ns-3.24: comparison src/core/random-variable.cc

equal deleted inserted replaced

-:f953bc93d9c3
+:68bd5111f38c
 #include <iostream>
 #include <math.h>
 #include <stdlib.h>
-#include <sys/time.h>			// for gettimeofday
+#include <sys/time.h>                   // for gettimeofday
 #include <unistd.h>
 #include <iostream>
 #include <sys/types.h>
 #include <sys/stat.h>
 #include <fcntl.h>
 #include <sstream>
 #include <vector>
 #include "test.h"
 #include "assert.h"
 #include "rng-stream.h"
 #include "fatal-error.h"
 using namespace std;
-namespace ns3{
+namespace ns3 {
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // Seed Manager
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-uint32_t SeedManager::GetSeed()
+uint32_t SeedManager::GetSeed ()
 {
 uint32_t s[6];
 RngStream::GetPackageSeed (s);
-NS_ASSERT(
+NS_ASSERT (
-s[0] == s[1] &&
+s[0] == s[1]
-s[0] == s[2] &&
+&& s[0] == s[2]
-s[0] == s[3] &&
+&& s[0] == s[3]
-s[0] == s[4] &&
+&& s[0] == s[4]
-s[0] == s[5]
+&& s[0] == s[5]
 );
 return s[0];
 }
-void SeedManager::SetSeed(uint32_t seed)
+void SeedManager::SetSeed (uint32_t seed)
 {
-Config::SetGlobal("RngSeed", IntegerValue(seed));
+Config::SetGlobal ("RngSeed", IntegerValue (seed));
 }
-void SeedManager::SetRun(uint32_t run)
+void SeedManager::SetRun (uint32_t run)
 {
-Config::SetGlobal("RngRun", IntegerValue(run));
+Config::SetGlobal ("RngRun", IntegerValue (run));
 }
-uint32_t SeedManager::GetRun()
+uint32_t SeedManager::GetRun ()
 {
 return RngStream::GetPackageRun ();
 }
 bool SeedManager::CheckSeed (uint32_t seed)
 {
-return RngStream::CheckSeed(seed);
+return RngStream::CheckSeed (seed);
 }
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // RandomVariableBase methods
 class RandomVariableBase
 {
 public:
 RandomVariableBase ();
 RandomVariableBase (const RandomVariableBase &o);
-virtual ~RandomVariableBase();
+virtual ~RandomVariableBase ();
-virtual double  GetValue() = 0;
+virtual double  GetValue () = 0;
-virtual uint32_t GetInteger();
+virtual uint32_t GetInteger ();
-virtual RandomVariableBase*   Copy(void) const = 0;
+virtual RandomVariableBase*   Copy (void) const = 0;
 protected:
-RngStream* m_generator;  //underlying generator being wrapped
+RngStream* m_generator;  // underlying generator being wrapped
 };
-RandomVariableBase::RandomVariableBase()
+RandomVariableBase::RandomVariableBase ()
-: m_generator(NULL)
+: m_generator (NULL)
 {
 }
-RandomVariableBase::RandomVariableBase(const RandomVariableBase& r)
+RandomVariableBase::RandomVariableBase (const RandomVariableBase& r)
-:m_generator(0)
+: m_generator (0)
 {
 if (r.m_generator)
 {
-m_generator = new RngStream(*r.m_generator);
+m_generator = new RngStream (*r.m_generator);
 }
 }
-RandomVariableBase::~RandomVariableBase()
+RandomVariableBase::~RandomVariableBase ()
 {
 delete m_generator;
 }
-uint32_t RandomVariableBase::GetInteger()
+uint32_t RandomVariableBase::GetInteger ()
 {
-return (uint32_t)GetValue();
+return (uint32_t)GetValue ();
 }
-//-------------------------------------------------------
+// -------------------------------------------------------
-RandomVariable::RandomVariable()
+RandomVariable::RandomVariable ()
 : m_variable (0)
-{}
+{
-RandomVariable::RandomVariable(const RandomVariable&o)
+}
+RandomVariable::RandomVariable (const RandomVariable&o)
 : m_variable (o.m_variable->Copy ())
-{}
+{
+}
 RandomVariable::RandomVariable (const RandomVariableBase &variable)
 : m_variable (variable.Copy ())
-{}
+{
+}
 RandomVariable &
 RandomVariable::operator = (const RandomVariable &o)
 {
 if (&o == this)
 {
 }
 delete m_variable;
 m_variable = o.m_variable->Copy ();
 return *this;
 }
-RandomVariable::~RandomVariable()
+RandomVariable::~RandomVariable ()
 {
 delete m_variable;
 }
 double
 RandomVariable::GetValue (void) const
 {
 return m_variable->GetValue ();
 }
 uint32_t
 RandomVariable::GetInteger (void) const
 {
 return m_variable->GetInteger ();
 }
 ATTRIBUTE_VALUE_IMPLEMENT (RandomVariable);
 ATTRIBUTE_CHECKER_IMPLEMENT (RandomVariable);
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // UniformVariableImpl
-class UniformVariableImpl : public RandomVariableBase {
+class UniformVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Creates a uniform random number generator in the
 * range [0.0 .. 1.0).
 */
-UniformVariableImpl();
+UniformVariableImpl ();
 /**
 * Creates a uniform random number generator with the specified range
 * \param s Low end of the range
 * \param l High end of the range
 */
-UniformVariableImpl(double s, double l);
+UniformVariableImpl (double s, double l);
-UniformVariableImpl(const UniformVariableImpl& c);
+UniformVariableImpl (const UniformVariableImpl& c);
 double GetMin (void) const;
 double GetMax (void) const;
 /**
 * \return A value between low and high values specified by the constructor
 */
-virtual double GetValue();
+virtual double GetValue ();
 /**
 * \return A value between low and high values specified by parameters
 */
-virtual double GetValue(double s, double l);
+virtual double GetValue (double s, double l);
-virtual RandomVariableBase*  Copy(void) const;
+virtual RandomVariableBase*  Copy (void) const;
 private:
 double m_min;
 double m_max;
 };
-UniformVariableImpl::UniformVariableImpl()
+UniformVariableImpl::UniformVariableImpl ()
-: m_min(0), m_max(1.0) { }
+: m_min (0),
+m_max (1.0)
-UniformVariableImpl::UniformVariableImpl(double s, double l)
+{
-: m_min(s), m_max(l) { }
+}
-UniformVariableImpl::UniformVariableImpl(const UniformVariableImpl& c)
+UniformVariableImpl::UniformVariableImpl (double s, double l)
-: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max) { }
+: m_min (s),
+m_max (l)
-double
+{
+}
+UniformVariableImpl::UniformVariableImpl (const UniformVariableImpl& c)
+: RandomVariableBase (c),
+m_min (c.m_min),
+m_max (c.m_max)
+{
+}
+double
 UniformVariableImpl::GetMin (void) const
 {
 return m_min;
 }
 double
 UniformVariableImpl::GetMax (void) const
 {
 return m_max;
 }
-double UniformVariableImpl::GetValue()
+double UniformVariableImpl::GetValue ()
 {
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
-return m_min + m_generator->RandU01() * (m_max - m_min);
+return m_min + m_generator->RandU01 () * (m_max - m_min);
 }
-double UniformVariableImpl::GetValue(double s, double l)
+double UniformVariableImpl::GetValue (double s, double l)
 {
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
-return s + m_generator->RandU01() * (l-s);
+return s + m_generator->RandU01 () * (l - s);
 }
-RandomVariableBase* UniformVariableImpl::Copy() const
+RandomVariableBase* UniformVariableImpl::Copy () const
 {
-return new UniformVariableImpl(*this);
+return new UniformVariableImpl (*this);
 }
-UniformVariable::UniformVariable()
+UniformVariable::UniformVariable ()
 : RandomVariable (UniformVariableImpl ())
-{}
+{
-UniformVariable::UniformVariable(double s, double l)
+}
+UniformVariable::UniformVariable (double s, double l)
 : RandomVariable (UniformVariableImpl (s, l))
-{}
+{
+}
-double UniformVariable::GetValue(void) const
+double UniformVariable::GetValue (void) const
 {
 return this->RandomVariable::GetValue ();
 }
-double UniformVariable::GetValue(double s, double l)
+double UniformVariable::GetValue (double s, double l)
 {
-return ((UniformVariableImpl*)Peek())->GetValue(s,l);
+return ((UniformVariableImpl*)Peek ())->GetValue (s,l);
 }
 uint32_t UniformVariable::GetInteger (uint32_t s, uint32_t l)
 {
-NS_ASSERT(s <= l);
+NS_ASSERT (s <= l);
-return static_cast<uint32_t>( GetValue(s, l+1) );
+return static_cast<uint32_t> ( GetValue (s, l + 1) );
 }
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // ConstantVariableImpl methods
-class ConstantVariableImpl : public RandomVariableBase {
+class ConstantVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Construct a ConstantVariableImpl RNG that returns zero every sample
 */
-ConstantVariableImpl();
+ConstantVariableImpl ();
 /**
 * Construct a ConstantVariableImpl RNG that returns the specified value
 * every sample.
