GetValue ();
}
uint32_t
RandomVariable::GetInteger (void) const
{
return m_variable->GetInteger ();
}
RandomVariableBase *
RandomVariable::Peek (void) const
{
return m_variable;
}
ATTRIBUTE_VALUE_IMPLEMENT (RandomVariable);
ATTRIBUTE_CHECKER_IMPLEMENT (RandomVariable);
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// UniformVariableImpl
class UniformVariableImpl : public RandomVariableBase {
public:
/**
* Creates a uniform random number generator in the
* range [0.0 .. 1.0).
*/
UniformVariableImpl();
/**
* Creates a uniform random number generator with the specified range
* \param s Low end of the range
* \param l High end of the range
*/
UniformVariableImpl(double s, double l);
UniformVariableImpl(const UniformVariableImpl& c);
double GetMin (void) const;
double GetMax (void) const;
/**
* \return A value between low and high values specified by the constructor
*/
virtual double GetValue();
/**
* \return A value between low and high values specified by parameters
*/
virtual double GetValue(double s, double l);
virtual RandomVariableBase* Copy(void) const;
private:
double m_min;
double m_max;
};
UniformVariableImpl::UniformVariableImpl()
: m_min(0), m_max(1.0) { }
UniformVariableImpl::UniformVariableImpl(double s, double l)
: m_min(s), m_max(l) { }
UniformVariableImpl::UniformVariableImpl(const UniformVariableImpl& c)
: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max) { }
double
UniformVariableImpl::GetMin (void) const
{
return m_min;
}
double
UniformVariableImpl::GetMax (void) const
{
return m_max;
}
double UniformVariableImpl::GetValue()
{
if(!m_generator)
{
m_generator = new RngStream();
}
return m_min + m_generator->RandU01() * (m_max - m_min);
}
double UniformVariableImpl::GetValue(double s, double l)
{
if(!m_generator)
{
m_generator = new RngStream();
}
return s + m_generator->RandU01() * (l-s);
}
RandomVariableBase* UniformVariableImpl::Copy() const
{
return new UniformVariableImpl(*this);
}
UniformVariable::UniformVariable()
: RandomVariable (UniformVariableImpl ())
{}
UniformVariable::UniformVariable(double s, double l)
: RandomVariable (UniformVariableImpl (s, l))
{}
double UniformVariable::GetValue()
{
return Peek()->GetValue();
}
double UniformVariable::GetValue(double s, double l)
{
return ((UniformVariableImpl*)Peek())->GetValue(s,l);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ConstantVariableImpl methods
class ConstantVariableImpl : public RandomVariableBase {
public:
/**
* Construct a ConstantVariableImpl RNG that returns zero every sample
*/
ConstantVariableImpl();
/**
* Construct a ConstantVariableImpl RNG that returns the specified value
* every sample.
* \param c Unchanging value for this RNG.
*/
ConstantVariableImpl(double c);
ConstantVariableImpl(const ConstantVariableImpl& c) ;
/**
* \brief Specify a new constant RNG for this generator.
* \param c New constant value for this RNG.
*/
void NewConstant(double c);
/**
* \return The constant value specified
*/
virtual double GetValue();
virtual uint32_t GetInteger();
virtual RandomVariableBase* Copy(void) const;
private:
double m_const;
};
ConstantVariableImpl::ConstantVariableImpl()
: m_const(0) { }
ConstantVariableImpl::ConstantVariableImpl(double c)
: m_const(c) { };
ConstantVariableImpl::ConstantVariableImpl(const ConstantVariableImpl& c)
: RandomVariableBase(c), m_const(c.m_const) { }
void ConstantVariableImpl::NewConstant(double c)
{ m_const = c;}
double ConstantVariableImpl::GetValue()
{
return m_const;
}
uint32_t ConstantVariableImpl::GetInteger()
{
return (uint32_t)m_const;
}
RandomVariableBase* ConstantVariableImpl::Copy() const
{
return new ConstantVariableImpl(*this);
}
ConstantVariable::ConstantVariable()
: RandomVariable (ConstantVariableImpl ())
{}
ConstantVariable::ConstantVariable(double c)
: RandomVariable (ConstantVariableImpl (c))
{}
void
ConstantVariable::SetConstant(double c)
{
*this = ConstantVariable (c);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// SequentialVariableImpl methods
class SequentialVariableImpl : public RandomVariableBase {
public:
/**
* \brief Constructor for the SequentialVariableImpl RNG.
