RWTRandPoisson< Generator > Class Template Reference

Used to generate random numbers from a Poisson distribution. More...

#include <rw/rand.h>

Inheritance diagram for RWTRandPoisson< Generator >:

Public Member Functions
	RWTRandPoisson (const Generator &g, double m=1)
	RWTRandPoisson (const RWTRandPoisson< Generator > &g)
	RWTRandPoisson (double m=1)
double	mean () const
virtual double	operator() ()
virtual size_t	operator() (double *out, size_t n)
void	setMean (double m)
Public Member Functions inherited from RWTRand< Generator >
	RWTRand ()
	RWTRand (const Generator &g)
	RWTRand (const RWTRand< Generator > &x)
Generator &	generator ()
const Generator &	generator () const
void	setGenerator (const Generator &x)
void	setGenerator (const RWTRand< Generator > &x)

Detailed Description

template<class Generator>
class RWTRandPoisson< Generator >

Class RWTRandPoisson generates random numbers from a Poisson distribution:

\[ f(x) = \left\{\begin{matrix} \frac{\mu^xe^{- \mu}}{x!} & \text{x = 0, 1, 2, ...} \\ \text{0} & \text{otherwise} \end{matrix}\right. \]

where \( \mu\) is the mean. The algorithm used to generate the random numbers begins with random numbers generated from a uniform distribution on the interval [0, 1]. Thus, the speed and statistical properties of the Poisson random number generator may be varied by varying the Generator template parameter, which is responsible for generating the uniformly distributed numbers.

The class Generator must be a function object whose function call operator returns a uniform random double between 0 and 1. The class RWRandGenerator supplied with the library may be used.

Synopsis

#include <rw/rand.h>
RWTRandPoisson<Generator> gen;

Example

#include <rw/rand.h>
#include <iostream.h>
#include "myrand.h"
 
int main() {
  // Create a uniform generator on [0, 1] with an
  // initial seed value.
  RWRandGenerator uniform(123456L);
 
  // Create a Poisson generator with mean = 4
  // using the above uniform generator for generator.
  RWTRandPoisson<RWRandGenerator> poisson(uniform, 4);
 
  // Create a Poisson generator with mean = 1 using class
  // RWRandGenerator.
  RWTRandPoisson<RWRandGenerator> poissonOne;
 
  // Create a poisson generator with mean 31 using a class
  // MyDoubleRand to generate the uniform [0, 1] deviates.
  RWTRandPoisson<MyDoubleRand> myPoisson(31);
 
  // Print a few values
  for (int j = 0; j < 10; j++) {
    cout << "poisson mean = 4 = " << gamma();
    cout << "\npoisson mean = 1 = " << gammaOne();
    cout << "\npoisson mean = 31 = " << myGamma() << endl;
  }
 
  // Restart one of the generators with another seed value
  // using the RWRandGenerator method restart().
  (poissonOne.generator()).restart(654321L);
 
  return 0;
}

Constructor & Destructor Documentation

RWTRandPoisson() [1/3]

template<class Generator >

RWTRandPoisson< Generator >::RWTRandPoisson

(

double

m = 1

)

Constructs a generator with mean = m.

RWTRandPoisson() [2/3]

template<class Generator >

RWTRandPoisson< Generator >::RWTRandPoisson

(

const RWTRandPoisson< Generator > &

g

)

Constructs self as a copy of g.

RWTRandPoisson() [3/3]

template<class Generator >

RWTRandPoisson< Generator >::RWTRandPoisson	(	const Generator &	g,
		double	m = 1 )

Constructs a generator with underlying uniform [0, 1] generator g and mean m.

Member Function Documentation

mean()

template<class Generator >

double RWTRandPoisson< Generator >::mean ( ) const

inline

Returns the value of mean for the distribution.

operator()() [1/2]

template<class Generator >

virtual double RWTRandPoisson< Generator >::operator() ( )

virtual

Returns the next random number in the sequence.

Implements RWRandInterface.

operator()() [2/2]

template<class Generator >

virtual size_t RWTRandPoisson< Generator >::operator() ( double * out, size_t n )

virtual

Assigns random numbers to n elements in out. Returns the number of random numbers generated.

Implements RWRandInterface.

setMean()

template<class Generator >

void RWTRandPoisson< Generator >::setMean

(

double

m

)

Sets the value of the mean of the distribution to m.