 * \param c Unchanging value for this RNG.
 */
-ConstantVariableImpl(double c);
+ConstantVariableImpl (double c);
-ConstantVariableImpl(const ConstantVariableImpl& c) ;
+ConstantVariableImpl (const ConstantVariableImpl& c);
 /**
 * \brief Specify a new constant RNG for this generator.
 * \param c New constant value for this RNG.
 */
-void    NewConstant(double c);
+void    NewConstant (double c);
 /**
 * \return The constant value specified
 */
-virtual double  GetValue();
+virtual double  GetValue ();
-virtual uint32_t GetInteger();
+virtual uint32_t GetInteger ();
-virtual RandomVariableBase*   Copy(void) const;
+virtual RandomVariableBase*   Copy (void) const;
 private:
 double m_const;
 };
-ConstantVariableImpl::ConstantVariableImpl()
+ConstantVariableImpl::ConstantVariableImpl ()
-: m_const(0) { }
+: m_const (0)
+{
-ConstantVariableImpl::ConstantVariableImpl(double c)
+}
-: m_const(c) { };
+ConstantVariableImpl::ConstantVariableImpl (double c)
-ConstantVariableImpl::ConstantVariableImpl(const ConstantVariableImpl& c)
+: m_const (c)
-: RandomVariableBase(c), m_const(c.m_const) { }
+{
+}
-void ConstantVariableImpl::NewConstant(double c)
-{ m_const = c;}
+ConstantVariableImpl::ConstantVariableImpl (const ConstantVariableImpl& c)
+: RandomVariableBase (c),
-double ConstantVariableImpl::GetValue()
+m_const (c.m_const)
+{
+}
+void ConstantVariableImpl::NewConstant (double c)
+{
+m_const = c;
+}
+double ConstantVariableImpl::GetValue ()
 {
 return m_const;
 }
-uint32_t ConstantVariableImpl::GetInteger()
+uint32_t ConstantVariableImpl::GetInteger ()
 {
 return (uint32_t)m_const;
 }
-RandomVariableBase* ConstantVariableImpl::Copy() const
+RandomVariableBase* ConstantVariableImpl::Copy () const
 {
-return new ConstantVariableImpl(*this);
+return new ConstantVariableImpl (*this);
 }
-ConstantVariable::ConstantVariable()
+ConstantVariable::ConstantVariable ()
 : RandomVariable (ConstantVariableImpl ())
-{}
+{
-ConstantVariable::ConstantVariable(double c)
+}
+ConstantVariable::ConstantVariable (double c)
 : RandomVariable (ConstantVariableImpl (c))
-{}
+{
-void
+}
-ConstantVariable::SetConstant(double c)
+void
+ConstantVariable::SetConstant (double c)
 {
 *this = ConstantVariable (c);
 }
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // SequentialVariableImpl methods
-class SequentialVariableImpl : public RandomVariableBase {
+class SequentialVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * \brief Constructor for the SequentialVariableImpl RNG.
 *
 * \param f First value of the sequence.
 * \param l One more than the last value of the sequence.
 * \param i Increment between sequence values
 * \param c Number of times each member of the sequence is repeated
 */
-SequentialVariableImpl(double f, double l, double i = 1, uint32_t c = 1);
+SequentialVariableImpl (double f, double l, double i = 1, uint32_t c = 1);
 /**
 * \brief Constructor for the SequentialVariableImpl RNG.
 *
 * Differs from the first only in that the increment parameter is a
 * \param f First value of the sequence.
 * \param l One more than the last value of the sequence.
 * \param i Reference to a RandomVariableBase for the sequence increment
 * \param c Number of times each member of the sequence is repeated
 */
-SequentialVariableImpl(double f, double l, const RandomVariable& i, uint32_t c = 1);
+SequentialVariableImpl (double f, double l, const RandomVariable& i, uint32_t c = 1);
-SequentialVariableImpl(const SequentialVariableImpl& c);
+SequentialVariableImpl (const SequentialVariableImpl& c);
-~SequentialVariableImpl();
+~SequentialVariableImpl ();
 /**
 * \return The next value in the Sequence
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase*  Copy(void) const;
+virtual RandomVariableBase*  Copy (void) const;
 private:
 double m_min;
 double m_max;
 RandomVariable  m_increment;
 uint32_t  m_consecutive;
 double m_current;
 uint32_t  m_currentConsecutive;
 };
-SequentialVariableImpl::SequentialVariableImpl(double f, double l, double i, uint32_t c)
+SequentialVariableImpl::SequentialVariableImpl (double f, double l, double i, uint32_t c)
-: m_min(f), m_max(l), m_increment(ConstantVariable(i)), m_consecutive(c),
+: m_min (f),
-m_current(f), m_currentConsecutive(0)
+m_max (l),
-{}
+m_increment (ConstantVariable (i)),
+m_consecutive (c),
-SequentialVariableImpl::SequentialVariableImpl(double f, double l, const RandomVariable& i, uint32_t c)
+m_current (f),
-: m_min(f), m_max(l), m_increment(i), m_consecutive(c),
+m_currentConsecutive (0)
-m_current(f), m_currentConsecutive(0)
+{
-{}
+}
-SequentialVariableImpl::SequentialVariableImpl(const SequentialVariableImpl& c)
+SequentialVariableImpl::SequentialVariableImpl (double f, double l, const RandomVariable& i, uint32_t c)
-: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max),
+: m_min (f),
-m_increment(c.m_increment), m_consecutive(c.m_consecutive),
+m_max (l),
-m_current(c.m_current), m_currentConsecutive(c.m_currentConsecutive)
+m_increment (i),
-{}
+m_consecutive (c),
+m_current (f),
-SequentialVariableImpl::~SequentialVariableImpl()
+m_currentConsecutive (0)
-{}
+{
+}
-double SequentialVariableImpl::GetValue()
+SequentialVariableImpl::SequentialVariableImpl (const SequentialVariableImpl& c)
+: RandomVariableBase (c),
+m_min (c.m_min),
+m_max (c.m_max),
+m_increment (c.m_increment),
+m_consecutive (c.m_consecutive),
+m_current (c.m_current),
+m_currentConsecutive (c.m_currentConsecutive)
+{
+}
+SequentialVariableImpl::~SequentialVariableImpl ()
+{
+}
+double SequentialVariableImpl::GetValue ()
 { // Return a sequential series of values
 double r = m_current;
 if (++m_currentConsecutive == m_consecutive)
 { // Time to advance to next
 m_currentConsecutive = 0;
-m_current += m_increment.GetValue();
+m_current += m_increment.GetValue ();
 if (m_current >= m_max)
-m_current = m_min + (m_current - m_max);
+{
+m_current = m_min + (m_current - m_max);
+}
 }
 return r;
 }
-RandomVariableBase* SequentialVariableImpl::Copy() const
+RandomVariableBase* SequentialVariableImpl::Copy () const
 {
-return new SequentialVariableImpl(*this);
+return new SequentialVariableImpl (*this);
 }
-SequentialVariable::SequentialVariable(double f, double l, double i, uint32_t c)
+SequentialVariable::SequentialVariable (double f, double l, double i, uint32_t c)
 : RandomVariable (SequentialVariableImpl (f, l, i, c))
-{}
+{
-SequentialVariable::SequentialVariable(double f, double l, const RandomVariable& i, uint32_t c)
+}
+SequentialVariable::SequentialVariable (double f, double l, const RandomVariable& i, uint32_t c)
 : RandomVariable (SequentialVariableImpl (f, l, i, c))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // ExponentialVariableImpl methods
-class ExponentialVariableImpl : public RandomVariableBase {
+class ExponentialVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Constructs an exponential random variable  with a mean
 * value of 1.0.