*
* The four parameters define the sequence. For example
* SequentialVariableImpl(0,5,1,2) creates a RNG that has the sequence
* 0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 0, 0 ...
* \param f First value of the sequence.
* \param l One more than the last value of the sequence.
* \param i Increment between sequence values
* \param c Number of times each member of the sequence is repeated
*/
SequentialVariableImpl(double f, double l, double i = 1, uint32_t c = 1);
/**
* \brief Constructor for the SequentialVariableImpl RNG.
*
* Differs from the first only in that the increment parameter is a
* random variable
* \param f First value of the sequence.
* \param l One more than the last value of the sequence.
* \param i Reference to a RandomVariableBase for the sequence increment
* \param c Number of times each member of the sequence is repeated
*/
SequentialVariableImpl(double f, double l, const RandomVariable& i, uint32_t c = 1);
SequentialVariableImpl(const SequentialVariableImpl& c);
~SequentialVariableImpl();
/**
* \return The next value in the Sequence
*/
virtual double GetValue();
virtual RandomVariableBase* Copy(void) const;
private:
double m_min;
double m_max;
RandomVariable m_increment;
uint32_t m_consecutive;
double m_current;
uint32_t m_currentConsecutive;
};
SequentialVariableImpl::SequentialVariableImpl(double f, double l, double i, uint32_t c)
: m_min(f), m_max(l), m_increment(ConstantVariable(i)), m_consecutive(c),
m_current(f), m_currentConsecutive(0)
{}
SequentialVariableImpl::SequentialVariableImpl(double f, double l, const RandomVariable& i, uint32_t c)
: m_min(f), m_max(l), m_increment(i), m_consecutive(c),
m_current(f), m_currentConsecutive(0)
{}
SequentialVariableImpl::SequentialVariableImpl(const SequentialVariableImpl& c)
: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max),
m_increment(c.m_increment), m_consecutive(c.m_consecutive),
m_current(c.m_current), m_currentConsecutive(c.m_currentConsecutive)
{}
SequentialVariableImpl::~SequentialVariableImpl()
{}
double SequentialVariableImpl::GetValue()
{ // Return a sequential series of values
double r = m_current;
if (++m_currentConsecutive == m_consecutive)
{ // Time to advance to next
m_currentConsecutive = 0;
m_current += m_increment.GetValue();
if (m_current >= m_max)
m_current = m_min + (m_current - m_max);
}
return r;
}
RandomVariableBase* SequentialVariableImpl::Copy() const
{
return new SequentialVariableImpl(*this);
}
SequentialVariable::SequentialVariable(double f, double l, double i, uint32_t c)
: RandomVariable (SequentialVariableImpl (f, l, i, c))
{}
SequentialVariable::SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c)
: RandomVariable (SequentialVariableImpl (f, l, i, c))
{}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ExponentialVariableImpl methods
class ExponentialVariableImpl : public RandomVariableBase {
public:
/**
* Constructs an exponential random variable with a mean
* value of 1.0.
*/
ExponentialVariableImpl();
/**
* \brief Constructs an exponential random variable with a specified mean
* \param m Mean value for the random variable
*/
explicit ExponentialVariableImpl(double m);
/**
* \brief Constructs an exponential random variable with spefified
* \brief mean and upper limit.
*
* Since exponential distributions can theoretically return unbounded values,
* it is sometimes useful to specify a fixed upper limit. Note however when
* the upper limit is specified, the true mean of the distribution is
* slightly smaller than the mean value specified.
