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 47 of file RandomGenerators.h.

#include <mrpt/random/RandomGenerators.h>

Public Member Functions
Initialization
	CRandomGenerator ()
	Default constructor: initialize random seed based on current time. More...

	CRandomGenerator (const uint32_t seed)
	Constructor for providing a custom random seed to initialize the PRNG. More...

void	randomize (const uint32_t seed)
	Initialize the PRNG from the given random seed. More...

void	randomize ()
	Randomize the generators, based on std::random_device. More...

Uniform pdf
uint32_t	drawUniform32bit ()
	Generate a uniformly distributed pseudo-random number using the MT19937 algorithm, in the whole range of 32-bit integers. More...

uint64_t	drawUniform64bit ()
	Returns a uniformly distributed pseudo-random number by joining two 32bit numbers from drawUniform32bit() More...

void	drawUniformUnsignedInt (uint32_t &ret_number)
	You can call this overloaded method with either 32 or 64bit unsigned ints for the sake of general coding. More...

void	drawUniformUnsignedInt (uint64_t &ret_number)

template<typename T , typename U , typename V >
void	drawUniformUnsignedIntRange (T &ret_number, const U min_val, const V max_val)
	Return a uniform unsigned integer in the range [min_val,max_val] (both inclusive) More...

double	drawUniform (const double Min, const double Max)
	Generate a uniformly distributed pseudo-random number using the MT19937 algorithm, scaled to the selected range. More...

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. More...

template<class VEC >
void	drawUniformVector (VEC &v, const double unif_min=0, const double unif_max=1)
	Fills the given vector with independent, uniformly distributed samples. More...

Normal/Gaussian pdf
double	drawGaussian1D_normalized ()
	Generate a normalized (mean=0, std=1) normally distributed sample. More...

double	drawGaussian1D (const double mean, const double std)
	Generate a normally distributed pseudo-random number. More...

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. More...

template<class MATRIX , class AUXVECTOR_T = MATRIX>
MATRIX	drawDefinitePositiveMatrix (const size_t dim, const double std_scale=1.0, const double diagonal_epsilon=1e-8)
	Generates a random definite-positive matrix of the given size, using the formula C = vv^t + epsilonI, with "v" being a vector of gaussian random samples. More...

template<class VEC >
void	drawGaussian1DVector (VEC &v, const double mean=0, const double std=1)
	Fills the given vector with independent, 1D-normally distributed samples. More...

template<typename T , typename MATRIX >
void	drawGaussianMultivariate (std::vector< T > &out_result, const MATRIX &cov, const std::vector< T > *mean=nullptr)
	Generate multidimensional random samples according to a given covariance matrix. More...

template<class VECTORLIKE , class COVMATRIX >
void	drawGaussianMultivariate (VECTORLIKE &out_result, const COVMATRIX &cov, const VECTORLIKE *mean=nullptr)
	Generate multidimensional random samples according to a given covariance matrix. More...

template<typename VECTOR_OF_VECTORS , typename COVMATRIX >
void	drawGaussianMultivariateMany (VECTOR_OF_VECTORS &ret, size_t desiredSamples, const COVMATRIX &cov, const typename VECTOR_OF_VECTORS::value_type *mean=nullptr)
	Generate a given number of multidimensional random samples according to a given covariance matrix. More...

Miscellaneous
template<class VEC >
void	permuteVector (const VEC &in_vector, VEC &out_result)
	Returns a random permutation of a vector: all the elements of the input vector are in the output but at random positions. More...

Protected Member Functions
void	MT19937_initializeGenerator (const uint32_t &seed)

Protected Attributes
std::mt19937_64	m_MT19937
	Data used internally by the MT19937 PRNG algorithm. More...

std::normal_distribution< double >	m_normdistribution

std::uniform_int_distribution< uint32_t >	m_uint32

std::uniform_int_distribution< uint64_t >	m_uint64

Constructor & Destructor Documentation

◆ CRandomGenerator() [1/2]

mrpt::random::CRandomGenerator::CRandomGenerator ( )

inline

Default constructor: initialize random seed based on current time.

Definition at line 64 of file RandomGenerators.h.

References randomize().

◆ CRandomGenerator() [2/2]

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

inline

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

Definition at line 66 of file RandomGenerators.h.

References randomize().

Member Function Documentation

◆ drawDefinitePositiveMatrix()

template<class MATRIX , class AUXVECTOR_T = MATRIX>

MATRIX mrpt::random::CRandomGenerator::drawDefinitePositiveMatrix	(	const size_t	dim,
		const double	std_scale = `1.0`,
		const double	diagonal_epsilon = `1e-8`
	)

inline

Generates a random definite-positive matrix of the given size, using the formula C = v*v^t + epsilon*I, with "v" being a vector of gaussian random samples.

Definition at line 188 of file RandomGenerators.h.

References mrpt::math::cov(), and drawGaussian1DMatrix().

Referenced by TEST().