 */
-ExponentialVariableImpl();
+ExponentialVariableImpl ();
 /**
 * \brief Constructs an exponential random variable with a specified mean
 * \param m Mean value for the random variable
 */
-explicit ExponentialVariableImpl(double m);
+explicit ExponentialVariableImpl (double m);
 /**
-* \brief Constructs an exponential random variable with spefified
+* \brief Constructs an exponential random variable with specified
-* \brief mean and upper limit.
+* 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.
+* slightly smaller than the mean value specified: \f$ m - b/(e^{b/m}-1) \f$.
 * \param m Mean value of the random variable
 * \param b Upper bound on returned values
 */
-ExponentialVariableImpl(double m, double b);
+ExponentialVariableImpl (double m, double b);
-ExponentialVariableImpl(const ExponentialVariableImpl& c);
+ExponentialVariableImpl (const ExponentialVariableImpl& c);
 /**
 * \return A random value from this exponential distribution
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 double m_mean;  // Mean value of RV
 double m_bound; // Upper bound on value (if non-zero)
 };
-ExponentialVariableImpl::ExponentialVariableImpl()
+ExponentialVariableImpl::ExponentialVariableImpl ()
-: m_mean(1.0), m_bound(0) { }
+: m_mean (1.0),
+m_bound (0)
-ExponentialVariableImpl::ExponentialVariableImpl(double m)
+{
-: m_mean(m), m_bound(0) { }
+}
-ExponentialVariableImpl::ExponentialVariableImpl(double m, double b)
+ExponentialVariableImpl::ExponentialVariableImpl (double m)
-: m_mean(m), m_bound(b) { }
+: m_mean (m),
+m_bound (0)
-ExponentialVariableImpl::ExponentialVariableImpl(const ExponentialVariableImpl& c)
+{
-: RandomVariableBase(c), m_mean(c.m_mean), m_bound(c.m_bound) { }
+}
-double ExponentialVariableImpl::GetValue()
+ExponentialVariableImpl::ExponentialVariableImpl (double m, double b)
-{
+: m_mean (m),
-if(!m_generator)
+m_bound (b)
 {
-m_generator = new RngStream();
+}
-}
-while(1)
+ExponentialVariableImpl::ExponentialVariableImpl (const ExponentialVariableImpl& c)
-{
+: RandomVariableBase (c),
-double r = -m_mean*log(m_generator->RandU01());
+m_mean (c.m_mean),
-if (m_bound == 0 || r <= m_bound) return r;
+m_bound (c.m_bound)
-//otherwise, try again
+{
 }
-}
+double ExponentialVariableImpl::GetValue ()
-RandomVariableBase* ExponentialVariableImpl::Copy() const
+{
-{
+if (!m_generator)
-return new ExponentialVariableImpl(*this);
+{
-}
+m_generator = new RngStream ();
+}
-ExponentialVariable::ExponentialVariable()
+while (1)
+{
+double r = -m_mean*log (m_generator->RandU01 ());
+if (m_bound == 0 || r <= m_bound)
+{
+return r;
+}
+// otherwise, try again
+}
+}
+RandomVariableBase* ExponentialVariableImpl::Copy () const
+{
+return new ExponentialVariableImpl (*this);
+}
+ExponentialVariable::ExponentialVariable ()
 : RandomVariable (ExponentialVariableImpl ())
-{}
+{
-ExponentialVariable::ExponentialVariable(double m)
+}
+ExponentialVariable::ExponentialVariable (double m)
 : RandomVariable (ExponentialVariableImpl (m))
-{}
+{
-ExponentialVariable::ExponentialVariable(double m, double b)
+}
+ExponentialVariable::ExponentialVariable (double m, double b)
 : RandomVariable (ExponentialVariableImpl (m, b))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // ParetoVariableImpl methods
-class ParetoVariableImpl : public RandomVariableBase {
+class ParetoVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Constructs a pareto random variable with a mean of 1 and a shape
 * parameter of 1.5
 */
-ParetoVariableImpl();
+ParetoVariableImpl ();
 /**
 * Constructs a pareto random variable with specified mean and shape
 * parameter of 1.5
 * \param m Mean value of the distribution
 */
-explicit ParetoVariableImpl(double m);
+explicit ParetoVariableImpl (double m);
 /**
 * Constructs a pareto random variable with the specified mean value and
 * shape parameter.
 * \param m Mean value of the distribution
 * \param s Shape parameter for the distribution
 */
-ParetoVariableImpl(double m, double s);
+ParetoVariableImpl (double m, double s);
 /**
 * \brief Constructs a pareto random variable with the specified mean
 * \brief value, shape (alpha), and upper bound.
 *
 * is slightly smaller than the mean value specified.
 * \param m Mean value
 * \param s Shape parameter
 * \param b Upper limit on returned values
 */
-ParetoVariableImpl(double m, double s, double b);
+ParetoVariableImpl (double m, double s, double b);
-ParetoVariableImpl(const ParetoVariableImpl& c);
+ParetoVariableImpl (const ParetoVariableImpl& c);
 /**
 * \return A random value from this Pareto distribution
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase* Copy() const;
+virtual RandomVariableBase* Copy () const;
 private:
 double m_mean;  // Mean value of RV
 double m_shape; // Shape parameter
 double m_bound; // Upper bound on value (if non-zero)
 };
-ParetoVariableImpl::ParetoVariableImpl()
+ParetoVariableImpl::ParetoVariableImpl ()
-: m_mean(1.0), m_shape(1.5), m_bound(0) { }
+: m_mean (1.0),
+m_shape (1.5),
-ParetoVariableImpl::ParetoVariableImpl(double m)
+m_bound (0)
-: m_mean(m), m_shape(1.5), m_bound(0) { }
+{
+}
-ParetoVariableImpl::ParetoVariableImpl(double m, double s)
-: m_mean(m), m_shape(s), m_bound(0) { }
+ParetoVariableImpl::ParetoVariableImpl (double m)
+: m_mean (m),
-ParetoVariableImpl::ParetoVariableImpl(double m, double s, double b)
+m_shape (1.5),
-: m_mean(m), m_shape(s), m_bound(b) { }
+m_bound (0)
+{
-ParetoVariableImpl::ParetoVariableImpl(const ParetoVariableImpl& c)
+}
-: RandomVariableBase(c), m_mean(c.m_mean), m_shape(c.m_shape),
-m_bound(c.m_bound) { }
+ParetoVariableImpl::ParetoVariableImpl (double m, double s)
+: m_mean (m),
-double ParetoVariableImpl::GetValue()
+m_shape (s),
-{
+m_bound (0)
-if(!m_generator)
+{
-{
+}
-m_generator = new RngStream();
+ParetoVariableImpl::ParetoVariableImpl (double m, double s, double b)
+: m_mean (m),
+m_shape (s),
+m_bound (b)
+{
+}
+ParetoVariableImpl::ParetoVariableImpl (const ParetoVariableImpl& c)
+: RandomVariableBase (c),
+m_mean (c.m_mean),
+m_shape (c.m_shape),
+m_bound (c.m_bound)
+{
+}
+double ParetoVariableImpl::GetValue ()
+{
+if (!m_generator)
+{
+m_generator = new RngStream ();
 }
 double scale = m_mean * ( m_shape - 1.0) / m_shape;
-while(1)
+while (1)
 {
-double r = (scale * ( 1.0 / pow(m_generator->RandU01(), 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 r;
+if (m_bound == 0 || r <= m_bound)
-//otherwise, try again
+{
-}
+return r;
 }
+// otherwise, try again
-RandomVariableBase* ParetoVariableImpl::Copy() const
+}
-{
+}
-return new ParetoVariableImpl(*this);
+RandomVariableBase* ParetoVariableImpl::Copy () const
+{
+return new ParetoVariableImpl (*this);
 }
 ParetoVariable::ParetoVariable ()
 : RandomVariable (ParetoVariableImpl ())
-{}
+{
-ParetoVariable::ParetoVariable(double m)
+}
+ParetoVariable::ParetoVariable (double m)
 : RandomVariable (ParetoVariableImpl (m))
-{}
+{
-ParetoVariable::ParetoVariable(double m, double s)
+}
+ParetoVariable::ParetoVariable (double m, double s)
 : RandomVariable (ParetoVariableImpl (m, s))
-{}
+{
-ParetoVariable::ParetoVariable(double m, double s, double b)
+}
+ParetoVariable::ParetoVariable (double m, double s, double b)
 : RandomVariable (ParetoVariableImpl (m, s, b))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // WeibullVariableImpl methods
-class WeibullVariableImpl : public RandomVariableBase {
+class WeibullVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Constructs a weibull random variable  with a mean
 * value of 1.0 and a shape (alpha) parameter of 1
 */
-WeibullVariableImpl();
+WeibullVariableImpl ();
 /**
 * Constructs a weibull random variable with the specified mean
 * value and a shape (alpha) parameter of 1.5.
