mrpt::random::CRandomGenerator Class Reference

A thred-safe pseudo random number generator, based on an internal MT19937 randomness generator. More...

#include <mrpt/random/RandomGenerators.h>

Collaboration diagram for mrpt::random::CRandomGenerator:

Classes
struct	TMT19937_data
	Data used internally by the MT19937 PRNG algorithm. More...
Public Member Functions
Initialization

	CRandomGenerator ()
	Default constructor: initialize random seed based on current time.
	CRandomGenerator (const uint32_t seed)
	Constructor for providing a custom random seed to initialize the PRNG.
void	randomize (const uint32_t seed)
	Initialize the PRNG from the given random seed.
void	randomize ()
	Randomize the generators, based on current time.
Uniform pdf

uint32_t	drawUniform32bit ()
	Generate a uniformly distributed pseudo-random number using the MT19937 algorithm, in the whole range of 32-bit integers.
double	drawUniform (const double Min, const double Max)
	Generate a uniformly distributed pseudo-random number using the MT19937 algorithm, scaled to the selected range.
template<class MAT >
void	drawUniformMatrix (MAT &matrix, const double unif_min=0, const double unif_max=1)
	Fills the given matrix with independent, uniformly distributed samples.
template<class T >
void	drawUniformVector (std::vector< T > &v, const double unif_min=0, const double unif_max=1)
	Fills the given vector with independent, uniformly distributed samples.
Normal/Gaussian pdf

double	drawGaussian1D_normalized (double *likelihood=NULL)
	Generate a normalized (mean=0, std=1) normally distributed sample.
double	drawGaussian1D (const double mean, const double std)
	Generate a normally distributed pseudo-random number.
template<class MAT >
void	drawGaussian1DMatrix (MAT &matrix, const double mean=0, const double std=1)
	Fills the given matrix with independent, 1D-normally distributed samples.
template<class T >
void	drawGaussian1DVector (std::vector< T > &v, const double mean=0, const double std=1)
	Fills the given vector with independent, 1D-normally distributed samples.
template<typename T >
void	drawGaussianMultivariate (std::vector< T > &out_result, const CMatrixTemplateNumeric< T > &cov, const std::vector< T > *mean=NULL)
	Generate multidimensional random samples according to a given covariance matrix.
template<typename VECTORLIKE , typename MATRIXLIKE >
void	drawGaussianMultivariateMany (std::vector< VECTORLIKE > &ret, size_t desiredSamples, const MATRIXLIKE &cov, const VECTORLIKE mean=NULL, VECTORLIKE samplesLikelihoods=NULL)
	Generate a given number of multidimensional random samples according to a given covariance matrix.
template<class VECTORLIKE , typename T , size_t N>
void	drawGaussianMultivariate (VECTORLIKE &out_result, const CMatrixFixedNumeric< T, N, N > &cov, const VECTORLIKE *mean=NULL)
	Generate multidimensional random samples according to a given covariance matrix.
template<typename T , size_t N>
void	drawGaussianMultivariateMany (std::vector< std::vector< T > > &ret, size_t desiredSamples, const CMatrixFixedNumeric< T, N, N > &cov, const std::vector< T > *mean=NULL)
	Generate a given number of multidimensional random samples according to a given covariance matrix.
Miscellaneous

template<class T >
void	permuteVector (const std::vector< T > &in_vector, std::vector< T > &out_result)
	Returns a random permutation of a vector: all the elements of the input vector are in the output but at random positions.
Protected Member Functions
void	MT19937_generateNumbers ()
void	MT19937_initializeGenerator (const uint32_t &seed)
Protected Attributes
struct mrpt::random::CRandomGenerator::TMT19937_data	m_MT19937_data
	Data used internally by the MT19937 PRNG algorithm.

Detailed Description

A thred-safe pseudo random number generator, based on an internal MT19937 randomness generator.

The base algorithm for randomness is platform-independent. See http://en.wikipedia.org/wiki/Mersenne_twister

For real thread-safety, each thread must create and use its own instance of this class.

Single-thread programs can use the static object mrpt::random::randomGenerator

Definition at line 51 of file RandomGenerators.h.

Constructor & Destructor Documentation

mrpt::random::CRandomGenerator::CRandomGenerator ( ) [inline]

Default constructor: initialize random seed based on current time.

