1733 lines
44 KiB
C++
1733 lines
44 KiB
C++
/* -*- Mode:C++; c-file-style:"gnu"; indent-tabs-mode:nil; -*- */
|
|
/*
|
|
* Copyright (c) 2006 Georgia Tech Research Corporation
|
|
* Copyright (c) 2011 Mathieu Lacage
|
|
*
|
|
* 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
|
|
*
|
|
* Authors: Rajib Bhattacharjea<raj.b@gatech.edu>
|
|
* Hadi Arbabi<marbabi@cs.odu.edu>
|
|
* Mathieu Lacage <mathieu.lacage@gmail.com>
|
|
*
|
|
* Modified by Mitch Watrous <watrous@u.washington.edu>
|
|
*
|
|
*/
|
|
#include "random-variable-stream.h"
|
|
#include "assert.h"
|
|
#include "boolean.h"
|
|
#include "double.h"
|
|
#include "integer.h"
|
|
#include "string.h"
|
|
#include "pointer.h"
|
|
#include "log.h"
|
|
#include "rng-stream.h"
|
|
#include "rng-seed-manager.h"
|
|
#include "unused.h"
|
|
#include <cmath>
|
|
#include <iostream>
|
|
#include <algorithm> // upper_bound
|
|
|
|
/**
|
|
* \file
|
|
* \ingroup randomvariable
|
|
* ns3::RandomVariableStream and related implementations
|
|
*/
|
|
|
|
namespace ns3 {
|
|
|
|
NS_LOG_COMPONENT_DEFINE ("RandomVariableStream");
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (RandomVariableStream);
|
|
|
|
TypeId
|
|
RandomVariableStream::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::RandomVariableStream")
|
|
.SetParent<Object> ()
|
|
.SetGroupName ("Core")
|
|
.AddAttribute ("Stream",
|
|
"The stream number for this RNG stream. -1 means \"allocate a stream automatically\". "
|
|
"Note that if -1 is set, Get will return -1 so that it is not possible to know which "
|
|
"value was automatically allocated.",
|
|
IntegerValue (-1),
|
|
MakeIntegerAccessor (&RandomVariableStream::SetStream,
|
|
&RandomVariableStream::GetStream),
|
|
MakeIntegerChecker<int64_t>())
|
|
.AddAttribute ("Antithetic", "Set this RNG stream to generate antithetic values",
|
|
BooleanValue (false),
|
|
MakeBooleanAccessor (&RandomVariableStream::SetAntithetic,
|
|
&RandomVariableStream::IsAntithetic),
|
|
MakeBooleanChecker ())
|
|
;
|
|
return tid;
|
|
}
|
|
|
|
RandomVariableStream::RandomVariableStream ()
|
|
: m_rng (0)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
RandomVariableStream::~RandomVariableStream ()
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
delete m_rng;
|
|
}
|
|
|
|
void
|
|
RandomVariableStream::SetAntithetic (bool isAntithetic)
|
|
{
|
|
NS_LOG_FUNCTION (this << isAntithetic);
|
|
m_isAntithetic = isAntithetic;
|
|
}
|
|
bool
|
|
RandomVariableStream::IsAntithetic (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_isAntithetic;
|
|
}
|
|
void
|
|
RandomVariableStream::SetStream (int64_t stream)
|
|
{
|
|
NS_LOG_FUNCTION (this << stream);
|
|
// negative values are not legal.
|
|
NS_ASSERT (stream >= -1);
|
|
delete m_rng;
|
|
if (stream == -1)
|
|
{
|
|
// The first 2^63 streams are reserved for automatic stream
|
|
// number assignment.
|
|
uint64_t nextStream = RngSeedManager::GetNextStreamIndex ();
|
|
NS_ASSERT (nextStream <= ((1ULL) << 63));
|
|
m_rng = new RngStream (RngSeedManager::GetSeed (),
|
|
nextStream,
|
|
RngSeedManager::GetRun ());
|
|
}
|
|
else
|
|
{
|
|
// The last 2^63 streams are reserved for deterministic stream
|
|
// number assignment.