 * \param m mean value of the distribution
 */
-WeibullVariableImpl(double m) ;
+WeibullVariableImpl (double m);
 /**
 * Constructs a weibull random variable with the specified mean
 * value and a shape (alpha).
 * \param m Mean value for the distribution.
 * \param s Shape (alpha) parameter for the distribution.
 */
-WeibullVariableImpl(double m, double s);
+WeibullVariableImpl (double m, double s);
 /**
 * \brief Constructs a weibull random variable with the specified mean
 * \brief value, shape (alpha), and upper bound.
 * Since WeibullVariableImpl distributions can theoretically return unbounded values,
 * it is sometimes usefull to specify a fixed upper limit.  Note however
 * that when the upper limit is specified, the true mean of the distribution
 * is slightly smaller than the mean value specified.
 * \param m Mean value for the distribution.
 * \param s Shape (alpha) parameter for the distribution.
 * \param b Upper limit on returned values
 */
-WeibullVariableImpl(double m, double s, double b);
+WeibullVariableImpl (double m, double s, double b);
-WeibullVariableImpl(const WeibullVariableImpl& c);
+WeibullVariableImpl (const WeibullVariableImpl& c);
 /**
 * \return A random value from this Weibull distribution
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 double m_mean;  // Mean value of RV
 double m_alpha; // Shape parameter
 double m_bound; // Upper bound on value (if non-zero)
 };
-WeibullVariableImpl::WeibullVariableImpl() : m_mean(1.0), m_alpha(1), m_bound(0) { }
+WeibullVariableImpl::WeibullVariableImpl () : m_mean (1.0),
-WeibullVariableImpl::WeibullVariableImpl(double m)
+m_alpha (1),
-: m_mean(m), m_alpha(1), m_bound(0) { }
+m_bound (0)
-WeibullVariableImpl::WeibullVariableImpl(double m, double s)
+{
-: m_mean(m), m_alpha(s), m_bound(0) { }
+}
-WeibullVariableImpl::WeibullVariableImpl(double m, double s, double b)
+WeibullVariableImpl::WeibullVariableImpl (double m)
-: m_mean(m), m_alpha(s), m_bound(b) { };
+: m_mean (m),
-WeibullVariableImpl::WeibullVariableImpl(const WeibullVariableImpl& c)
+m_alpha (1),
-: RandomVariableBase(c), m_mean(c.m_mean), m_alpha(c.m_alpha),
+m_bound (0)
-m_bound(c.m_bound) { }
+{
+}
-double WeibullVariableImpl::GetValue()
+WeibullVariableImpl::WeibullVariableImpl (double m, double s)
-{
+: m_mean (m),
-if(!m_generator)
+m_alpha (s),
-{
+m_bound (0)
-m_generator = new RngStream();
+{
+}
+WeibullVariableImpl::WeibullVariableImpl (double m, double s, double b)
+: m_mean (m),
+m_alpha (s),
+m_bound (b)
+{
+}
+WeibullVariableImpl::WeibullVariableImpl (const WeibullVariableImpl& c)
+: RandomVariableBase (c),
+m_mean (c.m_mean),
+m_alpha (c.m_alpha),
+m_bound (c.m_bound)
+{
+}
+double WeibullVariableImpl::GetValue ()
+{
+if (!m_generator)
+{
+m_generator = new RngStream ();
 }
 double exponent = 1.0 / m_alpha;
-while(1)
+while (1)
 {
-double r = m_mean * pow( -log(m_generator->RandU01()), exponent);
+double r = m_mean * pow ( -log (m_generator->RandU01 ()), exponent);
-if (m_bound == 0 || r <= m_bound) return r;
+if (m_bound == 0 || r <= m_bound)
-//otherwise, try again
+{
-}
+return r;
 }
+// otherwise, try again
-RandomVariableBase* WeibullVariableImpl::Copy() const
+}
-{
+}
-return new WeibullVariableImpl(*this);
-}
+RandomVariableBase* WeibullVariableImpl::Copy () const
+{
-WeibullVariable::WeibullVariable()
+return new WeibullVariableImpl (*this);
+}
+WeibullVariable::WeibullVariable ()
 : RandomVariable (WeibullVariableImpl ())
-{}
+{
-WeibullVariable::WeibullVariable(double m)
+}
+WeibullVariable::WeibullVariable (double m)
 : RandomVariable (WeibullVariableImpl (m))
-{}
+{
-WeibullVariable::WeibullVariable(double m, double s)
+}
+WeibullVariable::WeibullVariable (double m, double s)
 : RandomVariable (WeibullVariableImpl (m, s))
-{}
+{
-WeibullVariable::WeibullVariable(double m, double s, double b)
+}
+WeibullVariable::WeibullVariable (double m, double s, double b)
 : RandomVariable (WeibullVariableImpl (m, s, b))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // NormalVariableImpl methods
-class NormalVariableImpl : public RandomVariableBase { // Normally Distributed random var
+class NormalVariableImpl : public RandomVariableBase   // Normally Distributed random var
+{
 public:
 static const double INFINITE_VALUE;
 /**
 * Constructs an normal random variable  with a mean
 * value of 0 and variance of 1.
 */
-NormalVariableImpl();
+NormalVariableImpl ();
 /**
 * \brief Construct a normal random variable with specified mean and variance
 * \param m Mean value
 * \param v Variance
 * \param b Bound.  The NormalVariableImpl is bounded within +-bound of the mean.
