krige.cpp File Reference

#include "geoslib_f.h"
#include "geoslib_old_f.h"
#include "geoslib_f_private.h"
#include "geoslib_define.h"
#include "Enum/EAnam.hpp"
#include "Enum/ECalcMember.hpp"
#include "Polynomials/Hermite.hpp"
#include "Db/Db.hpp"
#include "Db/DbGrid.hpp"
#include "Db/DbStringFormat.hpp"
#include "Model/Model.hpp"
#include "Model/CovInternal.hpp"
#include "Neigh/NeighMoving.hpp"
#include "Neigh/NeighImage.hpp"
#include "Neigh/NeighUnique.hpp"
#include "Neigh/ANeigh.hpp"
#include "Anamorphosis/AnamDiscreteDD.hpp"
#include "Anamorphosis/AnamDiscreteIR.hpp"
#include "Anamorphosis/AnamHermite.hpp"
#include "Basic/String.hpp"
#include "Basic/NamingConvention.hpp"
#include "Basic/Utilities.hpp"
#include "Basic/Law.hpp"
#include "Basic/File.hpp"
#include "Basic/OptDbg.hpp"
#include "Basic/OptCustom.hpp"
#include "Covariances/CovContext.hpp"
#include "Drifts/DriftList.hpp"
#include "Estimation/KrigingSystem.hpp"
#include "Space/SpaceRN.hpp"
#include <math.h>
#include <string.h>
#include <stdio.h>
#include <limits.h>

Classes
struct	Disc_Structure

Functions
int	is_flag_data_disc_defined (void)
void	set_DBIN (Db *dbin)
void	set_DBOUT (Db *dbout)
int	krige_koption_manage (int mode, int flag_check, const EKrigOpt &calcul, int flag_rand, const VectorInt &ndiscs)
void	krige_lhs_print (int nech, int neq, int nred, int *flagloc, double *lhs)
void	krige_rhs_print (int nvar, int nech, int neq, int nred, int *flag, double *rhs)
void	krige_dual_print (int nech, int neq, int nred, int *flag, double *dual)
int	_krigsim (Db *dbin, Db *dbout, const Model *model, ANeigh *neigh, bool flag_bayes, const VectorDouble &dmean, const MatrixSquareSymmetric &dcov, int icase, int nbsimu, bool flag_dgm)
int	global_transitive (DbGrid *dbgrid, Model *model, int flag_verbose, int flag_regular, int ndisc, double *abundance, double *sse, double *cvtrans)
int	anakexp_f (DbGrid *db, double *covdd, double *covd0, double top, double bot, int cov_radius, int neigh_radius, int flag_sym, int nfeq)
int	anakexp_3D (DbGrid *db, double *cov_ref, int cov_radius, int neigh_ver, int neigh_hor, int flag_sym, Model *model, double nugget, int nfeq, int dbg_ix, int dbg_iy)
int	bayes_simulate (Model *model, int nbsimu, const VectorDouble &rmean, const VectorDouble &rcov, VectorDouble &smean)
int	krigsum (Db *dbin, Db *dbout, Model *model, ANeigh *neigh, bool flag_positive, const NamingConvention &namconv)
int	st_krige_data (Db *db, Model *model, double beta, int nsize1, int *ranks1, int nsize2, int *ranks2, int *rother, int flag_abs, double *data_est, double *data_var)
int	st_crit_global (Db *db, Model *model, int nsize1, int *ranks1, int *rother, double *crit)
int	sampling_f (Db *db, Model *model, double beta, int method1, int nsize1_max, int nsize1, int *ranks1, int method2, int nsize2_max, int nsize2, int *ranks2, int verbose)
int	krigsampling_f (Db *dbin, Db *dbout, Model *model, double beta, int nsize1, int *ranks1, int nsize2, int *ranks2, bool flag_std, int verbose)
int	declustering (Db *dbin, Model *model, int method, ANeigh *neigh, DbGrid *dbgrid, const VectorDouble &radius, const VectorInt &ndiscs, int flag_sel, bool verbose)
int	inhomogeneous_kriging (Db *dbdat, Db *dbsrc, Db *dbout, double power, int flag_source, Model *model_dat, Model *model_src)
void	_image_smoother (DbGrid *dbgrid, const NeighImage *neigh, int type, double range, int iptr0)

Function Documentation

void _image_smoother	(	DbGrid *	dbgrid,
		const NeighImage *	neigh,
		int	type,
		double	range,
		int	iptr0
	)

Smooth a regular grid

Parameters

[in]	dbgrid	input and output Db grid structure
[in]	neigh	Neigh structure
[in]	type	1 for Uniform; 2 for Gaussian
[in]	range	Range (used for Gaussian only)
[in]	iptr0	Storage address

