/* -*- 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>
//
#ifndef __random_variable_h__
#define __random_variable_h__
#include <vector>
#include <algorithm>
#include <stdint.h>
#include <istream>
#include <ostream>
#include "value.h"
#include "class-value-helper.h"
/**
* \ingroup core
* \defgroup randomvariable Random Variable Distributions
*
*/
namespace ns3{
class RandomVariableBase;
/**
* \brief The basic RNG for NS-3.
* \ingroup randomvariable
*
* Note: The underlying random number generation method used
* by NS-3 is the RngStream code by Pierre L'Ecuyer at
* the University of Montreal.
*
* NS-3 has a rich set of random number generators.
* Class RandomVariableBase defines the base class functionalty
* required for all random number generators. By default, the underlying
* generator is seeded with the time of day, and then deterministically
* creates a sequence of seeds for each subsequent generator that is created.
* The rest of the documentation outlines how to change this behavior.
*/
class RandomVariable
{
public:
RandomVariable();
RandomVariable(const RandomVariable&o);
RandomVariable &operator = (const RandomVariable &o);
~RandomVariable();
/**
* \brief Returns a random double from the underlying distribution
* \return A floating point random value
*/
double GetValue (void) const;
/**
* \brief Returns a random integer integer from the underlying distribution
* \return Integer cast of ::GetValue()
*/
uint32_t GetIntValue (void) const;
/**
* \brief Get the internal state of the RNG
*
* This function is for power users who understand the inner workings
* of the underlying RngStream method used. It returns the internal
* state of the RNG via the input parameter.
* \param seed Output parameter; gets overwritten with the internal state of
* of the RNG.
*/
void GetSeed(uint32_t seed[6]) const;
/**
* \brief Set seeding behavior
*
* Specify whether the POSIX device /dev/random is to
* be used for seeding. When this is used, the underlying
* generator is seeded with data from /dev/random instead of
* being seeded based upon the time of day. For this to be effective,
* it must be called before the creation of the first instance of a
* RandomVariableBase or subclass. Example:
* \code
* RandomVariable::UseDevRandom();
* UniformVariable x(2,3); //these are seeded randomly
* ExponentialVariable y(120); //etc
* \endcode
* \param udr True if /dev/random desired.
*/
static void UseDevRandom(bool udr = true);
/**
* \brief Use the global seed to force precisely reproducible results.
*
* It is often desirable to create a simulation that uses random
* numbers, while at the same time is completely reproducible.
* Specifying this set of six random seeds initializes the
* random number generator with the specified seed.
* Once this is set, all generators will produce fixed output
* from run to run. This is because each time a new generator is created,
* the underlying RngStream deterministically creates a new seed based upon
* the old one, hence a "stream" of RNGs. Example:
* \code
* RandomVariable::UseGlobalSeed(...);
* UniformVariable x(2,3); //these will give the same output everytime
* ExponentialVariable y(120); //as long as the seed stays the same
* \endcode
* \param s0
* \param s1
* \param s2
* \param s3
* \param s4
* \param s5
* \return True if seed is valid.
*/
static void UseGlobalSeed(uint32_t s0, uint32_t s1, uint32_t s2,
uint32_t s3, uint32_t s4, uint32_t s5);
/**
* \brief Set the run number of this simulation
*
* These RNGs have the ability to give independent sets of trials for a fixed
* global seed. For example, suppose one sets up a simulation with
* RandomVariables with a given global seed. Suppose the user wanted to
* retry the same simulation with different random values for validity,
* statistical rigor, etc. The user could either change the global seed and
* re-run the simulation, or could use this facility to increment all of the
* RNGs to a next substream state. This predictably advances the internal
* state of all RandomVariables n steps. This should be called immediately
* after the global seed is set, and before the creation of any
* RandomVariables. For example:
* \code
* RandomVariable::UseGlobalSeed(1,2,3,4,5,6);
* int N = atol(argv[1]); //read in run number from command line
* RandomVariable::SetRunNumber(N);
* UniformVariable x(0,10);
* ExponentialVariable y(2902);
* \endcode
* In this example, N could successivly be equal to 1,2,3, etc. and the user
* would continue to get independent runs out of the single simulation. For
* this simple example, the following might work:
* \code
* ./simulation 0
* ...Results for run 0:...