* \param m Mean value of the random variable
* \param b Upper bound on returned values
*/
ExponentialVariableImpl(double m, double b);
ExponentialVariableImpl(const ExponentialVariableImpl& c);
/**
* \return A random value from this exponential distribution
*/
virtual double GetValue();
virtual RandomVariableBase* Copy(void) const;
private:
double m_mean; // Mean value of RV
double m_bound; // Upper bound on value (if non-zero)
};
ExponentialVariableImpl::ExponentialVariableImpl()
: m_mean(1.0), m_bound(0) { }
ExponentialVariableImpl::ExponentialVariableImpl(double m)
: m_mean(m), m_bound(0) { }
ExponentialVariableImpl::ExponentialVariableImpl(double m, double b)
: m_mean(m), m_bound(b) { }
ExponentialVariableImpl::ExponentialVariableImpl(const ExponentialVariableImpl& c)
: RandomVariableBase(c), m_mean(c.m_mean), m_bound(c.m_bound) { }
double ExponentialVariableImpl::GetValue()
{
if(!m_generator)
{
m_generator = new RngStream();
}
while(1)
{
double r = -m_mean*log(m_generator->RandU01());
if (m_bound == 0 || r <= m_bound) return r;
//otherwise, try again
}
}
RandomVariableBase* ExponentialVariableImpl::Copy() const
{
return new ExponentialVariableImpl(*this);
}
ExponentialVariable::ExponentialVariable()
: RandomVariable (ExponentialVariableImpl ())
{}
ExponentialVariable::ExponentialVariable(double m)
: RandomVariable (ExponentialVariableImpl (m))
{}
ExponentialVariable::ExponentialVariable(double m, double b)
: RandomVariable (ExponentialVariableImpl (m, b))
{}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ParetoVariableImpl methods
class ParetoVariableImpl : public RandomVariableBase {
public:
/**
* Constructs a pareto random variable with a mean of 1 and a shape
* parameter of 1.5
*/
ParetoVariableImpl();
/**
* Constructs a pareto random variable with specified mean and shape
* parameter of 1.5
* \param m Mean value of the distribution
*/
explicit ParetoVariableImpl(double m);
/**
* Constructs a pareto random variable with the specified mean value and
* shape parameter.
* \param m Mean value of the distribution
* \param s Shape parameter for the distribution
*/
ParetoVariableImpl(double m, double s);
/**
* \brief Constructs a pareto random variable with the specified mean
* \brief value, shape (alpha), and upper bound.
*
* Since pareto distributions can theoretically return unbounded values,
* it is sometimes useful to specify a fixed upper limit. Note however
* when the upper limit is specified, the true mean of the distribution
* is slightly smaller than the mean value specified.
* \param m Mean value
* \param s Shape parameter
* \param b Upper limit on returned values
*/
ParetoVariableImpl(double m, double s, double b);
ParetoVariableImpl(const ParetoVariableImpl& c);
/**
* \return A random value from this Pareto distribution
*/
virtual double GetValue();
virtual RandomVariableBase* Copy() const;
private:
double m_mean; // Mean value of RV
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;
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);
}
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))
{}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// LogNormalVariableImpl
class LogNormalVariableImpl : public RandomVariableBase {
public:
/**
* \param mu mu parameter of the lognormal distribution
* \param sigma sigma parameter of the lognormal distribution
*/
LogNormalVariableImpl (double mu, double sigma);
/**
* \return A random value from this distribution
*/
virtual double GetValue ();
virtual RandomVariableBase* Copy(void) const;
private:
double m_mu;
double m_sigma;
};
RandomVariableBase* LogNormalVariableImpl::Copy () const
{
return new LogNormalVariableImpl (m_mu, m_sigma);
}
LogNormalVariableImpl::LogNormalVariableImpl (double mu, double sigma)
:m_mu(mu), m_sigma(sigma)
{
}
// The code from this function was adapted from the GNU Scientific
// Library 1.8:
/* randist/lognormal.c
*
* Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or (at
* your option) any later version.
