namespace mrpt::random

Overview

A namespace of pseudo-random numbers generators of diferent distributions.

The central class in this namespace is mrpt::random::CRandomGenerator

namespace random {

// classes

class CRandomGenerator;
class Generator_MT19937;

// global functions

template <class URBG>
uint64_t portable_uniform_distribution(URBG&& g, uint64_t min, uint64_t max);

template <class RandomIt, class URBG>
void shuffle(
    RandomIt first,
    RandomIt last,
    URBG&& g
    );

template <class RandomIt>
void shuffle(RandomIt first, RandomIt last);

template <class RandomIt, class URBG>
void partial_shuffle(
    RandomIt first,
    RandomIt last,
    URBG&& g,
    size_t N
    );

CRandomGenerator& getRandomGenerator();
ptrdiff_t random_generator_for_STL(ptrdiff_t i);

template <class MAT>
void matrixRandomUni(
    MAT& matrix,
    const double unif_min = 0,
    const double unif_max = 1
    );

template <class T>
void vectorRandomUni(
    std::vector<T>& v_out,
    const T& unif_min = 0,
    const T& unif_max = 1
    );

template <class MAT>
void matrixRandomNormal(
    MAT& matrix,
    const double mean = 0,
    const double std = 1
    );

template <class T>
void vectorRandomNormal(
    std::vector<T>& v_out,
    const T& mean = 0,
    const T& std = 1
    );

void Randomize(const uint32_t seed);
void Randomize();

template <class T>
void randomPermutation(
    const std::vector<T>& in_vector,
    std::vector<T>& out_result
    );

template <typename T, typename MATRIX>
void randomNormalMultiDimensionalMany(
    const MATRIX& cov,
    size_t desiredSamples,
    std::vector<std::vector<T>>& ret,
    std::vector<T>* samplesLikelihoods = nullptr
    );

template <typename T, typename MATRIXLIKE>
void randomNormalMultiDimensionalMany(
    const MATRIXLIKE& cov,
    size_t desiredSamples,
    std::vector<std::vector<T>>& ret
    );

template <typename T, typename MATRIX>
void randomNormalMultiDimensional(
    const MATRIX& cov,
    std::vector<T>& out_result
    );

} // namespace random

Global Functions

CRandomGenerator& getRandomGenerator()

A static instance of a CRandomGenerator class, for use in single-thread applications.

ptrdiff_t random_generator_for_STL(ptrdiff_t i)

A random number generator for usage in STL algorithms expecting a function like this (eg, random_shuffle):

template <class MAT>
void matrixRandomUni(
    MAT& matrix,
    const double unif_min = 0,
    const double unif_max = 1
    )

Fills the given matrix with independent, uniformly distributed samples.

Matrix classes can be mrpt::math::CMatrixDynamic or mrpt::math::CMatrixFixed

See also:

matrixRandomNormal

template <class T>
void vectorRandomUni(
    std::vector<T>& v_out,
    const T& unif_min = 0,
    const T& unif_max = 1
    )

Fills the given matrix with independent, uniformly distributed samples.

See also:

vectorRandomNormal

template <class MAT>
void matrixRandomNormal(
    MAT& matrix,
    const double mean = 0,
    const double std = 1
    )

Fills the given matrix with independent, normally distributed samples.

Matrix classes can be mrpt::math::CMatrixDynamic or mrpt::math::CMatrixFixed

See also:

matrixRandomUni

template <class T>
void vectorRandomNormal(
    std::vector<T>& v_out,
    const T& mean = 0,
    const T& std = 1
    )

Generates a random vector with independent, normally distributed samples.

See also:

matrixRandomUni

void Randomize(const uint32_t seed)

Randomize the generators.

A seed can be providen, or a current-time based seed can be used (default)

template <class T>
void randomPermutation(
    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.

template <typename T, typename MATRIX>
void randomNormalMultiDimensionalMany(
    const MATRIX& cov,
    size_t desiredSamples,
    std::vector<std::vector<T>>& ret,
    std::vector<T>* samplesLikelihoods = nullptr
    )

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
std::exception	On invalid covariance matrix

See also:

randomNormalMultiDimensional

template <typename T, typename MATRIXLIKE>
void randomNormalMultiDimensionalMany(
    const MATRIXLIKE& cov,
    size_t desiredSamples,
    std::vector<std::vector<T>>& ret
    )

Generate multidimensional random samples according to a given covariance matrix.

Parameters:

std::exception

On invalid covariance matrix

See also:

randomNormalMultiDimensional

template <typename T, typename MATRIX>
void randomNormalMultiDimensional(
    const MATRIX& cov,
    std::vector<T>& out_result
    )

Generate multidimensional random samples according to a given covariance matrix.

Parameters:

std::exception

On invalid covariance matrix

See also:

randomNormalMultiDimensionalMany