/* -*- 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>
//
#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 "random-variable.h"
#include "rng-stream.h"
#include "fatal-error.h"
using namespace std;
namespace ns3{
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// RandomVariable methods
uint32_t RandomVariable::runNumber = 0;
bool RandomVariable::initialized = false; // True if RngStream seed set
bool RandomVariable::useDevRandom = false; // True if use /dev/random
bool RandomVariable::globalSeedSet = false; // True if GlobalSeed called
int RandomVariable::devRandom = -1;
uint32_t RandomVariable::globalSeed[6];
unsigned long RandomVariable::heuristic_sequence;
RngStream* RandomVariable::m_static_generator = 0;
//the static object random_variable_initializer initializes the static members
//of RandomVariable
static class RandomVariableInitializer
{
public:
RandomVariableInitializer()
{
RandomVariable::Initialize(); // sets the static package seed
RandomVariable::m_static_generator = new RngStream();
RandomVariable::m_static_generator->InitializeStream();
}
~RandomVariableInitializer()
{
delete RandomVariable::m_static_generator;
}
} random_variable_initializer;
RandomVariable::RandomVariable()
{
m_generator = new RngStream();
m_generator->InitializeStream();
m_generator->ResetNthSubstream(RandomVariable::runNumber);
}
RandomVariable::RandomVariable(const RandomVariable& r)
{
m_generator = new RngStream(*r.m_generator);
}
RandomVariable::~RandomVariable()
{
delete m_generator;
}
uint32_t RandomVariable::GetIntValue()
{
return (uint32_t)GetValue();
}
void RandomVariable::UseDevRandom(bool udr)
{
RandomVariable::useDevRandom = udr;
}
void RandomVariable::GetSeed(uint32_t seed[6])
{
m_generator->GetState(seed);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// RandomVariable static methods
void RandomVariable::UseGlobalSeed(uint32_t s0, uint32_t s1, uint32_t s2,
uint32_t s3, uint32_t s4, uint32_t s5)
{
if (RandomVariable::globalSeedSet)
{
cerr << "Random number generator already initialized!" << endl;
cerr << "Call to RandomVariable::UseGlobalSeed() ignored" << endl;
return;
}
RandomVariable::globalSeed[0] = s0;
RandomVariable::globalSeed[1] = s1;
RandomVariable::globalSeed[2] = s2;
RandomVariable::globalSeed[3] = s3;
RandomVariable::globalSeed[4] = s4;
RandomVariable::globalSeed[5] = s5;
if (!RngStream::CheckSeed(RandomVariable::globalSeed))
NS_FATAL_ERROR("Invalid seed");
RandomVariable::globalSeedSet = true;
}
void RandomVariable::Initialize()
{
if (RandomVariable::initialized) return; // Already initialized and seeded
RandomVariable::initialized = true;
if (!RandomVariable::globalSeedSet)
{ // No global seed, try a random one
GetRandomSeeds(globalSeed);
}
// Seed the RngStream package
RngStream::SetPackageSeed(globalSeed);
}
void RandomVariable::GetRandomSeeds(uint32_t seeds[6])
{
// Check if /dev/random exists
if (RandomVariable::useDevRandom && RandomVariable::devRandom < 0)
{
RandomVariable::devRandom = open("/dev/random", O_RDONLY);
}
if (RandomVariable::devRandom > 0)
{ // Use /dev/random
while(true)
{
for (int i = 0; i < 6; ++i)
{
read(RandomVariable::devRandom, &seeds[i], sizeof(seeds[i]));
}
if (RngStream::CheckSeed(seeds)) break; // Got a valid one
}
}
else
{ // Seed from time of day (code borrowed from ns2 random seeding)
// Thanks to John Heidemann for this technique
while(true)
{
timeval tv;
gettimeofday(&tv, 0);
seeds[0] = (tv.tv_sec^tv.tv_usec^(++heuristic_sequence <<8))
& 0x7fffffff;
gettimeofday(&tv, 0);
seeds[1] = (tv.tv_sec^tv.tv_usec^(++heuristic_sequence <<8))
& 0x7fffffff;
gettimeofday(&tv, 0);
seeds[2] = (tv.tv_sec^tv.tv_usec^(++heuristic_sequence <<8))
& 0x7fffffff;
gettimeofday(&tv, 0);
seeds[3] = (tv.tv_sec^tv.tv_usec^(++heuristic_sequence <<8))
& 0x7fffffff;
gettimeofday(&tv, 0);
seeds[4] = (tv.tv_sec^tv.tv_usec^(++heuristic_sequence <<8))
& 0x7fffffff;
gettimeofday(&tv, 0);
