#include "Basic/VectorNumT.hpp"
#include "geoslib_define.h"
#include "Enum/ESPDECalcMode.hpp"
#include "Basic/NamingConvention.hpp"
#include "API/SPDEParam.hpp"
#include "LinearOp/PrecisionOpCs.hpp"
#include "LinearOp/PrecisionOpMultiConditional.hpp"
#include <vector>

Classes
class	SPDE

Functions
GSTLEARN_EXPORT int	krigingSPDE (Db dbin, Db dbout, Model model, bool flag_est=true, bool flag_std=false, const AMesh mesh=nullptr, int useCholesky=-1, const SPDEParam &params=SPDEParam(), int nbMC=10, bool verbose=false, bool showStats=false, const NamingConvention &namconv=NamingConvention("KrigingSPDE"))

GSTLEARN_EXPORT int	simulateSPDE (Db dbin, Db dbout, Model model, int nbsimu=1, const AMesh mesh=nullptr, int useCholesky=-1, const SPDEParam &params=SPDEParam(), bool verbose=false, bool showStats=false, const NamingConvention &namconv=NamingConvention("SimuSPDE"))

GSTLEARN_EXPORT double	logLikelihoodSPDE (Db dbin, Db dbout, Model model, const AMesh mesh=nullptr, int useCholesky=-1, int nbsimu=1, const SPDEParam &params=SPDEParam(), bool verbose=false)

GSTLEARN_EXPORT MatrixSparse *	buildInvNugget (Db dbin, Model model, const SPDEParam &params=SPDEParam())

GSTLEARN_EXPORT VectorDouble	krigingSPDENew (Db dbin, Db dbout, Model *model, const VectorMeshes &meshes=VectorMeshes(), int useCholesky=-1, bool verbose=false, const NamingConvention &namconv=NamingConvention("KrigingSPDE"))

Function Documentation

◆ buildInvNugget()

GSTLEARN_EXPORT MatrixSparse* buildInvNugget	(	Db *	db,
		Model *	model,
		const SPDEParam &	params
	)

Build the inverse of the Nugget Effect matrix It is established for:

the number of variables defined in 'dbin' (and in 'Model')
the active samples of 'dbin'
the samples where Z-variable (and possibly V-variable) is defined

Parameters

db	Input Db structure
model	Input Model structure
params	A structure for ruling the parameters of SPDE

◆ krigingSPDE()

GSTLEARN_EXPORT int krigingSPDE	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		bool	flag_est,
		bool	flag_std,
		const AMesh *	mesh,
		int	useCholesky,
		const SPDEParam &	params,
		int	nbMC,
		bool	verbose,
		bool	showStats,
		const NamingConvention &	namconv
	)

Perform the estimation by KRIGING under the SPDE framework

Parameters

dbin	Input Db (must contain the variable to be estimated)
dbout	Output Db where the estimation must be performed
model	Model definition
flag_est	True for the estimation
flag_std	True for the standard deviation of estimation error
mesh	Mesh description (optional)
useCholesky	Define the choice regarding Cholesky
params	Set of parameters
nbMC	Number of Monte-Carlo simulations used for variance calculation
verbose	Verbose flag
showStats	Show statistics for Linear Operations
namconv	Naming convention

Returns: Error return code

Remarks: You can provide an already existing mesh. Otherwise an optimal mesh will be created; internally: one per structure constituting the Model for Kriging.; Each mesh is created using the Turbo Meshing facility... based on an internal grid.; This internal grid is rotated according to the rotation of the structure. Its mesh size; is derived from the range (per direction) by dividing it by the refinement factor.; Note that switching 'flag_std' to ON implies that 'flag_est' is ON.

◆ krigingSPDENew()

GSTLEARN_EXPORT VectorDouble krigingSPDENew	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		const VectorMeshes &	meshes,
		int	useCholesky,
		bool	verbose,
		const NamingConvention &	namconv
	)

Perform the estimation by KRIGING under the SPDE framework

Parameters

dbin	Input Db (must contain the variable to be estimated)
dbout	Output Db where the estimation must be performed
model	Model definition
meshes	Meshes description (optional)
useCholesky	Define the choice regarding Cholesky
verbose	Verbose flag
namconv	Naming convention

Returns: Error return code

Remarks: You can provide an already existing mesh. Otherwise an optimal mesh will be created; internally: one per structure constituting the Model for Kriging.; Each mesh is created using the Turbo Meshing facility... based on an internal grid.; This internal grid is rotated according to the rotation of the structure. Its mesh size; is derived from the range (per direction) by dividing it by the refinement factor.

◆ logLikelihoodSPDE()

GSTLEARN_EXPORT double logLikelihoodSPDE	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		const AMesh *	mesh = `nullptr`,
		int	useCholesky = `-1`,
		int	nbsimu = `1`,
		const SPDEParam &	params = `SPDEParam()`,
		bool	verbose = `false`
	)

◆ simulateSPDE()

GSTLEARN_EXPORT int simulateSPDE	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		int	nbsimu,
		const AMesh *	mesh,
		int	useCholesky,
		const SPDEParam &	params,
		bool	verbose,
		bool	showStats,
		const NamingConvention &	namconv
	)

Perform simulations under the SPDE framework

Parameters

dbin	Input Db. If defined, the simulations are conditional; non conditional otherwise
dbout	Output Db where the simulations must be performed
model	Model definition
nbsimu	Number of simulations
mesh	Mesh description (optional)
useCholesky	Define the choice regarding Cholesky
params	Set of parametes
verbose	Verbose flag
showStats	Show statistics for Linear Operations
namconv	Naming convention

Returns: Error return code

Remarks: You can provide an already existing mesh. Otherwise an optimal mesh will be created; internally: one per structure constituting the Model for Kriging and one for Simulating; Each mesh is created using the Turbo Meshing facility... based on an internal grid.; This internal grid is rotated according to the rotation of the structure. Its mesh size; is derived from the range (per direction) by dividing it by the refinement factor.

Classes

Functions

Function Documentation

◆ buildInvNugget()

◆ krigingSPDE()

◆ krigingSPDENew()

◆ logLikelihoodSPDE()

◆ simulateSPDE()