|
|
uint64_t base = ((1ULL) << 63);
|
|
uint64_t target = base + stream;
|
|
m_rng = new RngStream (RngSeedManager::GetSeed (),
|
|
target,
|
|
RngSeedManager::GetRun ());
|
|
}
|
|
m_stream = stream;
|
|
}
|
|
int64_t
|
|
RandomVariableStream::GetStream (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_stream;
|
|
}
|
|
|
|
RngStream *
|
|
RandomVariableStream::Peek (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_rng;
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (UniformRandomVariable);
|
|
|
|
TypeId
|
|
UniformRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::UniformRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<UniformRandomVariable> ()
|
|
.AddAttribute ("Min", "The lower bound on the values returned by this RNG stream.",
|
|
DoubleValue (0),
|
|
MakeDoubleAccessor (&UniformRandomVariable::m_min),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Max", "The upper bound on the values returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&UniformRandomVariable::m_max),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
UniformRandomVariable::UniformRandomVariable ()
|
|
{
|
|
// m_min and m_max are initialized after constructor by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
UniformRandomVariable::GetMin (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_min;
|
|
}
|
|
double
|
|
UniformRandomVariable::GetMax (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_max;
|
|
}
|
|
|
|
double
|
|
UniformRandomVariable::GetValue (double min, double max)
|
|
{
|
|
NS_LOG_FUNCTION (this << min << max);
|
|
double v = min + Peek ()->RandU01 () * (max - min);
|
|
if (IsAntithetic ())
|
|
{
|
|
v = min + (max - v);
|
|
}
|
|
return v;
|
|
}
|
|
uint32_t
|
|
UniformRandomVariable::GetInteger (uint32_t min, uint32_t max)
|
|
{
|
|
NS_LOG_FUNCTION (this << min << max);
|
|
NS_ASSERT (min <= max);
|
|
return static_cast<uint32_t> ( GetValue ((double) (min), (double) (max) + 1.0) );
|
|
}
|
|
|
|
double
|
|
UniformRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_min, m_max);
|
|
}
|
|
uint32_t
|
|
UniformRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_min, m_max + 1);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (ConstantRandomVariable);
|
|
|
|
TypeId
|
|
ConstantRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::ConstantRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<ConstantRandomVariable> ()
|
|
.AddAttribute ("Constant", "The constant value returned by this RNG stream.",
|
|
DoubleValue (0),
|
|
MakeDoubleAccessor (&ConstantRandomVariable::m_constant),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
ConstantRandomVariable::ConstantRandomVariable ()
|
|
{
|
|
// m_constant is initialized after constructor by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
ConstantRandomVariable::GetConstant (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_constant;
|
|
}
|
|
|
|
double
|
|
ConstantRandomVariable::GetValue (double constant)
|
|
{
|
|
NS_LOG_FUNCTION (this << constant);
|
|
return constant;
|
|
}
|
|
uint32_t
|
|
ConstantRandomVariable::GetInteger (uint32_t constant)
|
|
{
|
|
NS_LOG_FUNCTION (this << constant);
|
|
return constant;
|
|
}
|
|
|
|
double
|
|
ConstantRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_constant);
|
|
}
|
|
uint32_t
|
|
ConstantRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_constant);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (SequentialRandomVariable);
|
|
|
|
TypeId
|
|
SequentialRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::SequentialRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<SequentialRandomVariable> ()
|
|
.AddAttribute ("Min", "The first value of the sequence.",
|
|
DoubleValue (0),
|
|
MakeDoubleAccessor (&SequentialRandomVariable::m_min),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Max", "One more than the last value of the sequence.",
|
|
DoubleValue (0),
|
|
MakeDoubleAccessor (&SequentialRandomVariable::m_max),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Increment", "The sequence random variable increment.",
|
|
StringValue ("ns3::ConstantRandomVariable[Constant=1]"),
|
|
MakePointerAccessor (&SequentialRandomVariable::m_increment),
|
|
MakePointerChecker<RandomVariableStream> ())
|
|
.AddAttribute ("Consecutive", "The number of times each member of the sequence is repeated.",
|
|
IntegerValue (1),
|
|
MakeIntegerAccessor (&SequentialRandomVariable::m_consecutive),
|
|
MakeIntegerChecker<uint32_t>());
|
|
return tid;
|
|
}
|
|
SequentialRandomVariable::SequentialRandomVariable ()
|
|
:
|
|
m_current (0),
|
|
m_currentConsecutive (0),
|
|
m_isCurrentSet (false)
|
|
{
|
|
// m_min, m_max, m_increment, and m_consecutive are initialized
|
|
// after constructor by attributes.
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
SequentialRandomVariable::GetMin (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_min;
|
|
}
|
|
|
|
double
|
|
SequentialRandomVariable::GetMax (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_max;
|
|
}
|
|
|
|
Ptr<RandomVariableStream>
|
|
SequentialRandomVariable::GetIncrement (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_increment;
|
|
}
|
|
|
|
uint32_t
|
|
SequentialRandomVariable::GetConsecutive (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_consecutive;
|
|
}
|
|
|
|
double
|
|
SequentialRandomVariable::GetValue (void)
|
|
{
|
|
// Set the current sequence value if it hasn't been set.
|
|
NS_LOG_FUNCTION (this);
|
|
if (!m_isCurrentSet)
|
|
{
|
|
// Start the sequence at its minimium value.
|
|
m_current = m_min;
|
|
m_isCurrentSet = true;
|
|
}
|
|
|
|
// 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;
|
|
}
|
|
|
|
uint32_t
|
|
SequentialRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue ();
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (ExponentialRandomVariable);
|
|
|
|
TypeId
|
|
ExponentialRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::ExponentialRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<ExponentialRandomVariable> ()
|
|
.AddAttribute ("Mean", "The mean of the values returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&ExponentialRandomVariable::m_mean),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Bound", "The upper bound on the values returned by this RNG stream.",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&ExponentialRandomVariable::m_bound),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
ExponentialRandomVariable::ExponentialRandomVariable ()
|
|
{
|
|
// m_mean and m_bound are initialized after constructor by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
ExponentialRandomVariable::GetMean (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_mean;
|
|
}
|
|
double
|
|
ExponentialRandomVariable::GetBound (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_bound;
|
|
}
|
|
|
|
double
|
|
ExponentialRandomVariable::GetValue (double mean, double bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << bound);
|
|
while (1)
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
double v = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
// Calculate the exponential random variable.
|
|
double r = -mean*std::log (v);
|
|
|
|
// Use this value if it's acceptable.