Definition at line 73 of file RandomGenerators.h.

mrpt::random::CRandomGenerator::CRandomGenerator ( const uint32_t seed ) [inline]

Constructor for providing a custom random seed to initialize the PRNG.

Definition at line 76 of file RandomGenerators.h.

Member Function Documentation

double mrpt::random::CRandomGenerator::drawGaussian1D	(	const double	mean,
		const double	std
	)			`[inline]`

Generate a normally distributed pseudo-random number.

Parameters:

	mean	The mean value of desired normal distribution
	std	The standard deviation value of desired normal distribution

Definition at line 138 of file RandomGenerators.h.

double mrpt::random::CRandomGenerator::drawGaussian1D_normalized ( double * likelihood = NULL )

Generate a normalized (mean=0, std=1) normally distributed sample.

Parameters:

likelihood

If desired, pass a pointer to a double which will receive the likelihood of the given sample to have been obtained, that is, the value of the normal pdf at the sample value.

Referenced by mrpt::random::matrixRandomNormal(), and mrpt::random::vectorRandomNormal().

template<class MAT >

void mrpt::random::CRandomGenerator::drawGaussian1DMatrix	(	MAT &	matrix,
		const double	mean = `0`,
		const double	std = `1`
	)			`[inline]`

Fills the given matrix with independent, 1D-normally distributed samples.

Matrix classes can be CMatrixTemplateNumeric or CMatrixFixedNumeric

See also:: drawGaussian1D

Definition at line 147 of file RandomGenerators.h.

References mrpt::math::mean().

template<class T >

void mrpt::random::CRandomGenerator::drawGaussian1DVector	(	std::vector< T > &	v,
		const double	mean = `0`,
		const double	std = `1`
	)			`[inline]`

Fills the given vector with independent, 1D-normally distributed samples.

See also:: drawGaussian1D

Definition at line 161 of file RandomGenerators.h.

References mrpt::math::mean().

template<class VECTORLIKE , typename T , size_t N>

void mrpt::random::CRandomGenerator::drawGaussianMultivariate	(	VECTORLIKE &	out_result,
		const CMatrixFixedNumeric< T, N, N > &	cov,
		const VECTORLIKE *	mean = `NULL`
	)			`[inline]`

Generate multidimensional random samples according to a given covariance matrix.

Mean is assumed to be zero if mean==NULL.

Exceptions:

std::exception

On invalid covariance matrix

See also:: drawGaussianMultivariateMany

Computes the eigenvalues/eigenvector decomposition of this matrix, so that: M = Z · D · Z^T, where columns in Z are the eigenvectors and the diagonal matrix D contains the eigenvalues as diagonal elements, sorted in ascending order.

Definition at line 296 of file RandomGenerators.h.

References ASSERT_, and mrpt::math::mean().

template<typename T >

void mrpt::random::CRandomGenerator::drawGaussianMultivariate	(	std::vector< T > &	out_result,
		const CMatrixTemplateNumeric< T > &	cov,
		const std::vector< T > *	mean = `NULL`
	)			`[inline]`

Generate multidimensional random samples according to a given covariance matrix.

Mean is assumed to be zero if mean==NULL.

Exceptions:

std::exception

On invalid covariance matrix

See also:: drawGaussianMultivariateMany

Referenced by mrpt::random::randomNormalMultiDimensional().

template<typename T , size_t N>

void mrpt::random::CRandomGenerator::drawGaussianMultivariateMany	(	std::vector< std::vector< T > > &	ret,
		size_t	desiredSamples,
		const CMatrixFixedNumeric< T, N, N > &	cov,
		const std::vector< T > *	mean = `NULL`
	)			`[inline]`

Generate a given number of multidimensional random samples according to a given covariance matrix.

Parameters:

	cov	The covariance matrix where to draw the samples from.
	desiredSamples	The number of samples to generate.
	ret	The output list of samples
	mean	The mean, or zeros if mean==NULL.

Computes the eigenvalues/eigenvector decomposition of this matrix, so that: M = Z · D · Z^T, where columns in Z are the eigenvectors and the diagonal matrix D contains the eigenvalues as diagonal elements, sorted in ascending order.

Definition at line 344 of file RandomGenerators.h.