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*/
/* The lognormal distribution has the form
p(x) dx = 1/(x * sqrt(2 pi sigma^2)) exp(-(ln(x) - zeta)^2/2 sigma^2) dx
for x > 0. Lognormal random numbers are the exponentials of
gaussian random numbers */
double
LogNormalVariableImpl::GetValue ()
{
if(!m_generator)
{
m_generator = new RngStream();
}
double u, v, r2, normal, z;
do
{
/* choose x,y in uniform square (-1,-1) to (+1,+1) */
u = -1 + 2 * m_generator->RandU01 ();
v = -1 + 2 * m_generator->RandU01 ();
/* see if it is in the unit circle */
r2 = u * u + v * v;
}
while (r2 > 1.0 || r2 == 0);
normal = u * sqrt (-2.0 * log (r2) / r2);
z = exp (m_sigma * normal + m_mu);
return z;
}
LogNormalVariable::LogNormalVariable (double mu, double sigma)
: RandomVariable (LogNormalVariableImpl (mu, sigma))
{}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// TriangularVariableImpl methods
class TriangularVariableImpl : public RandomVariableBase {
public:
/**
* Creates a triangle distribution random number generator in the
* range [0.0 .. 1.0), with mean of 0.5
*/
TriangularVariableImpl();
/**
* Creates a triangle distribution random number generator with the specified
* range
* \param s Low end of the range
* \param l High end of the range
* \param mean mean of the distribution
*/
TriangularVariableImpl(double s, double l, double mean);
TriangularVariableImpl(const TriangularVariableImpl& c);
/**
* \return A value from this distribution
*/
virtual double GetValue();
virtual RandomVariableBase* Copy(void) const;
private:
double m_min;
double m_max;
double m_mode; //easier to work with the mode internally instead of the mean
//they are related by the simple: mean = (min+max+mode)/3
};
TriangularVariableImpl::TriangularVariableImpl()
: m_min(0), m_max(1), m_mode(0.5) { }
TriangularVariableImpl::TriangularVariableImpl(double s, double l, double mean)
: m_min(s), m_max(l), m_mode(3.0*mean-s-l) { }
TriangularVariableImpl::TriangularVariableImpl(const TriangularVariableImpl& c)
: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max), m_mode(c.m_mode) { }
double TriangularVariableImpl::GetValue()
{
if(!m_generator)
{
m_generator = new RngStream();
}
double u = m_generator->RandU01();
if(u <= (m_mode - m_min) / (m_max - m_min) )
{
return m_min + sqrt(u * (m_max - m_min) * (m_mode - m_min) );
}
else
{
return m_max - sqrt( (1-u) * (m_max - m_min) * (m_max - m_mode) );
}
}
RandomVariableBase* TriangularVariableImpl::Copy() const
{
return new TriangularVariableImpl(*this);
}
TriangularVariable::TriangularVariable()
: RandomVariable (TriangularVariableImpl ())
{}
TriangularVariable::TriangularVariable(double s, double l, double mean)
: RandomVariable (TriangularVariableImpl (s,l,mean))
{}
std::ostream &operator << (std::ostream &os, const RandomVariable &var)
{
RandomVariableBase *base = var.Peek ();
ConstantVariableImpl *constant = dynamic_cast<ConstantVariableImpl *> (base);
if (constant != 0)
{
os << "Constant:" << constant->GetValue ();
return os;
}
UniformVariableImpl *uniform = dynamic_cast<UniformVariableImpl *> (base);
if (uniform != 0)
{
os << "Uniform:" << uniform->GetMin () << ":" << uniform->GetMax ();
return os;
}
// XXX: support other distributions
os.setstate (std::ios_base::badbit);
return os;
}
std::istream &operator >> (std::istream &is, RandomVariable &var)
{
std::string value;
is >> value;
std::string::size_type tmp;
tmp = value.find (":");
if (tmp == std::string::npos)
{
is.setstate (std::ios_base::badbit);
return is;
}
std::string type = value.substr (0, tmp);
value = value.substr (tmp + 1, value.npos);
if (type == "Constant")
{
istringstream iss (value);
double constant;
iss >> constant;
var = ConstantVariable (constant);
}
else if (type == "Uniform")
{
if (value.size () == 0)
{
var = UniformVariable ();
}
else
{
tmp = value.find (":");
if (tmp == value.npos)
{
NS_FATAL_ERROR ("bad Uniform value: " << value);
}
istringstream issA (value.substr (0, tmp));
istringstream issB (value.substr (tmp + 1, value.npos));
double a, b;
issA >> a;
issB >> b;
var = UniformVariable (a, b);
}
}
else
{
NS_FATAL_ERROR ("RandomVariable deserialization not implemented for " << type);
// XXX: support other distributions.
}
return is;
}
}//namespace ns3