*
* ./simulation 1
* ...Results for run 1:...
* \endcode
*/
static void SetRunNumber(uint32_t n);
RandomVariable (Attribute value);
operator Attribute () const;
private:
friend class RandomVariableValue;
friend std::ostream &operator << (std::ostream &os, const RandomVariable &var);
friend std::istream &operator >> (std::istream &os, RandomVariable &var);
RandomVariableBase *m_variable;
protected:
RandomVariable (const RandomVariableBase &variable);
RandomVariableBase *Peek (void) const;
};
/**
* \brief The uniform distribution RNG for NS-3.
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
* from a fixed uniform distribution. It also supports the generation of
* single random numbers from various uniform distributions.
*
* The low end of the range is always included and the high end
* of the range is always excluded.
* \code
* UniformVariable x(0,10);
* x.GetValue(); //will always return numbers [0,10)
* UniformVariable::GetSingleValue(100,1000); //returns a value [100,1000)
* \endcode
*/
class UniformVariable : public RandomVariable
{
public:
/**
* Creates a uniform random number generator in the
* range [0.0 .. 1.0).
*/
UniformVariable();
/**
* Creates a uniform random number generator with the specified range
* \param s Low end of the range
* \param l High end of the range
*/
UniformVariable(double s, double l);
public:
/**
* \param s Low end of the range
* \param l High end of the range
* \return A uniformly distributed random number between s and l
*/
static double GetSingleValue(double s, double l);
};
/**
* \brief A random variable that returns a constant
* \ingroup randomvariable
*
* Class ConstantVariableImpl defines a random number generator that
* returns the same value every sample.
*/
class ConstantVariable : public RandomVariable {
public:
/**
* Construct a ConstantVariableImpl RNG that returns zero every sample
*/
ConstantVariable();
/**
* Construct a ConstantVariableImpl RNG that returns the specified value
* every sample.
* \param c Unchanging value for this RNG.
*/
ConstantVariable(double c);
/**
* \brief Specify a new constant RNG for this generator.
* \param c New constant value for this RNG.
*/
void SetConstant(double c);
};
/**
* \brief Return a sequential list of values
* \ingroup randomvariable
*
* Class SequentialVariable defines a random number generator that
* returns a sequential sequence. The sequence monotonically
* increases for a period, then wraps around to the low value
* and begins monotonicaly increasing again.
*/
class SequentialVariable : public RandomVariable
{
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
*/
SequentialVariable(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
*/
SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c = 1);
};
/**
* \brief Exponentially Distributed random var
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
* from a fixed exponential distribution. It also supports the generation of
* single random numbers from various exponential distributions.
*
* The probability density function of an exponential variable
* is defined over the interval [0, +inf) as:
* \f$ \alpha e^{-\alpha x} \f$
* where \f$ \alpha = \frac{1}{mean} \f$
*
* The bounded version is defined over the internal [0,+inf) as:
* \f$ \left\{ \begin{array}{cl} \alpha e^{-\alpha x} & x < bound \\ bound & x > bound \end{array}\right. \f$
*
* \code
* ExponentialVariableImpl x(3.14);
* x.GetValue(); //will always return with mean 3.14
* ExponentialVariableImpl::GetSingleValue(20.1); //returns with mean 20.1
* ExponentialVariableImpl::GetSingleValue(108); //returns with mean 108
* \endcode
*
*/
class ExponentialVariable : public RandomVariable
{
public:
/**
* Constructs an exponential random variable with a mean
* value of 1.0.
*/
ExponentialVariable();
/**
* \brief Constructs an exponential random variable with a specified mean
* \param m Mean value for the random variable
*/
explicit ExponentialVariable(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
*/
ExponentialVariable(double m, double b);
/**
* \param m The mean of the distribution from which the return value is drawn
* \param b The upper bound value desired, beyond which values get clipped
* \return A random number from an exponential distribution with mean m
*/
static double GetSingleValue(double m, double b=0);
};
/**
* \brief ParetoVariableImpl distributed random var
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
* from a fixed pareto distribution. It also supports the generation of
* single random numbers from various pareto distributions.
*
* The probability density function is defined over the range [\f$x_m\f$,+inf) as:
* \f$ k \frac{x_m^k}{x^{k+1}}\f$ where \f$x_m > 0\f$ is called the location
* parameter and \f$ k > 0\f$ is called the pareto index or shape.