 */
-NormalVariableImpl(double m, double v, double b = INFINITE_VALUE);
+NormalVariableImpl (double m, double v, double b = INFINITE_VALUE);
-NormalVariableImpl(const NormalVariableImpl& c);
+NormalVariableImpl (const NormalVariableImpl& c);
 /**
 * \return A value from this normal distribution
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 double GetMean (void) const;
 double GetVariance (void) const;
 double GetBound (void) const;
 double m_next;      // The algorithm produces two values at a time
 };
 const double NormalVariableImpl::INFINITE_VALUE = 1e307;
-NormalVariableImpl::NormalVariableImpl()
+NormalVariableImpl::NormalVariableImpl ()
-: m_mean(0.0), m_variance(1.0), m_bound(INFINITE_VALUE), m_nextValid(false){}
+: m_mean (0.0),
+m_variance (1.0),
-NormalVariableImpl::NormalVariableImpl(double m, double v, double b)
+m_bound (INFINITE_VALUE),
-: m_mean(m), m_variance(v), m_bound(b), m_nextValid(false) { }
+m_nextValid (false)
+{
-NormalVariableImpl::NormalVariableImpl(const NormalVariableImpl& c)
+}
-: RandomVariableBase(c), m_mean(c.m_mean), m_variance(c.m_variance),
-m_bound(c.m_bound), m_nextValid(false) { }
+NormalVariableImpl::NormalVariableImpl (double m, double v, double b)
+: m_mean (m),
-double NormalVariableImpl::GetValue()
+m_variance (v),
-{
+m_bound (b),
-if(!m_generator)
+m_nextValid (false)
 {
-m_generator = new RngStream();
+}
+NormalVariableImpl::NormalVariableImpl (const NormalVariableImpl& c)
+: RandomVariableBase (c),
+m_mean (c.m_mean),
+m_variance (c.m_variance),
+m_bound (c.m_bound),
+m_nextValid (false)
+{
+}
+double NormalVariableImpl::GetValue ()
+{
+if (!m_generator)
+{
+m_generator = new RngStream ();
 }
 if (m_nextValid)
 { // use previously generated
 m_nextValid = false;
 return m_next;
 }
-while(1)
+while (1)
 { // See Simulation Modeling and Analysis p. 466 (Averill Law)
 // for algorithm; basically a Box-Muller transform:
 // http://en.wikipedia.org/wiki/Box-Muller_transform
-double u1 = m_generator->RandU01();
+double u1 = m_generator->RandU01 ();
-double u2 = 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);
+double y = sqrt ((-2 * log (w)) / w);
-m_next = m_mean + v2 * y * sqrt(m_variance);
+m_next = m_mean + v2 * y * sqrt (m_variance);
-//if next is in bounds, it is valid
+// if next is in bounds, it is valid
-m_nextValid = fabs(m_next-m_mean) <= m_bound;
+m_nextValid = fabs (m_next - m_mean) <= m_bound;
-double x1 = m_mean + v1 * y * sqrt(m_variance);
+double x1 = m_mean + v1 * y * sqrt (m_variance);
-//if x1 is in bounds, return it
+// if x1 is in bounds, return it
-if (fabs(x1-m_mean) <= m_bound)
+if (fabs (x1 - m_mean) <= m_bound)
 {
 return x1;
 }
-//otherwise try and return m_next if it is valid
+// otherwise try and return m_next if it is valid
 else if (m_nextValid)
-	    {
+{
-	      m_nextValid = false;
+m_nextValid = false;
-	      return m_next;
+return m_next;
-	    }
+}
-//otherwise, just run this loop again
+// otherwise, just run this loop again
 }
 }
 }
-RandomVariableBase* NormalVariableImpl::Copy() const
+RandomVariableBase* NormalVariableImpl::Copy () const
 {
-return new NormalVariableImpl(*this);
+return new NormalVariableImpl (*this);
 }
 double
 NormalVariableImpl::GetMean (void) const
 {
 NormalVariableImpl::GetBound (void) const
 {
 return m_bound;
 }
-NormalVariable::NormalVariable()
+NormalVariable::NormalVariable ()
 : RandomVariable (NormalVariableImpl ())
-{}
+{
-NormalVariable::NormalVariable(double m, double v)
+}
+NormalVariable::NormalVariable (double m, double v)
 : RandomVariable (NormalVariableImpl (m, v))
-{}
+{
-NormalVariable::NormalVariable(double m, double v, double b)
+}
+NormalVariable::NormalVariable (double m, double v, double b)
 : RandomVariable (NormalVariableImpl (m, v, b))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-class EmpiricalVariableImpl : public RandomVariableBase {
+// -----------------------------------------------------------------------------
+class EmpiricalVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Constructor for the EmpiricalVariableImpl random variables.
 */
-explicit EmpiricalVariableImpl();
+explicit EmpiricalVariableImpl ();
-virtual ~EmpiricalVariableImpl();
+virtual ~EmpiricalVariableImpl ();
-EmpiricalVariableImpl(const EmpiricalVariableImpl& c);
+EmpiricalVariableImpl (const EmpiricalVariableImpl& c);
 /**
 * \return A value from this empirical distribution
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 /**
 * \brief Specifies a point in the empirical distribution
 * \param v The function value for this point
 * \param c Probability that the function is less than or equal to v
 */
-virtual void CDF(double v, double c);  // Value, prob <= Value
+virtual void CDF (double v, double c);  // Value, prob <= Value
 private:
-class ValueCDF {
+class ValueCDF
-public:
+{
-ValueCDF();
+public:
-ValueCDF(double v, double c);
+ValueCDF ();
-ValueCDF(const ValueCDF& c);
+ValueCDF (double v, double c);
+ValueCDF (const ValueCDF& c);
 double value;
 double    cdf;
 };
-virtual void Validate();  // Insure non-decreasing emiprical values
+virtual void Validate ();  // Insure non-decreasing emiprical values
-virtual double Interpolate(double, double, double, double, double);
+virtual double Interpolate (double, double, double, double, double);
 bool validated; // True if non-decreasing validated
 std::vector<ValueCDF> emp;       // Empicical CDF
 };
 // ValueCDF methods
-EmpiricalVariableImpl::ValueCDF::ValueCDF()
+EmpiricalVariableImpl::ValueCDF::ValueCDF ()
-: value(0.0), cdf(0.0){ }
+: value (0.0),
-EmpiricalVariableImpl::ValueCDF::ValueCDF(double v, double c)
+cdf (0.0)
-: value(v), cdf(c) { }
+{
-EmpiricalVariableImpl::ValueCDF::ValueCDF(const ValueCDF& c)
+}
-: value(c.value), cdf(c.cdf) { }
+EmpiricalVariableImpl::ValueCDF::ValueCDF (double v, double c)
+: value (v),
-//-----------------------------------------------------------------------------
+cdf (c)
-//-----------------------------------------------------------------------------
+{
+}
+EmpiricalVariableImpl::ValueCDF::ValueCDF (const ValueCDF& c)
+: value (c.value),
+cdf (c.cdf)
+{
+}
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // EmpiricalVariableImpl methods
-EmpiricalVariableImpl::EmpiricalVariableImpl()
+EmpiricalVariableImpl::EmpiricalVariableImpl ()
-: validated(false) { }
+: validated (false)
+{
-EmpiricalVariableImpl::EmpiricalVariableImpl(const EmpiricalVariableImpl& c)
+}
-: RandomVariableBase(c), validated(c.validated), emp(c.emp) { }
+EmpiricalVariableImpl::EmpiricalVariableImpl (const EmpiricalVariableImpl& c)
-EmpiricalVariableImpl::~EmpiricalVariableImpl() { }
+: RandomVariableBase (c),
+validated (c.validated),
-double EmpiricalVariableImpl::GetValue()
+emp (c.emp)
+{
+}
+EmpiricalVariableImpl::~EmpiricalVariableImpl ()
+{
+}
+double EmpiricalVariableImpl::GetValue ()
 { // Return a value from the empirical distribution
 // This code based (loosely) on code by Bruce Mah (Thanks Bruce!)
