randomnumbers.cpp File Reference

#include <cstdlib>
#include "randomnumbers.hpp"
#include "linearalgebra.hpp"
#include <cmath>
#include <utility>
#include <vector>

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Functions
double	randf (double min, double max)
	Draw uniformly distributed samples between two numbers. More...
double	randn (double mean, double stdv)
	Draws from Gaussian \( \mathcal{N} (\mu,\sigma) \) (mean, standard deviation) using Box-Müller transform. More...
std::vector< double >	randn (std::vector< double > means, std::vector< double > stdv)
	Draws from uncorrelated Gaussians \( \mathcal{N} (\boldsymbol \mu,\boldsymbol{\sigma}) \) (vectors of mean, standard deviation) using Box-Müller transform. Loops over both vectors and calls randn(double mean, double stdv) every iteration. More...
std::vector< double >	randn (std::vector< double > stdv)
	Draws zero-mean samples from uncorrelated Gaussians \( \mathcal{N} (\boldsymbol 0,\boldsymbol{\sigma}) \) (standard deviation) using Box-Müller transform. Loops over both vectors and calls randn(double mean, double stdv) every iteration. More...
std::vector< double >	randn_Cholesky (std::vector< double > mean, std::vector< std::vector< double >> CholeskyLower_CovarianceMatrix)
	Drawing non-zero mean samples from an \( n \) dimensional correlated Gaussian. Invokes randn_Cholesky(std::vector<std::vector<double>> CholeskyLower_CovarianceMatrix) and adds mean. More...
std::vector< double >	randn_Cholesky (std::vector< std::vector< double >> CholeskyLower_CovarianceMatrix)
	Drawing non-zero mean samples from an \( n \) dimensional correlated Gaussian. This algorithm uses the lower Cholesky matrix of the (positive definite Hermitian) covariance matrix to transform (affine transform) n uncorrelated samples with standard deviation 1 to the right covariances. The mean is assumed zero. More...
std::vector< double >	randn (std::vector< std::vector< double >> DiagonalCovarianceMatrix)
	Drawing n zero mean samples from \( \mathcal{N} (\boldsymbol 0,\boldsymbol{\sigma}) \). No correlation is present between the parameters. More...

Function Documentation

randf()

double randf	(	double	min,
		double	max
	)

Draw uniformly distributed samples between two numbers.

Parameters

min	Minimum of the distribution.
max	Maximum of the distribution.

Returns

Sample.

Definition at line 13 of file randomnumbers.cpp.

randn() [1/4]

double randn	(	double	mean,
		double	stdv
	)

Draws from Gaussian \( \mathcal{N} (\mu,\sigma) \) (mean, standard deviation) using Box-Müller transform.

Parameters

mean	double containing \( \mu \)
stdv	double containing \( \sigma \)

Returns

double, sample from the distribution

Definition at line 17 of file randomnumbers.cpp.

randn() [2/4]

std::vector<double> randn	(	std::vector< double >	means,
		std::vector< double >	stdv
	)

Draws from uncorrelated Gaussians \( \mathcal{N} (\boldsymbol \mu,\boldsymbol{\sigma}) \) (vectors of mean, standard deviation) using Box-Müller transform. Loops over both vectors and calls randn(double mean, double stdv) every iteration.

Parameters

mean	vector containing \( \mu_i \)
stdv	vector containing \( \sigma_i \)

Returns

Vector of samples from the distributions.

Definition at line 30 of file randomnumbers.cpp.

randn() [3/4]

std::vector<double> randn

(

std::vector< double >

stdv

)

Draws zero-mean samples from uncorrelated Gaussians \( \mathcal{N} (\boldsymbol 0,\boldsymbol{\sigma}) \) (standard deviation) using Box-Müller transform. Loops over both vectors and calls randn(double mean, double stdv) every iteration.

Parameters

stdv	vector containing \( \sigma_i \)

Returns

Vector of samples from the distributions.

Definition at line 36 of file randomnumbers.cpp.

randn() [4/4]

std::vector<double> randn

(

std::vector< std::vector< double >>

DiagonalCovarianceMatrix

)

Drawing n zero mean samples from \( \mathcal{N} (\boldsymbol 0,\boldsymbol{\sigma}) \). No correlation is present between the parameters.

Parameters

DiagonalCovarianceMatrix

Matrix containing on the diagonal the variance, or standard deviation squared.

Returns

Vector containing n samples.

Definition at line 62 of file randomnumbers.cpp.

randn_Cholesky() [1/2]

std::vector<double> randn_Cholesky	(	std::vector< double >	mean,
		std::vector< std::vector< double >>	CholeskyLower_CovarianceMatrix
	)

Drawing non-zero mean samples from an \( n \) dimensional correlated Gaussian. Invokes randn_Cholesky(std::vector<std::vector<double>> CholeskyLower_CovarianceMatrix) and adds mean.

Parameters

mean	vector containing \( n \) means.
CholeskyLower_CovarianceMatrix	Matrix containing the n x n Lower Cholesky matrix of the n x n covariance matrix \( \boldsymbol \Sigma \), must be square and lower triangular.

Returns

Vector containing the non-zero mean correlated samples.

Definition at line 47 of file randomnumbers.cpp.

randn_Cholesky() [2/2]

std::vector<double> randn_Cholesky

(

std::vector< std::vector< double >>

CholeskyLower_CovarianceMatrix

)

Drawing non-zero mean samples from an \( n \) dimensional correlated Gaussian. This algorithm uses the lower Cholesky matrix of the (positive definite Hermitian) covariance matrix to transform (affine transform) n uncorrelated samples with standard deviation 1 to the right covariances. The mean is assumed zero.

Parameters

CholeskyLower_CovarianceMatrix

Matrix containing the n x n Lower Cholesky matrix of the n x n covariance matrix \( \boldsymbol \Sigma \), must be square and lower triangular.

Returns

Vector containing the zero mean correlated samples.

Definition at line 51 of file randomnumbers.cpp.