|
|
if (bound == 0 || r <= bound)
|
|
{
|
|
return r;
|
|
}
|
|
}
|
|
}
|
|
uint32_t
|
|
ExponentialRandomVariable::GetInteger (uint32_t mean, uint32_t bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << bound);
|
|
return static_cast<uint32_t> ( GetValue (mean, bound) );
|
|
}
|
|
|
|
double
|
|
ExponentialRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_mean, m_bound);
|
|
}
|
|
uint32_t
|
|
ExponentialRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_mean, m_bound);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (ParetoRandomVariable);
|
|
|
|
TypeId
|
|
ParetoRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::ParetoRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<ParetoRandomVariable> ()
|
|
.AddAttribute ("Scale", "The scale parameter for the Pareto distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&ParetoRandomVariable::m_scale),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Shape", "The shape parameter for the Pareto distribution returned by this RNG stream.",
|
|
DoubleValue (2.0),
|
|
MakeDoubleAccessor (&ParetoRandomVariable::m_shape),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Bound", "The upper bound on the values returned by this RNG stream (if non-zero).",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&ParetoRandomVariable::m_bound),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
ParetoRandomVariable::ParetoRandomVariable ()
|
|
{
|
|
// m_shape, m_shape, and m_bound are initialized after constructor
|
|
// by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
ParetoRandomVariable::GetScale (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_scale;
|
|
}
|
|
|
|
double
|
|
ParetoRandomVariable::GetShape (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_shape;
|
|
}
|
|
|
|
double
|
|
ParetoRandomVariable::GetBound (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_bound;
|
|
}
|
|
|
|
double
|
|
ParetoRandomVariable::GetValue (double scale, double shape, double bound)
|
|
{
|
|
// Calculate the scale parameter.
|
|
NS_LOG_FUNCTION (this << scale << shape << bound);
|
|
|
|
while (1)
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
double v = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
// Calculate the Pareto random variable.
|
|
double r = (scale * ( 1.0 / std::pow (v, 1.0 / shape)));
|
|
|
|
// Use this value if it's acceptable.
|
|
if (bound == 0 || r <= bound)
|
|
{
|
|
return r;
|
|
}
|
|
}
|
|
}
|
|
uint32_t
|
|
ParetoRandomVariable::GetInteger (uint32_t scale, uint32_t shape, uint32_t bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << scale << shape << bound);
|
|
return static_cast<uint32_t> ( GetValue (scale, shape, bound) );
|
|
}
|
|
|
|
double
|
|
ParetoRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_scale, m_shape, m_bound);
|
|
}
|
|
uint32_t
|
|
ParetoRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_scale, m_shape, m_bound);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (WeibullRandomVariable);
|
|
|
|
TypeId
|
|
WeibullRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::WeibullRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<WeibullRandomVariable> ()
|
|
.AddAttribute ("Scale", "The scale parameter for the Weibull distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&WeibullRandomVariable::m_scale),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Shape", "The shape parameter for the Weibull distribution returned by this RNG stream.",
|
|
DoubleValue (1),
|
|
MakeDoubleAccessor (&WeibullRandomVariable::m_shape),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Bound", "The upper bound on the values returned by this RNG stream.",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&WeibullRandomVariable::m_bound),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
WeibullRandomVariable::WeibullRandomVariable ()
|
|
{
|
|
// m_scale, m_shape, and m_bound are initialized after constructor
|
|
// by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetScale (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_scale;
|
|
}
|
|
double
|
|
WeibullRandomVariable::GetShape (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_shape;
|
|
}
|
|
double
|
|
WeibullRandomVariable::GetBound (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_bound;
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetValue (double scale, double shape, double bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << scale << shape << bound);
|
|
double exponent = 1.0 / shape;
|
|
while (1)
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
double v = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
// Calculate the Weibull random variable.
|
|
double r = scale * std::pow ( -std::log (v), exponent);
|
|
|
|
// Use this value if it's acceptable.