*
* The parameter \f$ x_m \f$ can be infered from the mean and the parameter \f$ k \f$
* with the equation \f$ x_m = mean \frac{k-1}{k}, k > 1\f$.
*
* \code
* ParetoVariableImpl x(3.14);
* x.GetValue(); //will always return with mean 3.14
* ParetoVariableImpl::GetSingleValue(20.1); //returns with mean 20.1
* ParetoVariableImpl::GetSingleValue(108); //returns with mean 108
* \endcode
*/
class ParetoVariable : public RandomVariable
{
public:
/**
* Constructs a pareto random variable with a mean of 1 and a shape
* parameter of 1.5
*/
ParetoVariable ();
/**
* Constructs a pareto random variable with specified mean and shape
* parameter of 1.5
* \param m Mean value of the distribution
*/
explicit ParetoVariable(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
*/
ParetoVariable(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
*/
ParetoVariable(double m, double s, double b);
/**
* \param m The mean value of the distribution from which the return value
* is drawn.
* \param s The shape parameter of the distribution from which the return
* value is drawn.
* \param b The upper bound to which to restrict return values
* \return A random number from a Pareto distribution with mean m and shape
* parameter s.
*/
static double GetSingleValue(double m, double s, double b=0);
};
/**
* \brief WeibullVariableImpl distributed random var
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
* from a fixed weibull distribution. It also supports the generation of
* single random numbers from various weibull distributions.
*
* The probability density function is defined over the interval [0, +inf]
* as: \f$ \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k} \f$
* where \f$ k > 0\f$ is the shape parameter and \f$ \lambda > 0\f$ is the scale parameter. The
* specified mean is related to the scale and shape parameters by the following relation:
* \f$ mean = \lambda\Gamma\left(1+\frac{1}{k}\right) \f$ where \f$ \Gamma \f$ is the Gamma function.
*/
class WeibullVariable : public RandomVariable {
public:
/**
* Constructs a weibull random variable with a mean
* value of 1.0 and a shape (alpha) parameter of 1
*/
WeibullVariable();
/**
* 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
*/
WeibullVariable(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.
*/
WeibullVariable(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
*/
WeibullVariable(double m, double s, double b);
/**
* \param m Mean value for the distribution.
* \param s Shape (alpha) parameter for the distribution.
* \param b Upper limit on returned values
* \return Random number from a distribution specified by m,s, and b
*/
static double GetSingleValue(double m, double s, double b=0);
};
/**
* \brief Class NormalVariableImpl defines a random variable with a
* normal (Gaussian) distribution.
* \ingroup randomvariable
*
* This class supports the creation of objects that return random numbers
* from a fixed normal distribution. It also supports the generation of
* single random numbers from various normal distributions.
*
* The density probability function is defined over the interval (-inf,+inf)
* as: \f$ \frac{1}{\sigma\sqrt{2\pi}} e^{-\frac{(x-\mu)^2}{s\sigma^2}}\f$
* where \f$ mean = \mu \f$ and \f$ variance = \sigma^2 \f$
*
*/
class NormalVariable : public RandomVariable
{
public:
static const double INFINITE_VALUE;
/**
* Constructs an normal random variable with a mean
* value of 0 and variance of 1.
*/
NormalVariable();
/**
* \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.
*/
NormalVariable(double m, double v, double b = INFINITE_VALUE);
/**
* \param m Mean value
* \param v Variance
* \param b Bound. The NormalVariableImpl is bounded within +-bound.
* \return A random number from a distribution specified by m,v, and b.
*/
static double GetSingleValue(double m, double v, double b = INFINITE_VALUE);
};
/**
* \brief EmpiricalVariableImpl distribution random var
* \ingroup randomvariable
*
* Defines a random variable that has a specified, empirical
* distribution. The distribution is specified by a
* series of calls to the CDF member function, specifying a
* value and the probability that the function value is less than
* the specified value. When values are requested,
* a uniform random variable is used to select a probabililty,
* and the return value is interpreted linerarly between the
* two appropriate points in the CDF
*/
class EmpiricalVariable : public RandomVariable {
public:
/**
* Constructor for the EmpiricalVariableImpl random variables.