seeds[5] = (tv.tv_sec^tv.tv_usec^(++heuristic_sequence <<8))
& 0x7fffffff;
if (RngStream::CheckSeed(seeds)) break; // Got a valid one
}
}
}
void RandomVariable::SetRunNumber(uint32_t n)
{
runNumber = n;
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// UniformVariable
UniformVariable::UniformVariable()
: m_min(0), m_max(1.0) { }
UniformVariable::UniformVariable(double s, double l)
: m_min(s), m_max(l) { }
UniformVariable::UniformVariable(const UniformVariable& c)
: RandomVariable(c), m_min(c.m_min), m_max(c.m_max) { }
double UniformVariable::GetValue()
{
return m_min + m_generator->RandU01() * (m_max - m_min);
}
RandomVariable* UniformVariable::Copy() const
{
return new UniformVariable(*this);
}
double UniformVariable::GetSingleValue(double s, double l)
{
return s + m_static_generator->RandU01() * (l - s);;
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ConstantVariable methods
ConstantVariable::ConstantVariable()
: m_const(0) { }
ConstantVariable::ConstantVariable(double c)
: m_const(c) { };
ConstantVariable::ConstantVariable(const ConstantVariable& c)
: RandomVariable(c), m_const(c.m_const) { }
void ConstantVariable::NewConstant(double c)
{ m_const = c;}
double ConstantVariable::GetValue()
{
return m_const;
}
uint32_t ConstantVariable::GetIntValue()
{
return (uint32_t)m_const;
}
RandomVariable* ConstantVariable::Copy() const
{
return new ConstantVariable(*this);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// SequentialVariable methods
SequentialVariable::SequentialVariable(double f, double l, double i, uint32_t c)
: m_min(f), m_max(l), m_increment(ConstantVariable(i).Copy()), m_consecutive(c),
m_current(f), m_currentConsecutive(0)
{
}
SequentialVariable::SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c)
: m_min(f), m_max(l), m_increment(i.Copy()), m_consecutive(c),
m_current(f), m_currentConsecutive(0)
{
}
SequentialVariable::SequentialVariable(const SequentialVariable& c)
: RandomVariable(c), m_min(c.m_min), m_max(c.m_max),
m_increment(c.m_increment->Copy()), m_consecutive(c.m_consecutive),
m_current(c.m_current), m_currentConsecutive(c.m_currentConsecutive)
{
}
SequentialVariable::~SequentialVariable()
{
delete m_increment;
}
double SequentialVariable::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;
}
RandomVariable* SequentialVariable::Copy() const
{
return new SequentialVariable(*this);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ExponentialVariable methods
ExponentialVariable::ExponentialVariable()
: m_mean(1.0), m_bound(0) { }
ExponentialVariable::ExponentialVariable(double m)
: m_mean(m), m_bound(0) { }
ExponentialVariable::ExponentialVariable(double m, double b)
: m_mean(m), m_bound(b) { }
ExponentialVariable::ExponentialVariable(const ExponentialVariable& c)
: RandomVariable(c), m_mean(c.m_mean), m_bound(c.m_bound) { }
double ExponentialVariable::GetValue()
{
double r = -m_mean*log(m_generator->RandU01());
if (m_bound != 0 && r > m_bound) return m_bound;
return r;
}
RandomVariable* ExponentialVariable::Copy() const
{
return new ExponentialVariable(*this);
}
double ExponentialVariable::GetSingleValue(double m, double b/*=0*/)
{
double r = -m*log(m_static_generator->RandU01());
if (b != 0 && r > b) return b;
return r;
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ParetoVariable methods
ParetoVariable::ParetoVariable()
: m_mean(1.0), m_shape(1.5), m_bound(0) { }
ParetoVariable::ParetoVariable(double m)
: m_mean(m), m_shape(1.5), m_bound(0) { }
ParetoVariable::ParetoVariable(double m, double s)
: m_mean(m), m_shape(s), m_bound(0) { }
ParetoVariable::ParetoVariable(double m, double s, double b)
: m_mean(m), m_shape(s), m_bound(b) { }
ParetoVariable::ParetoVariable(const ParetoVariable& c)
: RandomVariable(c), m_mean(c.m_mean), m_shape(c.m_shape),
m_bound(c.m_bound) { }
double ParetoVariable::GetValue()
{
double scale = m_mean * ( m_shape - 1.0) / m_shape;
double r = (scale * ( 1.0 / pow(m_generator->RandU01(), 1.0 / m_shape)));