Remarks

Limited to the monovariate case

int _krigsim	(	Db *	dbin,
		Db *	dbout,
		const Model *	model,
		ANeigh *	neigh,
		bool	flag_bayes,
		const VectorDouble &	dmean,
		const MatrixSquareSymmetric &	dcov,
		int	icase,
		int	nbsimu,
		bool	flag_dgm
	)

Conditioning 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]	flag_bayes	1 if Bayes option is switched ON
[in]	dmean	Array giving the prior means for the drift terms
[in]	dcov	Array containing the prior covariance matrix for the drift terms
[in]	icase	Case for PGS and GRF (or -1)
[in]	nbsimu	Number of simulations
[in]	flag_dgm	1 if the DGM version of kriging should be used

Remarks

: The model contains an anamorphosis with a change of support

: coefficient as soon as flag_dgm is TRUE

int anakexp_3D	(	DbGrid *	db,
		double *	cov_ref,
		int	cov_radius,
		int	neigh_ver,
		int	neigh_hor,
		int	flag_sym,
		Model *	model,
		double	nugget,
		int	nfeq,
		int	dbg_ix,
		int	dbg_iy
	)

Factorial Kriging analysis on a grid file using discretized covariances for the target variable. The discretized covariance of the total variable is calculated on the fly

Returns

Error return code

Parameters

[in]	db	input Db structure
[in]	cov_ref	Array of discretized covariance for target variable
[in]	cov_radius	Radius of the covariance array
[in]	neigh_ver	Radius of the Neighborhood along Vertical
[in]	neigh_hor	Radius of the Neighborhood along Horizontal
[in]	flag_sym	1 for symmetrized covariance
[in]	model	Model structure (only used for horizontal)
[in]	nugget	Additional Nugget Effect component
[in]	nfeq	0 or 1 drift function(s)
[in]	dbg_ix	Rank of the trace along X for variogram debug
[in]	dbg_iy	Rank of the trace along Y for variogram debug

Remarks

The discretized covariance of the target variable is provided

in 1-D along the vertical. Its extension to the space dimension

is performed using the theoretical factorized model

If dbg_ix < -1 || dbg_iy < -1, no variogram debug file is created

int anakexp_f	(	DbGrid *	db,
		double *	covdd,
		double *	covd0,
		double	top,
		double	bot,
		int	cov_radius,
		int	neigh_radius,
		int	flag_sym,
		int	nfeq
	)

Factorial Kriging analysis on a 1-D grid file using discretized covariances for total and partial variables

Returns

Error return code

Parameters

[in]	db	input Db structure
[in]	covdd	Array of discretized cov. for total variable
[in]	covd0	Array of discretized cov. for partial variable
[in]	top	Elevation of the Top variable
[in]	bot	Elevation of the bottom variable
[in]	cov_radius	Radius of the Covariance arrays
[in]	neigh_radius	Radius of the Neighborhood
[in]	flag_sym	1 for symmetrized covariance
[in]	nfeq	0 or 1 drift function(s)

int bayes_simulate	(	Model *	model,
		int	nbsimu,
		const VectorDouble &	rmean,
		const VectorDouble &	rcov,
		VectorDouble &	smean
	)

Simulate the drift coefficients from the posterior distributions

Returns

Error returned code

Parameters

[in]	model	Model structure
[in]	nbsimu	Number of simulation (0 for kriging)
[in]	rmean	Array giving the posterior means for the drift terms
[in]	rcov	Array containing the posterior covariance matrix for the drift terms
[out]	smean	Array for simulated posterior mean for the drift means

Remarks

The input array rcov is modified by this routine, due to

the use of the routine matrix_cholesky_decompose

int declustering	(	Db *	dbin,
		Model *	model,
		int	method,
		ANeigh *	neigh,
		DbGrid *	dbgrid,
		const VectorDouble &	radius,
		const VectorInt &	ndiscs,
		int	flag_sel,
		bool	verbose
	)

Perform the Declustering task

Returns

Error return code

Parameters

[in]	dbin	input Db structure
[in]	model	Model structure
[in]	method	Method for declustering
[in]	neigh	ANeigh structure
[in]	dbgrid	Grid auxiliary Db structure
[in]	radius	Array of neighborhood radius
[in]	ndiscs	Array of discretization
[in]	flag_sel	1 to mask off samples with zero weight
[in]	verbose	Verbose option

int global_transitive	(	DbGrid *	dbgrid,
		Model *	model,
		int	flag_verbose,
		int	flag_regular,
		int	ndisc,
		double *	abundance,
		double *	sse,
		double *	cvtrans
	)