*/
explicit EmpiricalVariable();
/**
* \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
*/
void CDF(double v, double c); // Value, prob <= Value
protected:
EmpiricalVariable (const RandomVariableBase &variable);
};
/**
* \brief Integer-based empirical distribution
* \ingroup randomvariable
*
* Defines an empirical distribution where all values are integers.
* Indentical to EmpiricalVariableImpl, but with slightly different
* interpolation between points.
*/
class IntEmpiricalVariable : public EmpiricalVariable
{
public:
IntEmpiricalVariable();
};
/**
* \brief a non-random variable
* \ingroup randomvariable
*
* Defines a random variable that has a specified, predetermined
* sequence. This would be useful when trying to force
* the RNG to return a known sequence, perhaps to
* compare NS-3 to some other simulator
*/
class DeterministicVariable : public RandomVariable
{
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 DeterministicVariable(double* d, uint32_t c);
};
/**
* \brief Log-normal Distributed random var
* \ingroup randomvariable
*
* LogNormalVariableImpl defines a random variable with log-normal
* distribution. If one takes the natural logarithm of random
* variable following the log-normal distribution, the obtained values
* follow a normal distribution.
* This class supports the creation of objects that return random numbers
* from a fixed lognormal distribution. It also supports the generation of
* single random numbers from various lognormal distributions.
*
* The probability density function is defined over the interval [0,+inf) as:
* \f$ \frac{1}{x\sigma\sqrt{2\pi}} e^{-\frac{(ln(x) - \mu)^2}{2\sigma^2}}\f$
* where \f$ mean = e^{\mu+\frac{\sigma^2}{2}} \f$ and
* \f$ variance = (e^{\sigma^2}-1)e^{2\mu+\sigma^2}\f$
*
* The \f$ \mu \f$ and \f$ \sigma \f$ parameters can be calculated from the mean
* and standard deviation with the following equations:
* \f$ \mu = ln(mean) - \frac{1}{2}ln\left(1+\frac{stddev}{mean^2}\right)\f$, and,
* \f$ \sigma = \sqrt{ln\left(1+\frac{stddev}{mean^2}\right)}\f$
*/
class LogNormalVariable : public RandomVariable
{
public:
/**
* \param mu mu parameter of the lognormal distribution
* \param sigma sigma parameter of the lognormal distribution
*/
LogNormalVariable (double mu, double sigma);
/**
* \param mu mu parameter of the underlying normal distribution
* \param sigma sigma parameter of the underlying normal distribution
* \return A random number from the distribution specified by mu and sigma
*/
static double GetSingleValue(double mu, double sigma);
};
/**
* \brief Triangularly Distributed random var
* \ingroup randomvariable
*
* This distribution is a triangular distribution. The probablility density
* is in the shape of a triangle.
*/
class TriangularVariable : public RandomVariable
{
public:
/**
* Creates a triangle distribution random number generator in the
* range [0.0 .. 1.0), with mean of 0.5
*/
TriangularVariable();
/**
* 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
*/
TriangularVariable(double s, double l, double mean);
/**
* \param s Low end of the range
* \param l High end of the range
* \param mean mean of the distribution
* \return A triangularly distributed random number between s and l
*/
static double GetSingleValue(double s, double l, double mean);
};
std::ostream &operator << (std::ostream &os, const RandomVariable &var);
std::istream &operator >> (std::istream &os, RandomVariable &var);
class RandomVariableValue : public Value {};
class RandomVariableParamSpec : public ParamSpec {};
template <typename T1>
Ptr<ParamSpec> MakeRandomVariableParamSpec (T1 a1);
template <typename T1, typename T2>
Ptr<ParamSpec> MakeRandomVariableParamSpec (T1 a1, T2 a2);
Ptr<AttributeChecker> MakeRandomVariableChecker (void);
}//namespace ns3
namespace ns3 {
template <typename T1>
Ptr<ParamSpec> MakeRandomVariableParamSpec (T1 a1)
{
return MakeClassValueHelperParamSpec<RandomVariable,
RandomVariableValue, RandomVariableParamSpec> (a1);
}
template <typename T1, typename T2>
Ptr<ParamSpec> MakeRandomVariableParamSpec (T1 a1, T2 a2)
{
return MakeClassValueHelperParamSpec<RandomVariable,
RandomVariableValue,RandomVariableParamSpec> (a1, a2);
}
} // namespace ns3
#endif