#include "gstlearn_export.hpp"
#include "Matrix/MatrixRectangular.hpp"

Functions
GSTLEARN_EXPORT int	mvndst_infin (double low, double sup)

GSTLEARN_EXPORT void	mvndst (int n, double lower, double upper, int infin, double correl, int maxpts, double abseps, double releps, double error, double value, int *inform)

GSTLEARN_EXPORT void	mvndst2n (double lower, double upper, double means, double correl, int maxpts, double abseps, double releps, double error, double value, int *inform)

GSTLEARN_EXPORT void	mvndst4 (double lower, double upper, double correl, int maxpts, double abseps, double releps, double error, double value, int inform)

GSTLEARN_EXPORT int	bessel_j (double x, double alpha, int nb, double *b)

GSTLEARN_EXPORT int	bessel_k (double x, double alpha, int nb, double *bk)

GSTLEARN_EXPORT double	loggamma (double parameter)

GSTLEARN_EXPORT double	ut_legendre (int flag_norm, int n, double v)

GSTLEARN_EXPORT double	ut_flegendre (int flag_norm, int n, int k0, double theta)

GSTLEARN_EXPORT double	golden_search (double(func_evaluate)(double test, void user_data), void user_data, double tolstop, double a0, double c0, double test_loc, double *niter)

GSTLEARN_EXPORT int	ut_chebychev_count (double(func)(double, double, int, double ), Cheb_Elem cheb_elem, double x, int nblin, double blin)

GSTLEARN_EXPORT int	ut_chebychev_coeffs (double(func)(double, double, int, double ), Cheb_Elem cheb_elem, int nblin, double blin)

GSTLEARN_EXPORT void	ut_vandercorput (int n, int flag_sym, int flag_rot, int ntri_arg, double *coor_arg)

GSTLEARN_EXPORT int	ut_icosphere (int n, int flag_rot, int ntri_arg, double *coor_arg)

GSTLEARN_EXPORT double	ut_factorial (int k)

GSTLEARN_EXPORT void	ut_log_factorial (int nbpoly, double *factor)

GSTLEARN_EXPORT MatrixRectangular *	vanDerCorput (int n, int nd)

GSTLEARN_EXPORT MatrixRectangular	fillLegendreMatrix (const VectorDouble &r, int legendreOrder)

Function Documentation

◆ bessel_j()

GSTLEARN_EXPORT int bessel_j	(	double	x,
		double	alpha,
		int	nb,
		double *	b
	)

This routine calculates Bessel functions J SUB(NB+ALPHA) (X) for non-negative argument X, and non-negative order NB+ALPHA.

Returns: Error return code : NCALC; NCALC < -1: An argument is out of range.; 1 < NCALC < NB: Not all requested function values could be; calculated accurately. BY(I) contains correct function values

Parameters

[in]	x	Working precision non-negative real argument for which J's are to be calculated.
[in]	alpha	Working precision fractional part of order for which J's are to be calculated. 0 <= ALPHA < 1.0.
[in]	nb	Integer number of functions to be calculated, NB > 0 The first function calculated is of order ALPHA, and the last is of order (NB - 1 + ALPHA).
[out]	b	Working precision output vector of length NB. If the routine terminates normally (NCALC=NB), the vector by[] contains the functions Y(ALPHA,X), ... ,Y(NB-1+ALPHA,X),

Remarks: This program is based on a program written by David J. Sookne (2); that computes values of the Bessel functions J or I of real; argument and integer order. Modifications include the; restriction of the computation to the J Bessel function of; non-negative real argument, the extension of the computation to; arbitrary positive order, and the elimination of most underflow.; References: "A Note on Backward Recurrence Algorithms," Olver, F.; W. J., and Sookne, D. J., Math. Comp. 26, 1972, pp 941-947.; "Bessel Functions of Real Argument and Integer Order," Sookne, D.; J., NBS Jour. of Res. B. 77B, 1973, pp 125-132.

◆ bessel_k()

GSTLEARN_EXPORT int bessel_k	(	double	x,
		double	alpha,
		int	nb,
		double *	bk
	)

This routine calculates modified Bessel functions of the second kind, K SUB(N+ALPHA) (X), for non-negative argument X and non-negative order N+ALPHA

Returns: Error return code : NCALC NCALC < -1: An argument is out of; range. 0 < NCALC < NB: Not all requested function values could be; calculated accurately. BK(I) contains correct function values; for I <= NCALC, and contains the ratios; K(ALPHA+I-1,X)/K(ALPHA+I-2,X) for the rest of the array.