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
-if (emp.size() == 0)
+if (emp.size () == 0)
 {
 return 0.0; // HuH? No empirical data
 }
 if (!validated)
 {
-Validate();      // Insure in non-decreasing
+Validate ();      // Insure in non-decreasing
 }
-double r = m_generator->RandU01();
+double r = m_generator->RandU01 ();
-if (r <= emp.front().cdf)
+if (r <= emp.front ().cdf)
 {
-return emp.front().value; // Less than first
+return emp.front ().value; // Less than first
 }
-if (r >= emp.back().cdf)
+if (r >= emp.back ().cdf)
 {
-return emp.back().value;  // Greater than last
+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;
+std::vector<ValueCDF>::size_type top = emp.size () - 1;
-while(1)
+while (1)
 {
 std::vector<ValueCDF>::size_type c = (top + bottom) / 2;
-if (r >= emp[c].cdf && r < emp[c+1].cdf)
+if (r >= emp[c].cdf && r < emp[c + 1].cdf)
 { // Found it
-return Interpolate(emp[c].cdf, emp[c+1].cdf,
+return Interpolate (emp[c].cdf, emp[c + 1].cdf,
-emp[c].value, emp[c+1].value,
+emp[c].value, emp[c + 1].value,
 r);
 }
 // Not here, adjust bounds
 if (r < emp[c].cdf)
 {
 top    = c - 1;
 bottom = c + 1;
 }
 }
 }
-RandomVariableBase* EmpiricalVariableImpl::Copy() const
+RandomVariableBase* EmpiricalVariableImpl::Copy () const
 {
-return new EmpiricalVariableImpl(*this);
+return new EmpiricalVariableImpl (*this);
 }
-void EmpiricalVariableImpl::CDF(double v, double c)
+void EmpiricalVariableImpl::CDF (double v, double c)
 { // Add a new empirical datapoint to the empirical cdf
 // NOTE.   These MUST be inserted in non-decreasing order
-emp.push_back(ValueCDF(v, c));
+emp.push_back (ValueCDF (v, c));
 }
-void EmpiricalVariableImpl::Validate()
+void EmpiricalVariableImpl::Validate ()
 {
 ValueCDF prior;
-for (std::vector<ValueCDF>::size_type i = 0; i < emp.size(); ++i)
+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");
+NS_FATAL_ERROR ("Empirical Dist error");
 }
 prior = current;
 }
 validated = true;
 }
-double EmpiricalVariableImpl::Interpolate(double c1, double c2,
+double EmpiricalVariableImpl::Interpolate (double c1, double c2,
 double v1, double v2, double r)
 { // Interpolate random value in range [v1..v2) based on [c1 .. r .. c2)
 return (v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
 }
-EmpiricalVariable::EmpiricalVariable()
+EmpiricalVariable::EmpiricalVariable ()
 : RandomVariable (EmpiricalVariableImpl ())
-{}
+{
+}
 EmpiricalVariable::EmpiricalVariable (const RandomVariableBase &variable)
 : RandomVariable (variable)
-{}
+{
-void
+}
-EmpiricalVariable::CDF(double v, double c)
+void
+EmpiricalVariable::CDF (double v, double c)
 {
 EmpiricalVariableImpl *impl = dynamic_cast<EmpiricalVariableImpl *> (Peek ());
 NS_ASSERT (impl);
 impl->CDF (v, c);
 }
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // IntegerValue EmpiricalVariableImpl methods
-class IntEmpiricalVariableImpl : public EmpiricalVariableImpl {
+class IntEmpiricalVariableImpl : public EmpiricalVariableImpl
+{
 public:
+IntEmpiricalVariableImpl ();
-IntEmpiricalVariableImpl();
+virtual RandomVariableBase* Copy (void) const;
-virtual RandomVariableBase* Copy(void) const;
 /**
 * \return An integer value from this empirical distribution
 */
-virtual uint32_t GetInteger();
+virtual uint32_t GetInteger ();
 private:
-virtual double Interpolate(double, double, double, double, double);
+virtual double Interpolate (double, double, double, double, double);
 };
-IntEmpiricalVariableImpl::IntEmpiricalVariableImpl() { }
+IntEmpiricalVariableImpl::IntEmpiricalVariableImpl ()
+{
-uint32_t IntEmpiricalVariableImpl::GetInteger()
+}
-{
-return (uint32_t)GetValue();
+uint32_t IntEmpiricalVariableImpl::GetInteger ()
-}
+{
+return (uint32_t)GetValue ();
-RandomVariableBase* IntEmpiricalVariableImpl::Copy() const
+}
-{
-return new IntEmpiricalVariableImpl(*this);
+RandomVariableBase* IntEmpiricalVariableImpl::Copy () const
-}
+{
+return new IntEmpiricalVariableImpl (*this);
-double IntEmpiricalVariableImpl::Interpolate(double c1, double c2,
+}
-double v1, double v2, double r)
+double IntEmpiricalVariableImpl::Interpolate (double c1, double c2,
+double v1, double v2, double r)
 { // Interpolate random value in range [v1..v2) based on [c1 .. r .. c2)
-return ceil(v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
+return ceil (v1 + ((v2 - v1) / (c2 - c1)) * (r - c1));
 }
-IntEmpiricalVariable::IntEmpiricalVariable()
+IntEmpiricalVariable::IntEmpiricalVariable ()
 : EmpiricalVariable (IntEmpiricalVariableImpl ())
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // DeterministicVariableImpl
 class DeterministicVariableImpl : public RandomVariableBase
 {
 public:
 /**
 * \brief Constructor
 *
 * Creates a generator that returns successive elements of the d array
 * on successive calls to ::Value().  Note that the d pointer is copied
 * for use by the generator (shallow-copy), not its contents, so the
 * contents of the array d points to have to remain unchanged for the use
 * of DeterministicVariableImpl to be meaningful.
 * \param d Pointer to array of random values to return in sequence
 * \param c Number of values in the array
 */
-explicit DeterministicVariableImpl(double* d, uint32_t c);
+explicit DeterministicVariableImpl (double* d, uint32_t c);
-virtual ~DeterministicVariableImpl();
+virtual ~DeterministicVariableImpl ();
 /**
 * \return The next value in the deterministic sequence
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 uint32_t   count;
 uint32_t   next;
 double* data;
 };
-DeterministicVariableImpl::DeterministicVariableImpl(double* d, uint32_t c)
+DeterministicVariableImpl::DeterministicVariableImpl (double* d, uint32_t c)
-: count(c), next(c), data(d)
+: count (c),
+next (c),
+data (d)
 { // Nothing else needed
 }
-DeterministicVariableImpl::~DeterministicVariableImpl() { }
+DeterministicVariableImpl::~DeterministicVariableImpl ()
+{
-double DeterministicVariableImpl::GetValue()
+}
-{
-if (next == count)
+double DeterministicVariableImpl::GetValue ()
+{
+if (next == count)
 {
 next = 0;
 }
 return data[next++];
 }
-RandomVariableBase* DeterministicVariableImpl::Copy() const
+RandomVariableBase* DeterministicVariableImpl::Copy () const
 {
-return new DeterministicVariableImpl(*this);
+return new DeterministicVariableImpl (*this);
 }
-DeterministicVariable::DeterministicVariable(double* d, uint32_t c)
+DeterministicVariable::DeterministicVariable (double* d, uint32_t c)
 : RandomVariable (DeterministicVariableImpl (d, c))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // LogNormalVariableImpl
-class LogNormalVariableImpl : public RandomVariableBase {
+class LogNormalVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * \param mu mu parameter of the lognormal distribution
 * \param sigma sigma parameter of the lognormal distribution
 */
 /**
 * \return A random value from this distribution
 */
 virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 double m_mu;
 double m_sigma;
 };
 {
 return new LogNormalVariableImpl (*this);
 }
 LogNormalVariableImpl::LogNormalVariableImpl (double mu, double sigma)
-:m_mu(mu), m_sigma(sigma)
+: m_mu (mu),
+m_sigma (sigma)
 {
 }
 // The code from this function was adapted from the GNU Scientific
 // Library 1.8:
 /* randist/lognormal.c
 *
 * Copyright (C) 1996, 1997, 1998, 1999, 2000 James Theiler, Brian Gough
 *
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 2 of the License, or (at
 * your option) any later version.
