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RandomNumberGenerator< Engine > Class Template Reference

#include <vigra/random.hxx>

List of all members.

Public Member Functions

double normal () const
double normal (double mean, double stddev) const
UInt32 operator() () const
 RandomNumberGenerator ()
 RandomNumberGenerator (RandomSeedTag)
template<class Iterator >
 RandomNumberGenerator (Iterator init, UInt32 length)
 RandomNumberGenerator (UInt32 theSeed)
void seed (RandomSeedTag)
void seed (UInt32 theSeed)
template<class Iterator >
void seed (Iterator init, UInt32 length)
double uniform () const
double uniform (double lower, double upper) const
double uniform53 () const
UInt32 uniformInt () const
UInt32 uniformInt (UInt32 beyond) const

Static Public Member Functions

static RandomNumberGeneratorglobal ()

Detailed Description

template<class Engine = detail::RandomState<detail::TT800>>
class vigra::RandomNumberGenerator< Engine >

Generic random number generator.

The actual generator is passed in the template argument Engine. Two generators are currently available:

Both generators have been designed by Makoto Matsumoto.

Traits defined:

FunctorTraits<RandomNumberGenerator<Engine> >::isInitializer is true (VigraTrueType).


Constructor & Destructor Documentation

Create a new random generator object with standard seed.

Due to standard seeding, the random numbers generated will always be the same. This is useful for debugging.

RandomNumberGenerator ( RandomSeedTag  )

Create a new random generator object with a random seed.

The seed is obtained from the machines current clock() and time() values.

Usage:

            RandomNumberGenerator<> rnd = RandomNumberGenerator<>(RandomSeed);

Create a new random generator object from the given seed.

The same seed will always produce identical random sequences.

RandomNumberGenerator ( Iterator  init,
UInt32  length 
)

Create a new random generator object from the given seed sequence.

Longer seed sequences lead to better initialization in the sense that the generator's state space is covered much better than is possible with 32-bit seeds alone.


Member Function Documentation

void seed ( RandomSeedTag  )

Re-initialize the random generator object with a random seed.

The seed is obtained from the machines current clock() and time() values.

Usage:

            RandomNumberGenerator<> rnd = ...;
            ...
            rnd.seed(RandomSeed);
void seed ( UInt32  theSeed)

Re-initialize the random generator object from the given seed.

The same seed will always produce identical random sequences.

void seed ( Iterator  init,
UInt32  length 
)

Re-initialize the random generator object from the given seed sequence.

Longer seed sequences lead to better initialization in the sense that the generator's state space is covered much better than is possible with 32-bit seeds alone.

UInt32 operator() ( ) const

Return a uniformly distributed integer random number in [0, 232).

That is, 0 <= i < 232.

UInt32 uniformInt ( ) const

Return a uniformly distributed integer random number in [0, 232).

That is, 0 <= i < 232.

UInt32 uniformInt ( UInt32  beyond) const

Return a uniformly distributed integer random number in [0, beyond).

That is, 0 <= i < beyond.

double uniform53 ( ) const

Return a uniformly distributed double-precision random number in [0.0, 1.0).

That is, 0.0 <= i < 1.0. All 53-bit bits of the mantissa are random (two 32-bit integers are used to create this number).

double uniform ( ) const

Return a uniformly distributed double-precision random number in [0.0, 1.0].

That is, 0.0 <= i <= 1.0. This nuber is computed by uniformInt() / 232, so it has effectively only 32 random bits.

double uniform ( double  lower,
double  upper 
) const

Return a uniformly distributed double-precision random number in [lower, upper].

That is, lower <= i <= upper. This number is computed from uniform(), so it has effectively only 32 random bits.

double normal ( double  mean,
double  stddev 
) const

Return a normal variate (Gaussian) random number with the given mean and standard deviation.

It uses the polar form of the Box-Muller transform.

static RandomNumberGenerator& global ( ) [static]

Access the global (program-wide) instance of the present random number generator.

Normally, you will create a local generator by one of the constructor calls. But sometimes it is useful to have all program parts access the same generator.


The documentation for this class was generated from the following file:

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de)
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

html generated using doxygen and Python
vigra 1.7.0 (Thu Aug 25 2011)