Parameters

[in]	x	Working precision non-negative real argument for which K's are to calculated. If K's are to be calculated, X must not be greater than XMAX.
[in]	alpha	Working precision fractional part of order for which K's are to be calculated. 0 <= ALPHA <1.0.
[in]	nb	Integer number of functions to be calculated, NB > 0. The first function calculated is of order ALPHA, and the last is of order (NB - 1 + ALPHA).
[out]	bk	Working precision output vector of length NB. If the routine terminates normally (NCALC=NB), the vector BK contains the functions : K(ALPHA,X), ... , K(NB-1+ALPHA,X),

Remarks: This program is based on a program written by J. B. Campbell (2); that computes values of the Bessel functions K of real argument; and real order. References: "On Temme's Algorithm for the \remark Modified Bessel Functions of the Third Kind," Campbell, J. B.,; TOMS 6(4), Dec. 1980, pp. 581-586. "A FORTRAN IV Subroutine for; the Modified Bessel Functions of the Third Kind of Real Order and; Real Argument," Campbell, J. B., Report NRC/ERB-925, National; Research Council, Canada.

◆ fillLegendreMatrix()

GSTLEARN_EXPORT MatrixRectangular fillLegendreMatrix	(	const VectorDouble &	r,
		int	legendreOrder
	)

◆ golden_search()

GSTLEARN_EXPORT double golden_search	(	double()(double test, void user_data)	func_evaluate,
		void *	user_data,
		double	tolstop,
		double	a0,
		double	c0,
		double *	test_loc,
		double *	niter
	)

Golden Search algorithm

Returns: The estimated value

Parameters

[in]	func_evaluate	Evaluating function
[in]	user_data	User Data
[in]	tolstop	Tolerance parameter
[in]	a0	Initial value for lower bound of interval
[in]	c0	Initial value for upper bound of interval
[out]	test_loc	Final value of the evaluating function
[out]	niter	Number of iterations

◆ loggamma()

GSTLEARN_EXPORT double loggamma ( double parameter )

Calculation of the logarithm of the gamma function

Returns: Logarithm of the gamma function

Parameters

[in] parameter raw value

◆ mvndst()

GSTLEARN_EXPORT void mvndst	(	int	n,
		double *	lower,
		double *	upper,
		int *	infin,
		double *	correl,
		int	maxpts,
		double	abseps,
		double	releps,
		double *	error,
		double *	value,
		int *	inform
	)

Multivariate Normal Probability

Parameters

[in]	n	Number of variables
[in]	lower	Array of lower integration limits
[in]	upper	Array of upper integration limits
[in]	infin	Array of integration limit flags <0 for ]-Infinity; +Infinity[ =0 for ]-Infinity; upper] =1 for [lower; +Infinity[ =2 for [lower; upper]
[in]	correl	Array of correlation coefficients
[in]	maxpts	Maximum number of function values allowed
[in]	abseps	Absolute error tolerance
[in]	releps	Relative error tolerance
[out]	error	Estimated absolute error with 90% confidence level
[out]	value	Estimated value for the integral
[out]	inform	Returned code

Remarks: The array correl must be entered as the non-diagonal upper part; of the correlation matrix, entered by line; This subroutine uses an algorithm given in the paper:; "Numerical Computation of Multivariate Normal Probabilities", in; J. of Computational and Graphical Stat., 1(1992), pp. 141-149, by; Alan Genz; Department of Mathematics; Washington State University; Pullman, WA 99164-3113; Email : AlanG.nosp@m.enz@.nosp@m.wsu.e.nosp@m.du

◆ mvndst2n()

GSTLEARN_EXPORT void mvndst2n	(	double *	lower,
		double *	upper,
		double *	means,
		double *	correl,
		int	maxpts,
		double	abseps,
		double	releps,
		double *	error,
		double *	value,
		int *	inform
	)

Calculate the multigaussian integral (non-normalized)

Parameters

[in]	lower	Array of lower bounds (Dimension: nvar)
[in]	upper	Array of upper bounds (Dimension: nvar)
[in]	means	Array of means (Dimension: 2)
[in]	correl	Correlation matrix (Dimension: 2*2)
[in]	maxpts	Maximum number of function values allowed
[in]	abseps	Absolute error tolerance
[in]	releps	Relative error tolerance
[out]	error	Estimated absolute error with 90% confidence level
[out]	value	Estimated value for the integral
[out]	inform	Returned code

◆ mvndst4()

GSTLEARN_EXPORT void mvndst4	(	double *	lower,
		double *	upper,
		double *	correl,
		int	maxpts,
		double	abseps,
		double	releps,
		double *	error,
		double *	value,
		int *	inform
	)

Calculate the quadri-variable gaussian integral

Parameters

[in]	lower	Array of lower bounds
[in]	upper	Array of upper bounds
[in]	correl	Correlation matrix (Dimension: 4*4)
[in]	maxpts	Maximum number of function values allowed
[in]	abseps	Absolute error tolerance
[in]	releps	Relative error tolerance
[out]	error	Estimated absolute error with 90% confidence level
[out]	value	Estimated value for the integral
[out]	inform	Returned code