 *
 * This program is distributed in the hope that it will be useful, but
 * WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with this program; if not, write to the Free Software
 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
 */
 /* The lognormal distribution has the form
 p(x) dx = 1/(x * sqrt(2 pi sigma^2)) exp(-(ln(x) - zeta)^2/2 sigma^2) dx
 for x > 0. Lognormal random numbers are the exponentials of
 gaussian random numbers */
 double
 LogNormalVariableImpl::GetValue ()
 {
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
 double u, v, r2, normal, z;
 do
 {
 return z;
 }
 LogNormalVariable::LogNormalVariable (double mu, double sigma)
 : RandomVariable (LogNormalVariableImpl (mu, sigma))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // GammaVariableImpl
 class GammaVariableImpl : public RandomVariableBase
 {
 public:
 /**
 /**
 * \return A random value from the gamma distribution with parameters alpha
 * and beta
 */
-double GetValue(double alpha, double beta);
+double GetValue (double alpha, double beta);
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 double m_alpha;
 double m_beta;
 NormalVariable m_normal;
 {
 return new GammaVariableImpl (m_alpha, m_beta);
 }
 GammaVariableImpl::GammaVariableImpl (double alpha, double beta)
-: m_alpha(alpha), m_beta(beta)
+: m_alpha (alpha),
+m_beta (beta)
 {
 }
 double
 GammaVariableImpl::GetValue ()
 {
-return GetValue(m_alpha, m_beta);
+return GetValue (m_alpha, 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 gereating 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)
+x^(alpha-1) * exp(-x/beta)
-	p(x; alpha, beta) = ----------------------------
+p(x; alpha, beta) = ----------------------------
 beta^alpha * Gamma(alpha)
 for x > 0.
 */
 double
 GammaVariableImpl::GetValue (double alpha, double beta)
 {
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
 if (alpha < 1)
 {
 double u = m_generator->RandU01 ();
-return GetValue(1.0 + alpha, beta) * pow (u, 1.0 / alpha);
+return GetValue (1.0 + alpha, beta) * pow (u, 1.0 / alpha);
 }
 double x, v, u;
 double d = alpha - 1.0 / 3.0;
 double c = (1.0 / 3.0) / sqrt (d);
 while (1)
 {
 do
 {
 x = m_normal.GetValue ();
 v = 1.0 + c * x;
-} while (v <= 0);
+}
+while (v <= 0);
 v = v * v * v;
 u = m_generator->RandU01 ();
 if (u < 1 - 0.0331 * x * x * x * x)
 {
 GammaVariable::GammaVariable (double alpha, double beta)
 : RandomVariable (GammaVariableImpl (alpha, beta))
 {
 }
-double GammaVariable::GetValue(void) const
+double GammaVariable::GetValue (void) const
 {
 return this->RandomVariable::GetValue ();
 }
-double GammaVariable::GetValue(double alpha, double beta) const
+double GammaVariable::GetValue (double alpha, double beta) const
 {
-return ((GammaVariableImpl*)Peek())->GetValue(alpha, beta);
+return ((GammaVariableImpl*)Peek ())->GetValue (alpha, beta);
 }
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // ErlangVariableImpl
 class ErlangVariableImpl : public RandomVariableBase
 {
 public:
 /**
 * \return A random value from the Erlang distribution with parameters k and
 * lambda.
 */
-double GetValue(unsigned int k, double lambda);
+double GetValue (unsigned int k, double lambda);
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 unsigned int m_k;
 double m_lambda;
 };
 {
 return new ErlangVariableImpl (m_k, m_lambda);
 }
 ErlangVariableImpl::ErlangVariableImpl (unsigned int k, double lambda)
-: m_k(k), m_lambda(lambda)
+: m_k (k),
+m_lambda (lambda)
 {
 }
 double
 ErlangVariableImpl::GetValue ()
 {
-return GetValue(m_k, m_lambda);
+return GetValue (m_k, 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)
+x^(k-1) * exp(-x/lambda)
-	p(x; k, lambda) = ---------------------------
+p(x; k, lambda) = ---------------------------
 lambda^k * (k-1)!
 for x > 0.
 */
 double
 ErlangVariableImpl::GetValue (unsigned int k, double lambda)
 {
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
-ExponentialVariable exponential(lambda);
+ExponentialVariable exponential (lambda);
 double result = 0;
 for (unsigned int i = 0; i < k; ++i)
 {
-result += exponential.GetValue();
+result += exponential.GetValue ();
 }
 return result;
 }
 ErlangVariable::ErlangVariable (unsigned int k, double lambda)
 : RandomVariable (ErlangVariableImpl (k, lambda))
 {
 }
-double ErlangVariable::GetValue(void) const
+double ErlangVariable::GetValue (void) const
 {
 return this->RandomVariable::GetValue ();
 }
-double ErlangVariable::GetValue(unsigned int k, double lambda) const
+double ErlangVariable::GetValue (unsigned int k, double lambda) const
 {
-return ((ErlangVariableImpl*)Peek())->GetValue(k, lambda);
+return ((ErlangVariableImpl*)Peek ())->GetValue (k, lambda);
 }
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // TriangularVariableImpl methods
-class TriangularVariableImpl : public RandomVariableBase {
+class TriangularVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * Creates a triangle distribution random number generator in the
 * range [0.0 .. 1.0), with mean of 0.5
 */
-TriangularVariableImpl();
+TriangularVariableImpl ();
 /**
 * Creates a triangle distribution random number generator with the specified
 * range
 * \param s Low end of the range
 * \param l High end of the range
 * \param mean mean of the distribution
 */
-TriangularVariableImpl(double s, double l, double mean);
+TriangularVariableImpl (double s, double l, double mean);
-TriangularVariableImpl(const TriangularVariableImpl& c);
+TriangularVariableImpl (const TriangularVariableImpl& c);
 /**
 * \return A value from this distribution
 */
-virtual double GetValue();
+virtual double GetValue ();
-virtual RandomVariableBase*  Copy(void) const;
+virtual RandomVariableBase*  Copy (void) const;
 private:
 double m_min;
 double m_max;
-double m_mode;  //easier to work with the mode internally instead of the mean
+double m_mode;  // easier to work with the mode internally instead of the mean
-//they are related by the simple: mean = (min+max+mode)/3
+// they are related by the simple: mean = (min+max+mode)/3
 };
-TriangularVariableImpl::TriangularVariableImpl()
+TriangularVariableImpl::TriangularVariableImpl ()
-: m_min(0), m_max(1), m_mode(0.5) { }
+: m_min (0),
+m_max (1),
-TriangularVariableImpl::TriangularVariableImpl(double s, double l, double mean)
+m_mode (0.5)
-: m_min(s), m_max(l), m_mode(3.0*mean-s-l) { }
+{
+}
-TriangularVariableImpl::TriangularVariableImpl(const TriangularVariableImpl& c)
-: RandomVariableBase(c), m_min(c.m_min), m_max(c.m_max), m_mode(c.m_mode) { }
+TriangularVariableImpl::TriangularVariableImpl (double s, double l, double mean)
+: m_min (s),
-double TriangularVariableImpl::GetValue()
+m_max (l),
-{
+m_mode (3.0 * mean - s - l)
-if(!m_generator)
+{
-{
+}
-m_generator = new RngStream();
-}
+TriangularVariableImpl::TriangularVariableImpl (const TriangularVariableImpl& c)
-double u = m_generator->RandU01();
+: RandomVariableBase (c),
-if(u <= (m_mode - m_min) / (m_max - m_min) )
+m_min (c.m_min),
-{
+m_max (c.m_max),
-return m_min + sqrt(u * (m_max - m_min) * (m_mode - m_min) );
+m_mode (c.m_mode)
+{
+}