if (m_bound != 0 && r > m_bound) return m_bound;
return r;
}
RandomVariable* ParetoVariable::Copy() const
{
return new ParetoVariable(*this);
}
double ParetoVariable::GetSingleValue(double m, double s, double b/*=0*/)
{
double scale = m * ( s - 1.0) / s;
double r = (scale * ( 1.0 / pow(m_static_generator->RandU01(), 1.0 / s)));
if (b != 0 && r > b) return b;
return r;
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// WeibullVariable methods
WeibullVariable::WeibullVariable() : m_mean(1.0), m_alpha(1), m_bound(0) { }
WeibullVariable::WeibullVariable(double m)
: m_mean(m), m_alpha(1), m_bound(0) { }
WeibullVariable::WeibullVariable(double m, double s)
: m_mean(m), m_alpha(s), m_bound(0) { }
WeibullVariable::WeibullVariable(double m, double s, double b)
: m_mean(m), m_alpha(s), m_bound(b) { };
WeibullVariable::WeibullVariable(const WeibullVariable& c)
: RandomVariable(c), m_mean(c.m_mean), m_alpha(c.m_alpha),
m_bound(c.m_bound) { }
double WeibullVariable::GetValue()
{
double exponent = 1.0 / m_alpha;
double r = m_mean * pow( -log(m_generator->RandU01()), exponent);
if (m_bound != 0 && r > m_bound) return m_bound;
return r;
}
RandomVariable* WeibullVariable::Copy() const
{
return new WeibullVariable(*this);
}
double WeibullVariable::GetSingleValue(double m, double s, double b/*=0*/)
{
double exponent = 1.0 / s;
double r = m * pow( -log(m_static_generator->RandU01()), exponent);
if (b != 0 && r > b) return b;
return r;
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// NormalVariable methods
bool NormalVariable::m_static_nextValid = false;
double NormalVariable::m_static_next;
const double NormalVariable::INFINITE_VALUE = 1e307;
NormalVariable::NormalVariable()
: m_mean(0.0), m_variance(1.0), m_bound(INFINITE_VALUE), m_nextValid(false){}
NormalVariable::NormalVariable(double m, double v, double b/*=INFINITE_VALUE*/)
: m_mean(m), m_variance(v), m_bound(b), m_nextValid(false) { }
NormalVariable::NormalVariable(const NormalVariable& c)
: RandomVariable(c), m_mean(c.m_mean), m_variance(c.m_variance),
m_bound(c.m_bound) { }
double NormalVariable::GetValue()
{
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
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 (fabs(m_next) > m_bound) m_next = m_bound * (m_next)/fabs(m_next);
m_nextValid = true;
double x1 = m_mean + v1 * y * sqrt(m_variance);
if (fabs(x1) > m_bound) x1 = m_bound * (x1)/fabs(x1);
return x1;
}
}
}
RandomVariable* NormalVariable::Copy() const
{
return new NormalVariable(*this);
}
double NormalVariable::GetSingleValue(double m, double v, double b)
{
if (m_static_nextValid)
{ // use previously generated
m_static_nextValid = false;
return m_static_next;
}
while(1)
{ // See Simulation Modeling and Analysis p. 466 (Averill Law)
// for algorithm
double u1 = m_static_generator->RandU01();
double u2 = m_static_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_static_next = m + v2 * y * sqrt(v);
if (fabs(m_static_next) > b) m_static_next = b * (m_static_next)/fabs(m_static_next);
m_static_nextValid = true;
double x1 = m + v1 * y * sqrt(v);
if (fabs(x1) > b) x1 = b * (x1)/fabs(x1);
return x1;
}
}
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// ValueCDF methods
EmpiricalVariable::ValueCDF::ValueCDF()
: value(0.0), cdf(0.0){ }
EmpiricalVariable::ValueCDF::ValueCDF(double v, double c)
: value(v), cdf(c) { }
EmpiricalVariable::ValueCDF::ValueCDF(const ValueCDF& c)
: value(c.value), cdf(c.cdf) { }
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// EmpiricalVariable methods
EmpiricalVariable::EmpiricalVariable()
: validated(false) { }
EmpiricalVariable::EmpiricalVariable(const EmpiricalVariable& c)
: RandomVariable(c), validated(c.validated), emp(c.emp) { }
EmpiricalVariable::~EmpiricalVariable() { }
double EmpiricalVariable::GetValue()
{ // Return a value from the empirical distribution
// This code based (loosely) on code by Bruce Mah (Thanks Bruce!)
