#include "gstlearn_export.hpp"
#include "geoslib_define.h"
#include "Enum/EKrigOpt.hpp"
#include "Calculators/ACalcInterpolator.hpp"
#include "Matrix/MatrixRectangular.hpp"
#include "Matrix/MatrixSquareSymmetric.hpp"
#include "Anamorphosis/AAnam.hpp"

Classes
class	Krigtest_Res

class	CalcKriging

Functions
GSTLEARN_EXPORT int	kriging (Db dbin, Db dbout, Model model, ANeigh neigh, const EKrigOpt &calcul=EKrigOpt::fromKey("POINT"), bool flag_est=true, bool flag_std=true, bool flag_varz=false, const VectorInt &ndiscs=VectorInt(), const VectorInt &rank_colcok=VectorInt(), const MatrixRectangular *matLC=nullptr, const NamingConvention &namconv=NamingConvention("Kriging"))

GSTLEARN_EXPORT int	krigcell (Db dbin, Db dbout, Model model, ANeigh neigh, bool flag_est=true, bool flag_std=true, const VectorInt &ndiscs=VectorInt(), const VectorInt &rank_colcok=VectorInt(), const NamingConvention &namconv=NamingConvention("KrigCell"))

GSTLEARN_EXPORT int	kribayes (Db dbin, Db dbout, Model model, ANeigh neigh, const VectorDouble &prior_mean=VectorDouble(), const MatrixSquareSymmetric &prior_cov=MatrixSquareSymmetric(), bool flag_est=true, bool flag_std=true, const NamingConvention &namconv=NamingConvention("Bayes"))

GSTLEARN_EXPORT int	krigprof (Db dbin, Db dbout, Model model, ANeigh neigh, bool flag_est=true, bool flag_std=true, const NamingConvention &namconv=NamingConvention("KrigProf"))

GSTLEARN_EXPORT int	kriggam (Db dbin, Db dbout, Model model, ANeigh neigh, AAnam *anam, const NamingConvention &namconv=NamingConvention("KrigGam"))

GSTLEARN_EXPORT Krigtest_Res	krigtest (Db dbin, Db dbout, Model model, ANeigh neigh, int iech0=0, const EKrigOpt &calcul=EKrigOpt::fromKey("POINT"), const VectorInt &ndiscs=VectorInt(), bool flagPerCell=false, bool verbose=true)

GSTLEARN_EXPORT int	xvalid (Db db, Model model, ANeigh *neigh, bool flag_kfold=false, int flag_xvalid_est=1, int flag_xvalid_std=1, int flag_xvalid_varz=0, const VectorInt &rank_colcok=VectorInt(), const NamingConvention &namconv=NamingConvention("Xvalid"))

GSTLEARN_EXPORT int	test_neigh (Db dbin, Db dbout, Model model, ANeigh neigh, const NamingConvention &namconv=NamingConvention("Neigh"))

Function Documentation

◆ kribayes()

GSTLEARN_EXPORT int kribayes	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		const VectorDouble &	prior_mean,
		const MatrixSquareSymmetric &	prior_cov,
		bool	flag_est,
		bool	flag_std,
		const NamingConvention &	namconv
	)

Estimation with Bayesian Drift

Returns: Error return code

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	prior_mean	Array giving the prior means for the drift terms
[in]	prior_cov	Array containing the prior covariance matrix for the drift terms
[in]	flag_est	Pointer for the storing the estimation
[in]	flag_std	Pointer for the storing the standard deviation
[in]	namconv	Naming convention

◆ krigcell()

GSTLEARN_EXPORT int krigcell	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		bool	flag_est,
		bool	flag_std,
		const VectorInt &	ndiscs,
		const VectorInt &	rank_colcok,
		const NamingConvention &	namconv
	)

Standard Block Kriging with variable cell dimension

Returns: Error return code

Parameters

[in]	dbin	Input Db structure
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	ndiscs	Array giving the discretization counts
[in]	flag_est	Option for the storing the estimation
[in]	flag_std	Option for the storing the standard deviation
[in]	rank_colcok	Option for running Collocated Cokriging
[in]	namconv	Naming convention