+double TriangularVariableImpl::GetValue ()
+{
+if (!m_generator)
+{
+m_generator = new RngStream ();
+}
+double u = m_generator->RandU01 ();
+if (u <= (m_mode - m_min) / (m_max - m_min) )
+{
+return m_min + sqrt (u * (m_max - m_min) * (m_mode - m_min) );
 }
 else
 {
-return m_max - sqrt( (1-u) * (m_max - m_min) * (m_max - m_mode) );
+return m_max - sqrt ( (1 - u) * (m_max - m_min) * (m_max - m_mode) );
 }
 }
-RandomVariableBase* TriangularVariableImpl::Copy() const
+RandomVariableBase* TriangularVariableImpl::Copy () const
 {
-return new TriangularVariableImpl(*this);
+return new TriangularVariableImpl (*this);
 }
-TriangularVariable::TriangularVariable()
+TriangularVariable::TriangularVariable ()
 : RandomVariable (TriangularVariableImpl ())
-{}
+{
-TriangularVariable::TriangularVariable(double s, double l, double mean)
+}
+TriangularVariable::TriangularVariable (double s, double l, double mean)
 : RandomVariable (TriangularVariableImpl (s,l,mean))
-{}
+{
+}
-//-----------------------------------------------------------------------------
-//-----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
 // ZipfVariableImpl
-class ZipfVariableImpl : public RandomVariableBase {
+class ZipfVariableImpl : public RandomVariableBase
+{
 public:
 /**
 * \param n the number of possible items
 * \param alpha the alpha parameter
 */
 /**
 * \return A random value from this distribution
 */
 virtual double GetValue ();
-virtual RandomVariableBase* Copy(void) const;
+virtual RandomVariableBase* Copy (void) const;
 private:
 long m_n;
 double m_alpha;
-double m_c; //the normalization constant
+double m_c; // the normalization constant
 };
 RandomVariableBase* ZipfVariableImpl::Copy () const
 {
 return new ZipfVariableImpl (m_n, m_alpha);
 }
 ZipfVariableImpl::ZipfVariableImpl ()
-:m_n(1), m_alpha(0), m_c(1)
+: m_n (1),
+m_alpha (0),
+m_c (1)
 {
 }
 ZipfVariableImpl::ZipfVariableImpl (long n, double alpha)
-:m_n(n), m_alpha(alpha), m_c(0)
+: m_n (n),
-{
+m_alpha (alpha),
-//calculate the normalization constant c
+m_c (0)
-for(int i=1;i<=n;i++)
+{
-{
+// calculate the normalization constant c
-m_c+=(1.0/pow((double)i,alpha));
+for (int i = 1; i <= n; i++)
-}
+{
-m_c=1.0/m_c;
+m_c += (1.0 / pow ((double)i,alpha));
+}
+m_c = 1.0 / m_c;
 }
 double
 ZipfVariableImpl::GetValue ()
 {
-if(!m_generator)
+if (!m_generator)
 {
-m_generator = new RngStream();
+m_generator = new RngStream ();
 }
-double u = m_generator->RandU01();
+double u = m_generator->RandU01 ();
-double sum_prob=0,zipf_value=0;
+double sum_prob = 0,zipf_value = 0;
-for(int i=1;i<=m_n;i++)
+for (int i = 1; i <= m_n; i++)
 {
-sum_prob+=m_c/pow((double)i,m_alpha);
+sum_prob += m_c / pow ((double)i,m_alpha);
-if(sum_prob>u)
+if (sum_prob > u)
 {
-zipf_value=i;
+zipf_value = i;
 break;
 }
 }
 return zipf_value;
 }
 ZipfVariable::ZipfVariable ()
 : RandomVariable (ZipfVariableImpl ())
-{}
+{
+}
 ZipfVariable::ZipfVariable (long n, double alpha)
 : RandomVariable (ZipfVariableImpl (n, alpha))
-{}
+{
+}
-std::ostream &operator << (std::ostream &os, const RandomVariable &var)
+// -----------------------------------------------------------------------------
+// -----------------------------------------------------------------------------
+// ZetaVariableImpl
+class ZetaVariableImpl : public RandomVariableBase
+{
+public:
+/**
+* \param alpha the alpha parameter
+*/
+ZetaVariableImpl (double alpha);
+/**
+* \A zipf variable with alpha=1.1
+*/
+ZetaVariableImpl ();
+/**
+* \return A random value from this distribution
+*/
+virtual double GetValue ();
+virtual RandomVariableBase* Copy (void) const;
+private:
+double m_alpha;
+double m_b; // just for calculus simplifications
+};
+RandomVariableBase* ZetaVariableImpl::Copy () const
+{
+return new ZetaVariableImpl (m_alpha);
+}
+ZetaVariableImpl::ZetaVariableImpl ()
+: m_alpha (3.14),
+m_b (pow (2.0, 2.14))
+{
+}
+ZetaVariableImpl::ZetaVariableImpl (double alpha)
+: m_alpha (alpha),
+m_b (pow (2.0, alpha - 1.0))
+{
+}
+/*
+The code for the following generator functions is borrowed from:
+L. Devroye: Non-Uniform Random Variate Generation, Springer-Verlag, New York, 1986.
+page 551
+*/
+double
+ZetaVariableImpl::GetValue ()
+{
+if (!m_generator)
+{
+m_generator = new RngStream ();
+}
+double u, v;
+double X, T;
+double test;
+do
+{
+u = m_generator->RandU01 ();
+v = m_generator->RandU01 ();
+X = floor (pow (u, -1.0 / (m_alpha - 1.0)));
+T = 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;
+}
+ZetaVariable::ZetaVariable ()
+: RandomVariable (ZetaVariableImpl ())
+{
+}
+ZetaVariable::ZetaVariable (double alpha)
+: RandomVariable (ZetaVariableImpl (alpha))
+{
+}
+std::ostream & operator << (std::ostream &os, const RandomVariable &var)
 {
 RandomVariableBase *base = var.Peek ();
 ConstantVariableImpl *constant = dynamic_cast<ConstantVariableImpl *> (base);
 if (constant != 0)
 {
 }
 // XXX: support other distributions
 os.setstate (std::ios_base::badbit);
 return os;
 }
-std::istream &operator >> (std::istream &is, RandomVariable &var)
+std::istream & operator >> (std::istream &is, RandomVariable &var)
 {
 std::string value;
 is >> value;
 std::string::size_type tmp;
 tmp = value.find (":");
 class BasicRandomNumberTestCase : public TestCase
 {
 public:
 BasicRandomNumberTestCase ();
-virtual ~BasicRandomNumberTestCase () {}
+virtual ~BasicRandomNumberTestCase ()
+{
+}
 private:
 virtual bool DoRun (void);
 };
 //
 LogNormalVariable lognormal (mu, sigma);
 vector<double> samples;
 const int NSAMPLES = 10000;
 double sum = 0;
 //
 // Get and store a bunch of samples.  As we go along sum them and then find
 // the mean value of the samples.
 //
 for (int n = NSAMPLES; n; --n)
 sum += value;
 samples.push_back (value);
 }
 double obtainedMean = sum / NSAMPLES;
 NS_TEST_EXPECT_MSG_EQ_TOL (obtainedMean, desiredMean, 0.1, "Got unexpected mean value from LogNormalVariable");
 //
 // Wander back through the saved stamples and find their standard deviation
 //
 sum = 0;
 for (vector<double>::iterator iter = samples.begin (); iter != samples.end (); iter++)
 class RandomNumberSerializationTestCase : public TestCase
 {
 public:
 RandomNumberSerializationTestCase ();
-virtual ~RandomNumberSerializationTestCase () {}
+virtual ~RandomNumberSerializationTestCase ()
+{
+}
 private:
 virtual bool DoRun (void);
 };
 RandomNumberSerializationTestCase::DoRun (void)
 {
 RandomVariableValue val;
 val.DeserializeFromString ("Uniform:0.1:0.2", MakeRandomVariableChecker ());
 RandomVariable rng = val.Get ();
 NS_TEST_ASSERT_MSG_EQ (val.SerializeToString (MakeRandomVariableChecker ()), "Uniform:0.1:0.2",
 "Deserialize and Serialize \"Uniform:0.1:0.2\" mismatch");
 val.DeserializeFromString ("Normal:0.1:0.2", MakeRandomVariableChecker ());
 rng = val.Get ();
 NS_TEST_ASSERT_MSG_EQ (val.SerializeToString (MakeRandomVariableChecker ()), "Normal:0.1:0.2",
 AddTestCase (new RandomNumberSerializationTestCase);
 }
 BasicRandomNumberTestSuite BasicRandomNumberTestSuite;
-}//namespace ns3
+} // namespace ns3

changeset 5969	68bd5111f38c
parent 5299	67ea1b4dd3fe
child 6094	0ac9a2fffbfb