|
|
if (bound == 0 || r <= bound)
|
|
{
|
|
return r;
|
|
}
|
|
}
|
|
}
|
|
uint32_t
|
|
WeibullRandomVariable::GetInteger (uint32_t scale, uint32_t shape, uint32_t bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << scale << shape << bound);
|
|
return static_cast<uint32_t> ( GetValue (scale, shape, bound) );
|
|
}
|
|
|
|
double
|
|
WeibullRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_scale, m_shape, m_bound);
|
|
}
|
|
uint32_t
|
|
WeibullRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_scale, m_shape, m_bound);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (NormalRandomVariable);
|
|
|
|
const double NormalRandomVariable::INFINITE_VALUE = 1e307;
|
|
|
|
TypeId
|
|
NormalRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::NormalRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<NormalRandomVariable> ()
|
|
.AddAttribute ("Mean", "The mean value for the normal distribution returned by this RNG stream.",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&NormalRandomVariable::m_mean),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Variance", "The variance value for the normal distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&NormalRandomVariable::m_variance),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Bound", "The bound on the values returned by this RNG stream.",
|
|
DoubleValue (INFINITE_VALUE),
|
|
MakeDoubleAccessor (&NormalRandomVariable::m_bound),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
NormalRandomVariable::NormalRandomVariable ()
|
|
:
|
|
m_nextValid (false)
|
|
{
|
|
// m_mean, m_variance, and m_bound are initialized after constructor
|
|
// by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetMean (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_mean;
|
|
}
|
|
double
|
|
NormalRandomVariable::GetVariance (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_variance;
|
|
}
|
|
double
|
|
NormalRandomVariable::GetBound (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_bound;
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetValue (double mean, double variance, double bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << variance << bound);
|
|
if (m_nextValid)
|
|
{ // use previously generated
|
|
m_nextValid = false;
|
|
double x2 = mean + m_v2 * m_y * std::sqrt (variance);
|
|
if (std::fabs (x2 - mean) <= bound)
|
|
{
|
|
return x2;
|
|
}
|
|
}
|
|
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 = Peek ()->RandU01 ();
|
|
double u2 = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u1 = (1 - u1);
|
|
u2 = (1 - u2);
|
|
}
|
|
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 = std::sqrt ((-2 * std::log (w)) / w);
|
|
double x1 = mean + v1 * y * std::sqrt (variance);
|
|
// if x1 is in bounds, return it, cache v2 and y
|
|
if (std::fabs (x1 - mean) <= bound)
|
|
{
|
|
m_nextValid = true;
|
|
m_y = y;
|
|
m_v2 = v2;
|
|
return x1;
|
|
}
|
|
// otherwise try and return the other if it is valid
|
|
double x2 = mean + v2 * y * std::sqrt (variance);
|
|
if (std::fabs (x2 - mean) <= bound)
|
|
{
|
|
m_nextValid = false;
|
|
return x2;
|
|
}
|
|
// otherwise, just run this loop again
|
|
}
|
|
}
|
|
}
|
|
|
|
uint32_t
|
|
NormalRandomVariable::GetInteger (uint32_t mean, uint32_t variance, uint32_t bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << variance << bound);
|
|
return static_cast<uint32_t> ( GetValue (mean, variance, bound) );
|
|
}
|
|
|
|
double
|
|
NormalRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_mean, m_variance, m_bound);
|
|
}
|
|
uint32_t
|
|
NormalRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_mean, m_variance, m_bound);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (LogNormalRandomVariable);
|
|
|
|
TypeId
|
|
LogNormalRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::LogNormalRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<LogNormalRandomVariable> ()
|
|
.AddAttribute ("Mu", "The mu value for the log-normal distribution returned by this RNG stream.",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&LogNormalRandomVariable::m_mu),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Sigma", "The sigma value for the log-normal distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&LogNormalRandomVariable::m_sigma),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
LogNormalRandomVariable::LogNormalRandomVariable ()
|
|
{
|
|
// m_mu and m_sigma are initialized after constructor by
|
|
// attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
LogNormalRandomVariable::GetMu (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_mu;
|
|
}
|
|
double
|
|
LogNormalRandomVariable::GetSigma (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_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
|
|
LogNormalRandomVariable::GetValue (double mu, double sigma)
|
|
{
|
|
double v1, v2, r2, normal, x;
|
|
|
|
NS_LOG_FUNCTION (this << mu << sigma);
|
|
|
|
do
|
|
{
|
|
/* choose x,y in uniform square (-1,-1) to (+1,+1) */
|
|
|
|
double u1 = Peek ()->RandU01 ();
|
|
double u2 = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u1 = (1 - u1);
|
|
u2 = (1 - u2);
|
|
}
|
|
|
|
v1 = -1 + 2 * u1;
|
|
v2 = -1 + 2 * u2;
|
|
|
|
/* see if it is in the unit circle */
|
|
r2 = v1 * v1 + v2 * v2;
|
|
}
|
|
while (r2 > 1.0 || r2 == 0);
|
|
|
|
normal = v1 * std::sqrt (-2.0 * std::log (r2) / r2);
|
|
|
|
x = std::exp (sigma * normal + mu);
|
|
|
|
return x;
|
|
}
|
|
|
|
uint32_t
|
|
LogNormalRandomVariable::GetInteger (uint32_t mu, uint32_t sigma)
|
|
{
|
|
NS_LOG_FUNCTION (this << mu << sigma);
|
|
return static_cast<uint32_t> ( GetValue (mu, sigma));
|
|
}
|
|
|
|
double
|
|
LogNormalRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_mu, m_sigma);
|
|
}
|
|
uint32_t
|
|
LogNormalRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_mu, m_sigma);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (GammaRandomVariable);
|
|
|
|
TypeId
|
|
GammaRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::GammaRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<GammaRandomVariable> ()
|
|
.AddAttribute ("Alpha", "The alpha value for the gamma distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&GammaRandomVariable::m_alpha),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Beta", "The beta value for the gamma distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&GammaRandomVariable::m_beta),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
GammaRandomVariable::GammaRandomVariable ()
|
|
:
|
|
m_nextValid (false)
|
|
{
|
|
// m_alpha and m_beta are initialized after constructor by
|
|
// attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetAlpha (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_alpha;
|
|
}
|
|
double
|
|
GammaRandomVariable::GetBeta (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_beta;
|
|
}
|
|
|
|
/*
|
|
The code for the following generator functions was adapted from ns-2
|
|
tools/ranvar.cc
|
|
|
|
Originally the algorithm was devised by Marsaglia in 2000:
|
|
G. Marsaglia, W. W. Tsang: A simple method for generating Gamma variables
|
|
ACM Transactions on mathematical software, Vol. 26, No. 3, Sept. 2000
|
|
|
|
The Gamma distribution density function has the form
|
|
|
|
x^(alpha-1) * exp(-x/beta)
|
|
p(x; alpha, beta) = ----------------------------
|
|
beta^alpha * Gamma(alpha)
|
|
|
|
for x > 0.
