Random number generators


Files
file	rndgen.h
	Random number generator stub functions and classes.
Classes
class	UniformDETimeRndGen
	uniform DETime random generator More...
class	ExponentialDETimeRndGen
	exponential DETime random generator More...
class	UniformDoubleRndGen
	uniform double random generator More...
class	UniformIntRndGen
	uniform long int random generator More...
class	ExponentialRndGen
	exponential random generator More...
class	GaussianRndGen
	Gaussian random generator. More...
class	Geometric0RndGen
	Geometric random generator. More...
class	Geometric1RndGen
	Geometric random generator. More...
class	ParetoRndGen
	Pareto random generator. More...
class	WeibullRndGen
	Weibull random generator. More...
class	RayleighRndGen
	Rayleigh random generator. More...
Functions
void	SetSeed (char *genType, int seed)
	Set the random generator type and the seed.
double	rndUniformDouble (double low=0.0, double high=1.0)
	uniform double random generator
long int	rndUniformInt (long int low=0, long int high=1)
	uniform long int random generator
double	rndExponential (double M)
	exponential random generator
double	rndGaussian (double M, double sigma)
	Gaussian random generator.
unsigned int	rndGeometric0 (double M)
	Geometric random generator.
unsigned int	rndGeometric1 (double M)
	Geometric random generator.
long int	rndNegativeBinomial (long r, float p)
	Negative Binomial random generator.
double	rndPareto (double location, double shape)
	Pareto random generator.
double	rndWeibull (double location, double scale, double shape)
	Weibull random generator.
double	rndRayleigh (double sigma)
	Rayleigh random generator.
void	rndShuffle (void *vect, size_t size, size_t elem_size)
	Shuffle an array.

Function Documentation

double rndExponential ( double M )

exponential random generator
random generation of a double, exponentally distributed
Parameters:

M mean value of the exponential distribution

Returns:
a double

double rndGaussian ( double M,

double sigma

)

Gaussian random generator.
random generation of a double, following a Gaussian distribution
Parameters:

M mean value of the Gaussian distribution

sigma variance of the Gaussian distribution

Returns:
a double

unsigned int rndGeometric0 ( double M )

Geometric random generator.
random generation of an unsigned int, following a geometric distribution between 0 and infinity
Parameters:

M mean value of the geometric distribution

Returns:
an unsigned int

unsigned int rndGeometric1 ( double M )

Geometric random generator.
random generation of an unsigned int, following a geometric distribution between 1 and infinity
Parameters:

M mean value of the geometric distribution

Returns:
an unsigned int

long int rndNegativeBinomial ( long r,

float p

)

Negative Binomial random generator.
random generation of a long int, following a negative binomial distribution with PDF:
$p(x) = \left( \begin{array}{c} k + r -1 \\k \end{array} \right) p^r (1-p)^k$

Parameters:

r refers to the above formula

p refers to the above formula

mean and variance of the negative binomial distribution are:
$E[x] = r \frac{1-p}{p} \qquad \qquad \qquad \sigma^2 = r \frac{1-p}{p^2}$

Returns:
an unsigned int

double rndPareto ( double location,

double shape

)

Pareto random generator.
random generation of a double, following a Pareto distribution with PDF and CDF:
$p(x) = \frac{shape \, location^{shape}}{x^{shape+1}} \qquad \qquad \qquad P(x) = 1 - \left(\frac{location}{x}\right)^{shape}$

Parameters:

location refers to the above formula

shape refers to the above formula

mean and variance of the Pareto distribution are:
$E[x] = \frac{location \, shape}{shape - 1} \qquad \qquad \qquad \sigma^2 = \frac{location^2 \, shape} { (shape -2) (shape - 1)^2 }$

Returns:
a double

double rndRayleigh ( double sigma )

Rayleigh random generator.
random generation of a double, following a Rayleigh distribution with PDF and CDF:
$p(x) = \frac{x}{sigma^2} e^{-x^2/(2 sigma^2)} \qquad \qquad \qquad P(x) = 1 - e^{-x^2/(2 sigma^2)}$

Parameters:

sigma refers to the above formula

mean and variance of the Rayleigh distribution are:
$E[x] = sigma \sqrt{\frac{\pi}{2}} \qquad \qquad \qquad \sigma^2 = sigma \frac{4-\pi}{2}$

Returns:
a double

void rndShuffle ( void * vect,

size_t size,

size_t elem_size

)

Shuffle an array.
This function randomly shuffles the order of elem_size objects, each of size size, stored in the array vect[0..n-1].
Parameters:

vect the array to be shuffled

size the size of each element of the array

elem_size the number of elements of the array

double rndUniformDouble ( double low = 0.0,

double high = 1.0

)

uniform double random generator
random generation of a double, uniformly distributed between [low, high)
Parameters:

low lower bound of the generation (inclusive)

high upper bound of the generation (exclusive)

Returns:
a double

long int rndUniformInt ( long int low = 0,

long int high = 1

)

uniform long int random generator
random generation of a long integer, uniformly distributed between [low, high]
Parameters:

low lower bound of the generation (inclusive)

high upper bound of the generation (inclusive)

Returns:
a long int

double rndWeibull ( double location,

double scale,

double shape

)

Weibull random generator.
random generation of a double, following a Weibull distribution with PDF and CDF:
$p(x) = \frac{shape}{scale^{shape}} x^{shape-1} e^{-((x-location)/scale)^{shape}} \qquad \qquad \qquad P(x) = 1 - e^{-((x-location)/scale)^{shape}}$

Parameters:

location refers to the above formula

scale refers to the above formula

shape refers to the above formula

mean and variance of the Weibull distribution are:
$E[x] = location + scale \, \Gamma\left(\frac{shape+1}{shape}\right) \qquad \qquad \qquad \sigma^2 = scale^2 \, ( \Gamma\left(\frac{shape+2}{shape}\right) - \Gamma^2\left(\frac{shape+1}{shape}\right) )$

Returns:
a double

void SetSeed ( char * genType,

int seed

)

Set the random generator type and the seed.

Parameters:

genType is the random generator type as specified by GSL constants;

seed is the seed of the generator.

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Random number generators

Files

Classes

Functions

Function Documentation