◆ kriggam()

GSTLEARN_EXPORT int kriggam	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		AAnam *	anam,
		const NamingConvention &	namconv
	)

Punctual Kriging in the Anamorphosed Gaussian Model

Returns: Error return code

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	anam	AAnam structure
[in]	namconv	Naming convention

◆ kriging()

GSTLEARN_EXPORT int kriging	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		const EKrigOpt &	calcul,
		bool	flag_est,
		bool	flag_std,
		bool	flag_varz,
		const VectorInt &	ndiscs,
		const VectorInt &	rank_colcok,
		const MatrixRectangular *	matLC,
		const NamingConvention &	namconv
	)

Standard Kriging

Returns: Error return code

Parameters

[in]	dbin	Input Db structure
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	calcul	Kriging calculation option (EKrigOpt)
[in]	ndiscs	Array giving the discretization counts
[in]	flag_est	Option for storing the estimation
[in]	flag_std	Option for storing the standard deviation
[in]	flag_varz	Option for storing the variance of the estimator (only available for stationary model)
[in]	rank_colcok	Option for running Collocated Cokriging
[in]	matLC	Matrix of linear combination (or NULL) (Dimension: nvarLC * model->getNVar())
[in]	namconv	Naming convention

◆ krigprof()

GSTLEARN_EXPORT int krigprof	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		bool	flag_est,
		bool	flag_std,
		const NamingConvention &	namconv
	)

Punctual Kriging based on profiles

Returns: Error return code

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	flag_est	Option for the storing the estimation
[in]	flag_std	Option for the storing the standard deviation
[in]	namconv	Naming convention

◆ krigtest()

GSTLEARN_EXPORT Krigtest_Res krigtest	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		int	iech0,
		const EKrigOpt &	calcul,
		const VectorInt &	ndiscs,
		bool	flagPerCell,
		bool	verbose
	)

Perform kriging and return the calculation elements

Returns: A Krigtest_Res structure

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure
[in]	neigh	ANeigh structure
[in]	iech0	Rank of the target sample
[in]	calcul	Kriging calculation option (EKrigOpt)
[in]	ndiscs	Array giving the discretization counts
[in]	flagPerCell	Use local block extensions (when defined)
[in]	verbose	When TRUE, the full debugging flag is switched ON (the current status is reset after the run)

◆ test_neigh()

GSTLEARN_EXPORT int test_neigh	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		const NamingConvention &	namconv
	)

Check the Neighborhood

Returns: Error return code (0: success, 1: error)

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure (optional)
[in]	neigh	ANeigh structure
[in]	namconv	Naming Convention

Remarks: This procedure creates the following arrays:; 1 - The number of selected samples; 2 - The maximum neighborhood distance; 3 - The minimum neighborhood distance; 4 - The number of non-empty sectors; 5 - The number of consecutive empty sectors

◆ xvalid()

GSTLEARN_EXPORT int xvalid	(	Db *	db,
		Model *	model,
		ANeigh *	neigh,
		bool	flag_kfold,
		int	flag_xvalid_est,
		int	flag_xvalid_std,
		int	flag_xvalid_varz,
		const VectorInt &	rank_colcok,
		const NamingConvention &	namconv
	)

Standard Cross-Validation

Parameters

db	Db structure
model	Model structure
neigh	ANeigh structure
flag_kfold	True if a code (K-FOLD) is used
flag_xvalid_est	Option for storing the estimation: 1 for Z-Z; -1 for Z; 0 not stored
flag_xvalid_std	Option for storing the standard deviation: 1:for (Z*-Z)/S; -1 for S; 0 not stored
flag_xvalid_varz	Option for storing the variance of the estimator: 1 to store and 0 not stored
rank_colcok	Option for running Collocated Cokriging
namconv	Naming Convention

Returns: Error return code

Classes

Functions

Function Documentation

◆ kribayes()

◆ krigcell()

◆ kriggam()

◆ kriging()

◆ krigprof()

◆ krigtest()

◆ test_neigh()

◆ xvalid()