|
|
*/
|
|
double
|
|
GammaRandomVariable::GetValue (double alpha, double beta)
|
|
{
|
|
NS_LOG_FUNCTION (this << alpha << beta);
|
|
if (alpha < 1)
|
|
{
|
|
double u = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
return GetValue (1.0 + alpha, beta) * std::pow (u, 1.0 / alpha);
|
|
}
|
|
|
|
double x, v, u;
|
|
double d = alpha - 1.0 / 3.0;
|
|
double c = (1.0 / 3.0) / std::sqrt (d);
|
|
|
|
while (1)
|
|
{
|
|
do
|
|
{
|
|
// Get a value from a normal distribution that has mean
|
|
// zero, variance 1, and no bound.
|
|
double mean = 0.0;
|
|
double variance = 1.0;
|
|
double bound = NormalRandomVariable::INFINITE_VALUE;
|
|
x = GetNormalValue (mean, variance, bound);
|
|
|
|
v = 1.0 + c * x;
|
|
}
|
|
while (v <= 0);
|
|
|
|
v = v * v * v;
|
|
u = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
if (u < 1 - 0.0331 * x * x * x * x)
|
|
{
|
|
break;
|
|
}
|
|
if (std::log (u) < 0.5 * x * x + d * (1 - v + std::log (v)))
|
|
{
|
|
break;
|
|
}
|
|
}
|
|
|
|
return beta * d * v;
|
|
}
|
|
|
|
uint32_t
|
|
GammaRandomVariable::GetInteger (uint32_t alpha, uint32_t beta)
|
|
{
|
|
NS_LOG_FUNCTION (this << alpha << beta);
|
|
return static_cast<uint32_t> ( GetValue (alpha, beta));
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_alpha, m_beta);
|
|
}
|
|
uint32_t
|
|
GammaRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_alpha, m_beta);
|
|
}
|
|
|
|
double
|
|
GammaRandomVariable::GetNormalValue (double mean, double variance, double bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << variance << bound);
|
|
if (m_nextValid)
|
|
{ // use previously generated
|
|
m_nextValid = false;
|
|
double x2 = mean + m_v2 * m_y * std::sqrt (variance);
|
|
if (std::fabs (x2 - mean) <= bound)
|
|
{
|
|
return x2;
|
|
}
|
|
}
|
|
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 = Peek ()->RandU01 ();
|
|
double u2 = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u1 = (1 - u1);
|
|
u2 = (1 - u2);
|
|
}
|
|
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 = std::sqrt ((-2 * std::log (w)) / w);
|
|
double x1 = mean + v1 * y * std::sqrt (variance);
|
|
// if x1 is in bounds, return it, cache v2 an y
|
|
if (std::fabs (x1 - mean) <= bound)
|
|
{
|
|
m_nextValid = true;
|
|
m_y = y;
|
|
m_v2 = v2;
|
|
return x1;
|
|
}
|
|
// otherwise try and return the other if it is valid
|
|
double x2 = mean + v2 * y * std::sqrt (variance);
|
|
if (std::fabs (x2 - mean) <= bound)
|
|
{
|
|
m_nextValid = false;
|
|
return x2;
|
|
}
|
|
// otherwise, just run this loop again
|
|
}
|
|
}
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (ErlangRandomVariable);
|
|
|
|
TypeId
|
|
ErlangRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::ErlangRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<ErlangRandomVariable> ()
|
|
.AddAttribute ("K", "The k value for the Erlang distribution returned by this RNG stream.",
|
|
IntegerValue (1),
|
|
MakeIntegerAccessor (&ErlangRandomVariable::m_k),
|
|
MakeIntegerChecker<uint32_t>())
|
|
.AddAttribute ("Lambda", "The lambda value for the Erlang distribution returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&ErlangRandomVariable::m_lambda),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
ErlangRandomVariable::ErlangRandomVariable ()
|
|
{
|
|
// m_k and m_lambda are initialized after constructor by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
uint32_t
|
|
ErlangRandomVariable::GetK (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_k;
|
|
}
|
|
double
|
|
ErlangRandomVariable::GetLambda (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_lambda;
|
|
}
|
|
|
|
/*
|
|
The code for the following generator functions was adapted from ns-2
|
|
tools/ranvar.cc
|
|
|
|
The Erlang distribution density function has the form
|
|
|
|
x^(k-1) * exp(-x/lambda)
|
|
p(x; k, lambda) = ---------------------------
|
|
lambda^k * (k-1)!
|
|
|
|
for x > 0.