References ASSERT_, and mrpt::math::mean().

template<typename VECTORLIKE , typename MATRIXLIKE >

void mrpt::random::CRandomGenerator::drawGaussianMultivariateMany	(	std::vector< VECTORLIKE > &	ret,
		size_t	desiredSamples,
		const MATRIXLIKE &	cov,
		const VECTORLIKE *	mean = `NULL`,
		VECTORLIKE *	samplesLikelihoods = `NULL`
	)			`[inline]`

Generate a given number of multidimensional random samples according to a given covariance matrix.

Parameters:

	cov	The covariance matrix where to draw the samples from.
	desiredSamples	The number of samples to generate.
	samplesLikelihoods	If desired, set to a valid pointer to a vector, where it will be stored the likelihoods of having obtained each sample: the product of the gaussian-pdf for each independent variable.
	ret	The output list of samples
	mean	The mean, or zeros if mean==NULL.

Exceptions:

std::exception

On invalid covariance matrix

See also:: drawGaussianMultivariate

Computes the eigenvalues/eigenvector decomposition of this matrix, so that: M = Z · D · Z^T, where columns in Z are the eigenvectors and the diagonal matrix D contains the eigenvalues as diagonal elements, sorted in ascending order.

Definition at line 195 of file RandomGenerators.h.

References ASSERT_, mrpt::math::mean(), MRPT_END_WITH_CLEAN_UP, and MRPT_START.

Referenced by mrpt::random::randomNormalMultiDimensionalMany(), and mrpt::math::transform_gaussian_montecarlo().

double mrpt::random::CRandomGenerator::drawUniform	(	const double	Min,
		const double	Max
	)			`[inline]`

Generate a uniformly distributed pseudo-random number using the MT19937 algorithm, scaled to the selected range.

Definition at line 91 of file RandomGenerators.h.

Referenced by mrpt::random::matrixRandomUni(), mrpt::slam::PF_implementation< PARTICLE_TYPE >::PF_SLAM_aux_perform_one_rejection_sampling_step(), and mrpt::random::vectorRandomUni().

uint32_t mrpt::random::CRandomGenerator::drawUniform32bit ( )

Generate a uniformly distributed pseudo-random number using the MT19937 algorithm, in the whole range of 32-bit integers.

See: http://en.wikipedia.org/wiki/Mersenne_twister

Referenced by mrpt::slam::PF_implementation< PARTICLE_TYPE >::PF_SLAM_aux_perform_one_rejection_sampling_step(), mrpt::slam::PF_implementation< PARTICLE_TYPE >::PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal(), and mrpt::random::random_generator_for_STL().

template<class MAT >

void mrpt::random::CRandomGenerator::drawUniformMatrix	(	MAT &	matrix,
		const double	unif_min = `0`,
		const double	unif_max = `1`
	)			`[inline]`

Fills the given matrix with independent, uniformly distributed samples.

Matrix classes can be CMatrixTemplateNumeric or CMatrixFixedNumeric

See also:: drawUniform

Definition at line 100 of file RandomGenerators.h.

template<class T >

void mrpt::random::CRandomGenerator::drawUniformVector	(	std::vector< T > &	v,
		const double	unif_min = `0`,
		const double	unif_max = `1`
	)			`[inline]`

Fills the given vector with independent, uniformly distributed samples.

See also:: drawUniform

Definition at line 114 of file RandomGenerators.h.

void mrpt::random::CRandomGenerator::MT19937_generateNumbers ( ) [protected]

void mrpt::random::CRandomGenerator::MT19937_initializeGenerator ( const uint32_t & seed ) [protected]

template<class T >

void mrpt::random::CRandomGenerator::permuteVector	(	const std::vector< T > &	in_vector,
		std::vector< T > &	out_result
	)			`[inline]`

Returns a random permutation of a vector: all the elements of the input vector are in the output but at random positions.

Definition at line 398 of file RandomGenerators.h.

Referenced by mrpt::random::randomPermutation().

void mrpt::random::CRandomGenerator::randomize ( )

Randomize the generators, based on current time.

void mrpt::random::CRandomGenerator::randomize ( const uint32_t seed )

Initialize the PRNG from the given random seed.

Referenced by mrpt::random::Randomize().

Member Data Documentation

struct mrpt::random::CRandomGenerator::TMT19937_data mrpt::random::CRandomGenerator::m_MT19937_data [protected]

Data used internally by the MT19937 PRNG algorithm.

mrpt::random::CRandomGenerator Class Reference

Classes

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation

Member Data Documentation