/* -*- 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 (PValue value);

  operator PValue () 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();