|
|
*/
|
|
double
|
|
ErlangRandomVariable::GetValue (uint32_t k, double lambda)
|
|
{
|
|
NS_LOG_FUNCTION (this << k << lambda);
|
|
double mean = lambda;
|
|
double bound = 0.0;
|
|
|
|
double result = 0;
|
|
for (unsigned int i = 0; i < k; ++i)
|
|
{
|
|
result += GetExponentialValue (mean, bound);
|
|
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
uint32_t
|
|
ErlangRandomVariable::GetInteger (uint32_t k, uint32_t lambda)
|
|
{
|
|
NS_LOG_FUNCTION (this << k << lambda);
|
|
return static_cast<uint32_t> ( GetValue (k, lambda));
|
|
}
|
|
|
|
double
|
|
ErlangRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_k, m_lambda);
|
|
}
|
|
uint32_t
|
|
ErlangRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_k, m_lambda);
|
|
}
|
|
|
|
double
|
|
ErlangRandomVariable::GetExponentialValue (double mean, double bound)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << bound);
|
|
while (1)
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
double v = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
// Calculate the exponential random variable.
|
|
double r = -mean*std::log (v);
|
|
|
|
// Use this value if it's acceptable.
|
|
if (bound == 0 || r <= bound)
|
|
{
|
|
return r;
|
|
}
|
|
}
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (TriangularRandomVariable);
|
|
|
|
TypeId
|
|
TriangularRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::TriangularRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<TriangularRandomVariable> ()
|
|
.AddAttribute ("Mean", "The mean value for the triangular distribution returned by this RNG stream.",
|
|
DoubleValue (0.5),
|
|
MakeDoubleAccessor (&TriangularRandomVariable::m_mean),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Min", "The lower bound on the values returned by this RNG stream.",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&TriangularRandomVariable::m_min),
|
|
MakeDoubleChecker<double>())
|
|
.AddAttribute ("Max", "The upper bound on the values returned by this RNG stream.",
|
|
DoubleValue (1.0),
|
|
MakeDoubleAccessor (&TriangularRandomVariable::m_max),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
TriangularRandomVariable::TriangularRandomVariable ()
|
|
{
|
|
// m_mean, m_min, and m_max are initialized after constructor by
|
|
// attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetMean (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_mean;
|
|
}
|
|
double
|
|
TriangularRandomVariable::GetMin (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_min;
|
|
}
|
|
double
|
|
TriangularRandomVariable::GetMax (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_max;
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetValue (double mean, double min, double max)
|
|
{
|
|
// Calculate the mode.
|
|
NS_LOG_FUNCTION (this << mean << min << max);
|
|
double mode = 3.0 * mean - min - max;
|
|
|
|
// Get a uniform random variable in [0,1].
|
|
double u = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
|
|
// Calculate the triangular random variable.
|
|
if (u <= (mode - min) / (max - min) )
|
|
{
|
|
return min + std::sqrt (u * (max - min) * (mode - min) );
|
|
}
|
|
else
|
|
{
|
|
return max - std::sqrt ( (1 - u) * (max - min) * (max - mode) );
|
|
}
|
|
}
|
|
|
|
uint32_t
|
|
TriangularRandomVariable::GetInteger (uint32_t mean, uint32_t min, uint32_t max)
|
|
{
|
|
NS_LOG_FUNCTION (this << mean << min << max);
|
|
return static_cast<uint32_t> ( GetValue (mean, min, max) );
|
|
}
|
|
|
|
double
|
|
TriangularRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_mean, m_min, m_max);
|
|
}
|
|
uint32_t
|
|
TriangularRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_mean, m_min, m_max);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (ZipfRandomVariable);
|
|
|
|
TypeId
|
|
ZipfRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::ZipfRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<ZipfRandomVariable> ()
|
|
.AddAttribute ("N", "The n value for the Zipf distribution returned by this RNG stream.",
|
|
IntegerValue (1),
|
|
MakeIntegerAccessor (&ZipfRandomVariable::m_n),
|
|
MakeIntegerChecker<uint32_t>())
|
|
.AddAttribute ("Alpha", "The alpha value for the Zipf distribution returned by this RNG stream.",
|
|
DoubleValue (0.0),
|
|
MakeDoubleAccessor (&ZipfRandomVariable::m_alpha),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
ZipfRandomVariable::ZipfRandomVariable ()
|
|
{
|
|
// m_n and m_alpha are initialized after constructor by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
uint32_t
|
|
ZipfRandomVariable::GetN (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_n;
|
|
}
|
|
double
|
|
ZipfRandomVariable::GetAlpha (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_alpha;
|
|
}
|
|
|
|
double
|
|
ZipfRandomVariable::GetValue (uint32_t n, double alpha)
|
|
{
|
|
NS_LOG_FUNCTION (this << n << alpha);
|
|
// Calculate the normalization constant c.
|
|
m_c = 0.0;
|
|
for (uint32_t i = 1; i <= n; i++)
|
|
{
|
|
m_c += (1.0 / std::pow ((double)i,alpha));
|
|
}
|
|
m_c = 1.0 / m_c;
|
|
|
|
// Get a uniform random variable in [0,1].