◆ mvndst_infin()

GSTLEARN_EXPORT int mvndst_infin	(	double	low,
		double	sup
	)

Set the flags for the bound of numerical integration

Returns: Flag for the bound

Parameters

[in]	low	Lower integration bound
[in]	sup	Upper integration bound

◆ ut_chebychev_coeffs()

GSTLEARN_EXPORT int ut_chebychev_coeffs	(	double()(double, double, int, double )	func,
		Cheb_Elem *	cheb_elem,
		int	nblin,
		double *	blin
	)

Calculates the coefficients of the Chebychev polynomial which is an approximation of a given function

Returns: Error return code

Parameters

[in]	func	Function to be approximated
[in]	cheb_elem	Cheb_Elem structure
[in]	nblin	Number of terms in the polynomial expansion
[in]	blin	Array of coefficients for polynomial expansion

◆ ut_chebychev_count()

GSTLEARN_EXPORT int ut_chebychev_count	(	double()(double, double, int, double )	func,
		Cheb_Elem *	cheb_elem,
		double	x,
		int	nblin,
		double *	blin
	)

Evaluate the number of coefficients necessary to evaluate a function (at a sample location) at a given approximation

Returns: Minimum number of necessary coefficients

Parameters

[in]	func	Function to be approximated
[in]	cheb_elem	Cheb_Elem structure
[in]	x	Sampling value
[in]	nblin	Number of terms in the polynomial expansion
[in]	blin	Array of coefficients for polynomial expansion

◆ ut_factorial()

GSTLEARN_EXPORT double ut_factorial ( int k )

Calculates the factorial coefficient

Returns: Returned value

Parameters

[in] k Value

◆ ut_flegendre()

GSTLEARN_EXPORT double ut_flegendre	(	int	flag_norm,
		int	n,
		int	k0,
		double	theta
	)

Returns the Spherical Legendre normalized function

Parameters

[in]	flag_norm	1 for normalized and 0 otherwise
[in]	n	Degree
[in]	k0	Order (ABS(k0) <= n)
[in]	theta	Theta angle in radian

◆ ut_icosphere()

GSTLEARN_EXPORT int ut_icosphere	(	int	n,
		int	flag_rot,
		int *	ntri_arg,
		double **	coor_arg
	)

Generate regular Icosahedron discretization

Returns: Error return code

Parameters

[in]	n	Number of discretization steps
[in]	flag_rot	Perform a random rotation
[out]	ntri_arg	Number of points
[out]	coor_arg	Array of point coordinates (Dimension: 3*ntri)

Remarks: As random number are used in this function, a specific seed; is fixed here

◆ ut_legendre()

GSTLEARN_EXPORT double ut_legendre	(	int	flag_norm,
		int	n,
		double	v
	)

Returns the Associated Legendre Function

Parameters

[in]	flag_norm	1 for normalized and 0 otherwise
[in]	n	Degree
[in]	v	Value

◆ ut_log_factorial()

GSTLEARN_EXPORT void ut_log_factorial	(	int	nbpoly,
		double *	factor
	)

Calculates the nbpoly log-factorial coefficients

Parameters

[in]	nbpoly	Number of terms
[out]	factor	logarithm of factorials

◆ ut_vandercorput()

GSTLEARN_EXPORT void ut_vandercorput	(	int	n,
		int	flag_sym,
		int	flag_rot,
		int *	ntri_arg,
		double **	coor_arg
	)

Generate a Van Der Corput list of points in R^3

Parameters

[in]	n	Number of points
[in]	flag_sym	Duplicate the samples by symmetry
[in]	flag_rot	Perform a random rotation
[out]	ntri_arg	Number of points
[out]	coor_arg	Array of point coordinates (Dimension: 3*ntri)

◆ vanDerCorput()

GSTLEARN_EXPORT MatrixRectangular* vanDerCorput	(	int	n,
		int	nd
	)

Function to compute the vector Van der Corput sequence or the Halton sequence

Parameters

n	The number of values to be computed
nd	The dimension of output sequence

Returns: A matrix of dimensions [n,nd] with the sequence values (between 0 and 1)

Note: The dimension nd should be lower or equal to 50.

Functions

Function Documentation

◆ bessel_j()

◆ bessel_k()

◆ fillLegendreMatrix()

◆ golden_search()

◆ loggamma()

◆ mvndst()

◆ mvndst2n()

◆ mvndst4()

◆ mvndst_infin()

◆ ut_chebychev_coeffs()

◆ ut_chebychev_count()

◆ ut_factorial()

◆ ut_flegendre()

◆ ut_icosphere()

◆ ut_legendre()

◆ ut_log_factorial()

◆ ut_vandercorput()

◆ vanDerCorput()