◆ drawGaussian1D()

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 162 of file RandomGenerators.h.

References drawGaussian1D_normalized(), and mean().

Referenced by drawGaussian1DMatrix(), drawGaussian1DVector(), mrpt::maps::CBeaconMap::internal_insertObservation(), ransac_data_assoc_run(), mrpt::slam::CRejectionSamplingRangeOnlyLocalization::RS_drawFromProposal(), mrpt::maps::CBeaconMap::simulateBeaconReadings(), and mrpt::maps::CLandmarksMap::simulateRangeBearingReadings().

◆ drawGaussian1D_normalized()

double CRandomGenerator::drawGaussian1D_normalized ( )

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.

Definition at line 42 of file RandomGenerator.cpp.

References m_MT19937, and m_normdistribution.

Referenced by mrpt::obs::CActionRobotMovement3D::computeFromOdometry_model6DOF(), mrpt::obs::CActionRobotMovement2D::computeFromOdometry_modelThrun(), mrpt::poses::CPoseRandomSampler::do_sample_2D(), mrpt::poses::CPoseRandomSampler::do_sample_3D(), drawGaussian1D(), drawGaussianMultivariate(), drawGaussianMultivariateMany(), mrpt::obs::CActionRobotMovement2D::drawSingleSample_modelThrun(), mrpt::obs::CActionRobotMovement2D::fastDrawSingleSample_modelGaussian(), mrpt::random::matrixRandomNormal(), mrpt::maps::CLandmarksMap::simulateBeaconReadings(), mrpt::kinematics::CVehicleSimulVirtualBase::simulateOneTimeStep(), mrpt::maps::CLandmarksMap::simulateRangeBearingReadings(), mrpt::maps::COccupancyGridMap2D::simulateScanRay(), and mrpt::random::vectorRandomNormal().

◆ drawGaussian1DMatrix()

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 mrpt::math::CMatrixTemplateNumeric or mrpt::math::CMatrixFixedNumeric

See also: drawGaussian1D

Definition at line 174 of file RandomGenerators.h.

References drawGaussian1D(), and mean().

Referenced by do_test_init_random(), drawDefinitePositiveMatrix(), PosePDFGaussTests::generateRandomPose2DPDF(), Pose3DPDFGaussTests::generateRandomPose3DPDF(), and Pose3DQuatPDFGaussTests::generateRandomPose3DPDF().

◆ drawGaussian1DVector()

template<class VEC >

void mrpt::random::CRandomGenerator::drawGaussian1DVector	(	VEC &	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 207 of file RandomGenerators.h.

References drawGaussian1D(), and mean().

Referenced by TEST_F().

◆ drawGaussianMultivariate() [1/2]

template<typename T , typename MATRIX >

void mrpt::random::CRandomGenerator::drawGaussianMultivariate	(	std::vector< T > &	out_result,
		const MATRIX &	cov,
		const std::vector< T > *	mean = `nullptr`
	)

inline

Generate multidimensional random samples according to a given covariance matrix.

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

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 223 of file RandomGenerators.h.

References mrpt::math::cov(), drawGaussian1D_normalized(), and mean().

◆ drawGaussianMultivariate() [2/2]

template<class VECTORLIKE , class COVMATRIX >

void mrpt::random::CRandomGenerator::drawGaussianMultivariate	(	VECTORLIKE &	out_result,
		const COVMATRIX &	cov,
		const VECTORLIKE *	mean = `nullptr`
	)

inline

Generate multidimensional random samples according to a given covariance matrix.

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

Exceptions

std::exception On invalid covariance matrix

See also: drawGaussianMultivariateMany

Definition at line 264 of file RandomGenerators.h.

References mrpt::math::cov(), drawGaussian1D_normalized(), and mean().

◆ drawGaussianMultivariateMany()

template<typename VECTOR_OF_VECTORS , typename COVMATRIX >

void mrpt::random::CRandomGenerator::drawGaussianMultivariateMany	(	VECTOR_OF_VECTORS &	ret,
		size_t	desiredSamples,
		const COVMATRIX &	cov,
		const typename VECTOR_OF_VECTORS::value_type *	mean = `nullptr`
	)

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==nullptr.

Definition at line 310 of file RandomGenerators.h.

References mrpt::math::cov(), drawGaussian1D_normalized(), and mean().

Referenced by mrpt::poses::CPosePDFParticles::copyFrom(), mrpt::poses::CPose3DQuatPDFGaussianInf::drawManySamples(), mrpt::poses::CPose3DQuatPDFGaussian::drawManySamples(), mrpt::poses::CPose3DPDFGaussianInf::drawManySamples(), mrpt::poses::CPosePDFGaussian::drawManySamples(), mrpt::poses::CPose3DPDFGaussian::drawManySamples(), mrpt::poses::CPosePDFGaussianInf::drawManySamples(), mrpt::random::randomNormalMultiDimensionalMany(), and mrpt::math::transform_gaussian_montecarlo().