|
|
double u = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
|
|
double sum_prob = 0,zipf_value = 0;
|
|
for (uint32_t i = 1; i <= m_n; i++)
|
|
{
|
|
sum_prob += m_c / std::pow ((double)i,m_alpha);
|
|
if (sum_prob > u)
|
|
{
|
|
zipf_value = i;
|
|
break;
|
|
}
|
|
}
|
|
return zipf_value;
|
|
}
|
|
|
|
uint32_t
|
|
ZipfRandomVariable::GetInteger (uint32_t n, uint32_t alpha)
|
|
{
|
|
NS_LOG_FUNCTION (this << n << alpha);
|
|
return static_cast<uint32_t> ( GetValue (n, alpha));
|
|
}
|
|
|
|
double
|
|
ZipfRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_n, m_alpha);
|
|
}
|
|
uint32_t
|
|
ZipfRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_n, m_alpha);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (ZetaRandomVariable);
|
|
|
|
TypeId
|
|
ZetaRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::ZetaRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<ZetaRandomVariable> ()
|
|
.AddAttribute ("Alpha", "The alpha value for the zeta distribution returned by this RNG stream.",
|
|
DoubleValue (3.14),
|
|
MakeDoubleAccessor (&ZetaRandomVariable::m_alpha),
|
|
MakeDoubleChecker<double>())
|
|
;
|
|
return tid;
|
|
}
|
|
ZetaRandomVariable::ZetaRandomVariable ()
|
|
{
|
|
// m_alpha is initialized after constructor by attributes
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
double
|
|
ZetaRandomVariable::GetAlpha (void) const
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return m_alpha;
|
|
}
|
|
|
|
double
|
|
ZetaRandomVariable::GetValue (double alpha)
|
|
{
|
|
NS_LOG_FUNCTION (this << alpha);
|
|
m_b = std::pow (2.0, alpha - 1.0);
|
|
|
|
double u, v;
|
|
double X, T;
|
|
double test;
|
|
|
|
do
|
|
{
|
|
// Get a uniform random variable in [0,1].
|
|
u = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
u = (1 - u);
|
|
}
|
|
|
|
// Get a uniform random variable in [0,1].
|
|
v = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
v = (1 - v);
|
|
}
|
|
|
|
X = std::floor (std::pow (u, -1.0 / (m_alpha - 1.0)));
|
|
T = std::pow (1.0 + 1.0 / X, m_alpha - 1.0);
|
|
test = v * X * (T - 1.0) / (m_b - 1.0);
|
|
}
|
|
while ( test > (T / m_b) );
|
|
|
|
return X;
|
|
}
|
|
|
|
uint32_t
|
|
ZetaRandomVariable::GetInteger (uint32_t alpha)
|
|
{
|
|
NS_LOG_FUNCTION (this << alpha);
|
|
return static_cast<uint32_t> ( GetValue (alpha));
|
|
}
|
|
|
|
double
|
|
ZetaRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return GetValue (m_alpha);
|
|
}
|
|
uint32_t
|
|
ZetaRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue (m_alpha);
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (DeterministicRandomVariable);
|
|
|
|
TypeId
|
|
DeterministicRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::DeterministicRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<DeterministicRandomVariable> ()
|
|
;
|
|
return tid;
|
|
}
|
|
DeterministicRandomVariable::DeterministicRandomVariable ()
|
|
:
|
|
m_count (0),
|
|
m_next (0),
|
|
m_data (0)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
DeterministicRandomVariable::~DeterministicRandomVariable ()
|
|
{
|
|
// Delete any values currently set.
|
|
NS_LOG_FUNCTION (this);
|
|
if (m_data != 0)
|
|
{
|
|
delete[] m_data;
|
|
}
|
|
}
|
|
|
|
void
|
|
DeterministicRandomVariable::SetValueArray (double* values, std::size_t length)
|
|
{
|
|
NS_LOG_FUNCTION (this << values << length);
|
|
// Delete any values currently set.
|
|
if (m_data != 0)
|
|
{
|
|
delete[] m_data;
|
|
}
|
|
|
|
// Make room for the values being set.
|
|
m_data = new double[length];
|
|
m_count = length;
|
|
m_next = length;
|
|
|
|
// Copy the values.
|
|
for (std::size_t i = 0; i < m_count; i++)
|
|
{
|
|
m_data[i] = values[i];
|
|
}
|
|
}
|
|
|
|
double
|
|
DeterministicRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
// Make sure the array has been set.
|
|
NS_ASSERT (m_count > 0);
|
|
|
|
if (m_next == m_count)
|
|
{
|
|
m_next = 0;
|
|
}
|
|
return m_data[m_next++];
|
|
}
|
|
|
|
uint32_t
|
|
DeterministicRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return (uint32_t)GetValue ();
|
|
}
|
|
|
|
NS_OBJECT_ENSURE_REGISTERED (EmpiricalRandomVariable);
|
|
|
|
// ValueCDF methods
|
|
EmpiricalRandomVariable::ValueCDF::ValueCDF (void)
|
|
: value (0.0),
|
|
cdf (0.0)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
EmpiricalRandomVariable::ValueCDF::ValueCDF (double v, double c)
|
|
: value (v),
|
|
cdf (c)
|
|
{
|
|
NS_LOG_FUNCTION (this << v << c);
|
|
NS_ASSERT (c >= 0.0 && c <= 1.0);
|
|
}
|
|
|
|
bool
|
|
operator < (EmpiricalRandomVariable::ValueCDF a,
|
|
EmpiricalRandomVariable::ValueCDF b)
|
|
{
|
|
return a.cdf < b.cdf;
|
|
}
|
|
|
|
TypeId
|
|
EmpiricalRandomVariable::GetTypeId (void)
|
|
{
|
|
static TypeId tid = TypeId ("ns3::EmpiricalRandomVariable")
|
|
.SetParent<RandomVariableStream>()
|
|
.SetGroupName ("Core")
|
|
.AddConstructor<EmpiricalRandomVariable> ()
|
|
.AddAttribute ("Interpolate",
|
|
"Treat the CDF as a smooth distribution and interpolate, "
|
|
"default is to treat the CDF as a histogram and sample.",
|
|
BooleanValue (false),
|
|
MakeBooleanAccessor (&EmpiricalRandomVariable::m_interpolate),
|
|
MakeBooleanChecker ())
|
|
;
|
|
return tid;
|
|
}
|
|
EmpiricalRandomVariable::EmpiricalRandomVariable (void)
|
|
: m_validated (false)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
}
|
|
|
|
bool
|
|
EmpiricalRandomVariable::SetInterpolate (bool interpolate)
|
|
{
|
|
NS_LOG_FUNCTION (this << interpolate);
|
|
bool prev = m_interpolate;
|
|
m_interpolate = interpolate;
|
|
return prev;
|
|
}
|
|
|
|
uint32_t
|
|
EmpiricalRandomVariable::GetInteger (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
return static_cast<uint32_t> (GetValue ());
|
|
}
|
|
|
|
bool
|
|
EmpiricalRandomVariable::PreSample (double & value)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
|
|
if (!m_validated)
|
|
{
|
|
Validate ();
|
|
}
|
|
|
|
// Get a uniform random variable in [0, 1].