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;
}
}
RandomVariable* EmpiricalVariable::Copy() const
{
return new EmpiricalVariable(*this);
}
void EmpiricalVariable::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 EmpiricalVariable::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 EmpiricalVariable::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));
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// Integer EmpiricalVariable methods
IntEmpiricalVariable::IntEmpiricalVariable() { }
uint32_t IntEmpiricalVariable::GetIntValue()
{
return (uint32_t)GetValue();
}
RandomVariable* IntEmpiricalVariable::Copy() const
{
return new IntEmpiricalVariable(*this);
}
double IntEmpiricalVariable::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));
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// DeterministicVariable
DeterministicVariable::DeterministicVariable(double* d, uint32_t c)
: count(c), next(c), data(d)
{ // Nothing else needed
}
DeterministicVariable::~DeterministicVariable() { }
double DeterministicVariable::GetValue()
{
if (next == count) next = 0;
return data[next++];
}
RandomVariable* DeterministicVariable::Copy() const
{
return new DeterministicVariable(*this);
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// LogNormalVariable
RandomVariable* LogNormalVariable::Copy () const
{
return new LogNormalVariable (m_mu, m_sigma);
}
LogNormalVariable::LogNormalVariable (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
LogNormalVariable::GetValue ()
{
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;
}
double LogNormalVariable::GetSingleValue (double mu, double sigma)
{
double u, v, r2, normal, z;
do
{
/* choose x,y in uniform square (-1,-1) to (+1,+1) */
u = -1 + 2 * m_static_generator->RandU01 ();
v = -1 + 2 * m_static_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 (sigma * normal + mu);
return z;
}
//-----------------------------------------------------------------------------
//-----------------------------------------------------------------------------
// TriangularVariable methods
TriangularVariable::TriangularVariable()
: m_min(0), m_max(1), m_mode(0.5) { }
TriangularVariable::TriangularVariable(double s, double l, double mean)
: m_min(s), m_max(l), m_mode(3.0*mean-s-l) { }
TriangularVariable::TriangularVariable(const TriangularVariable& c)
: RandomVariable(c), m_min(c.m_min), m_max(c.m_max), m_mode(c.m_mode) { }
double TriangularVariable::GetValue()
{
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) );
}
RandomVariable* TriangularVariable::Copy() const
{
return new TriangularVariable(*this);
}
double TriangularVariable::GetSingleValue(double s, double l, double mean)
{
double mode = 3.0*mean-s-l;
double u = m_static_generator->RandU01();
if(u <= (mode - s) / (l - s) )
return s + sqrt(u * (l - s) * (mode - s) );
else
return l - sqrt( (1-u) * (l - s) * (l - mode) );
}
}//namespace ns3
#ifdef RUN_SELF_TESTS
#include "test.h"
#include <vector>
namespace ns3 {
class RandomVariableTest : public Test
{
public:
RandomVariableTest () : Test ("RandomVariable") {}
virtual bool RunTests (void)
{
bool ok = true;
const double desired_mean = 1.0;
const double desired_stddev = 1.0;
double tmp = log (1 + (desired_stddev/desired_mean)*(desired_stddev/desired_mean));
double sigma = sqrt (tmp);
double mu = log (desired_mean) - 0.5*tmp;
// Test a custom lognormal instance
{
LogNormalVariable lognormal (mu, sigma);
vector<double> samples;
const int NSAMPLES = 10000;
double sum = 0;
for (int n = NSAMPLES; n; --n)
{
double value = lognormal.GetValue ();
sum += value;
samples.push_back (value);
}
double obtained_mean = sum / NSAMPLES;
sum = 0;
for (vector<double>::iterator iter = samples.begin (); iter != samples.end (); iter++)
{
double tmp = (*iter - obtained_mean);
sum += tmp*tmp;
}
double obtained_stddev = sqrt (sum / (NSAMPLES - 1));
if (not (obtained_mean/desired_mean > 0.90 and obtained_mean/desired_mean < 1.10))
{
ok = false;
Failure () << "Obtained lognormal mean value " << obtained_mean << ", expected " << desired_mean << std::endl;
}
if (not (obtained_stddev/desired_stddev > 0.90 and obtained_stddev/desired_stddev < 1.10))
{
ok = false;
Failure () << "Obtained lognormal stddev value " << obtained_stddev <<
", expected " << desired_stddev << std::endl;
}
}
// Test GetSingleValue
{
vector<double> samples;
const int NSAMPLES = 10000;
double sum = 0;
for (int n = NSAMPLES; n; --n)
{
double value = LogNormalVariable::GetSingleValue (mu, sigma);
sum += value;
samples.push_back (value);
}
double obtained_mean = sum / NSAMPLES;
sum = 0;
for (vector<double>::iterator iter = samples.begin (); iter != samples.end (); iter++)
{
double tmp = (*iter - obtained_mean);
sum += tmp*tmp;
}
double obtained_stddev = sqrt (sum / (NSAMPLES - 1));
if (not (obtained_mean/desired_mean > 0.90 and obtained_mean/desired_mean < 1.10))
{
ok = false;
Failure () << "Obtained LogNormalVariable::GetSingleValue mean value " << obtained_mean
<< ", expected " << desired_mean << std::endl;
}
if (not (obtained_stddev/desired_stddev > 0.90 and obtained_stddev/desired_stddev < 1.10))
{
ok = false;
Failure () << "Obtained LogNormalVariable::GetSingleValue stddev value " << obtained_stddev <<
", expected " << desired_stddev << std::endl;
}
}
return ok;
}
};
static RandomVariableTest g_random_variable_tests;
}//namespace ns3
#endif /* RUN_SELF_TESTS */