|
|
double r = Peek ()->RandU01 ();
|
|
if (IsAntithetic ())
|
|
{
|
|
r = (1 - r);
|
|
}
|
|
|
|
value = r;
|
|
bool valid = false;
|
|
// check extrema
|
|
if (r <= m_emp.front ().cdf)
|
|
{
|
|
value = m_emp.front ().value; // Less than first
|
|
valid = true;
|
|
}
|
|
else if (r >= m_emp.back ().cdf)
|
|
{
|
|
value = m_emp.back ().value; // Greater than last
|
|
valid = true;
|
|
}
|
|
return valid;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::GetValue (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
|
|
double value;
|
|
if (PreSample (value))
|
|
{
|
|
return value;
|
|
}
|
|
|
|
// value now has the (unused) URNG selector
|
|
if (m_interpolate)
|
|
{
|
|
value = DoInterpolate (value);
|
|
}
|
|
else
|
|
{
|
|
value = DoSampleCDF (value);
|
|
}
|
|
return value;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::DoSampleCDF (double r)
|
|
{
|
|
NS_LOG_FUNCTION (this << r);
|
|
|
|
ValueCDF selector (0, r);
|
|
auto bound = std::upper_bound (m_emp.begin (), m_emp.end (), selector);
|
|
|
|
return bound->value;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::Interpolate (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
|
|
double value;
|
|
if (PreSample (value))
|
|
{
|
|
return value;
|
|
}
|
|
|
|
// value now has the (unused) URNG selector
|
|
value = DoInterpolate (value);
|
|
return value;
|
|
}
|
|
|
|
double
|
|
EmpiricalRandomVariable::DoInterpolate (double r)
|
|
{
|
|
NS_LOG_FUNCTION (this << r);
|
|
|
|
// Return a value from the empirical distribution
|
|
// This code based (loosely) on code by Bruce Mah (Thanks Bruce!)
|
|
|
|
// search
|
|
ValueCDF selector (0, r);
|
|
auto upper = std::upper_bound (m_emp.begin (), m_emp.end (), selector);
|
|
auto lower = std::prev (upper, 1);
|
|
if (upper == m_emp.begin ())
|
|
{
|
|
lower = upper;
|
|
}
|
|
|
|
// Interpolate random value in range [v1..v2) based on [c1 .. r .. c2)
|
|
double c1 = lower->cdf;
|
|
double c2 = upper->cdf;
|
|
double v1 = lower->value;
|
|
double v2 = upper->value;
|
|
|
|
double value = (v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
|
|
return value;
|
|
}
|
|
|
|
void
|
|
EmpiricalRandomVariable::CDF (double v, double c)
|
|
{
|
|
// Add a new empirical datapoint to the empirical cdf
|
|
// NOTE. These MUST be inserted in non-decreasing order
|
|
NS_LOG_FUNCTION (this << v << c);
|
|
m_emp.push_back (ValueCDF (v, c));
|
|
}
|
|
|
|
void
|
|
EmpiricalRandomVariable::Validate (void)
|
|
{
|
|
NS_LOG_FUNCTION (this);
|
|
if (m_emp.empty ())
|
|
{
|
|
NS_FATAL_ERROR ("CDF is not initialized");
|
|
}
|
|
ValueCDF prior = m_emp[0];
|
|
for (auto current : m_emp)
|
|
{
|
|
if (current.value < prior.value || current.cdf < prior.cdf)
|
|
{ // Error
|
|
std::cerr << "Empirical Dist error,"
|
|
<< " current value " << current.value
|
|
<< " prior value " << prior.value
|
|
<< " current cdf " << current.cdf
|
|
<< " prior cdf " << prior.cdf << std::endl;
|
|
NS_FATAL_ERROR ("Empirical Dist error");
|
|
}
|
|
prior = current;
|
|
}
|
|
if (prior.cdf != 1.0)
|
|
{
|
|
NS_FATAL_ERROR ("CDF does not cover the whole distribution");
|
|
}
|
|
m_validated = true;
|
|
}
|
|
|
|
} // namespace ns3
|