Estimation of the variance by transitive method

Returns

Error return code

Parameters

[in]	dbgrid	Db structure containing the dicretization grid
[in]	model	Model structure
[in]	flag_verbose	1 for a verbose output
[in]	flag_regular	1 for regular; 0 for stratified
[in]	ndisc	Number of Discretization steps
[out]	abundance	Global estimated abundance
[out]	sse	Global standard deviation
[out]	cvtrans	CV transitive

int inhomogeneous_kriging	(	Db *	dbdat,
		Db *	dbsrc,
		Db *	dbout,
		double	power,
		int	flag_source,
		Model *	model_dat,
		Model *	model_src
	)

Inhomogeneous Kriging with Sources

Returns

Error return code

Parameters

[in]	dbdat	Db structure containing Data
[in]	dbsrc	Db structure containing Sources
[in]	dbout	Output Db structure
[in]	power	Power of the Distance decay
[in]	flag_source	If the result is the source, rather than diffusion
[in]	model_dat	Model structure for the data
[in]	model_src	Model structure for the sources

int is_flag_data_disc_defined

(

void

)

void krige_dual_print	(	int	nech,
		int	neq,
		int	nred,
		int *	flag,
		double *	dual
	)

Print the Dual matrix

Parameters

[in]	nech	Number of active points (optional)
[in]	neq	Number of equations
[in]	nred	Reduced number of equations
[in]	flag	Flag array (optional)
[in]	dual	Kriging Dual matrix

int krige_koption_manage	(	int	mode,
		int	flag_check,
		const EKrigOpt &	calcul,
		int	flag_rand,
		const VectorInt &	ndiscs
	)

Management of Kriging option

Returns

Error return code

Parameters

[in]	mode	1 for allocation; -1 for deallocation
[in]	flag_check	1 if the file should be checked
[in]	calcul	Type of calculation (EKrigOpt)
[in]	flag_rand	0 if the second discretization is regular 1 if the second point must be randomized
[in]	ndiscs	Discretization parameters (or NULL)

Remarks

This function manages the global structure KOPTION

void krige_lhs_print	(	int	nech,
		int	neq,
		int	nred,
		int *	flagloc,
		double *	lhs
	)

Print the L.H.S. matrix

Parameters

[in]	nech	Number of active points (optional)
[in]	neq	Number of equations
[in]	nred	Reduced number of equations
[in]	flagloc	Flag array (optional)
[in]	lhs	Kriging L.H.S

void krige_rhs_print	(	int	nvar,
		int	nech,
		int	neq,
		int	nred,
		int *	flag,
		double *	rhs
	)

Print the R.H.S. matrix

Parameters

[in]	nvar	Number of variables
[in]	nech	Number of active points (optional)
[in]	neq	Number of equations
[in]	nred	Reduced number of equations
[in]	flag	Flag array (optional)
[in]	rhs	Kriging R.H.S. matrix

int krigsampling_f	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		double	beta,
		int	nsize1,
		int *	ranks1,
		int	nsize2,
		int *	ranks2,
		bool	flag_std,
		int	verbose
	)

Perform the Kriging procedure using the parcimonious search within the whole input data set

Returns

Error retun code

Parameters

[in]	dbin	Input Db structure
[in]	dbout	Output Db structure
[in]	model	Model structure
[in]	beta	Thresholding value
[in]	nsize1	Number of exact pivots currently selected
[in]	ranks1	Ranks of exact pivots
[in]	nsize2	Number of ACP pivots currently selected
[in]	ranks2	Ranks of ACP pivots
[in]	flag_std	Option for storing the standard deviation
[in]	verbose	Verbose flag

int krigsum	(	Db *	dbin,
		Db *	dbout,
		Model *	model,
		ANeigh *	neigh,
		bool	flag_positive,
		const NamingConvention &	namconv
	)

Punctual Multivariate Kriging under a constraint

Returns

Error return code

Parameters

[in]	dbin	input Db structure
[in]	dbout	output Db structure
[in]	model	Model structure (univariate)
[in]	neigh	ANeigh structure
[in]	flag_positive	1 for a positive constraints
[in]	namconv	Naming convention

Remarks

All the variables are estimated using the same model

In this procedure, we assume that:

- the problem is multivariate ("z" variables)

- the constraints is stored in "sum" (only used in dbout)

int sampling_f	(	Db *	db,
		Model *	model,
		double	beta,
		int	method1,
		int	nsize1_max,
		int	nsize1,
		int *	ranks1,
		int	method2,
		int	nsize2_max,
		int	nsize2,
		int *	ranks2,
		int	verbose
	)

Optimize the sampling design

Returns

Error retun code

Parameters

[in]	db	Db structure
[in]	model	Model structure
[in]	beta	Thresholding value
[in]	method1	Criterion for choosing exact pivots 1 : Local evaluation 2 : Global evaluation
[in]	nsize1_max	Maximum number of exact pivots
[in]	nsize1	Number of exact pivots currently selected
[in]	ranks1	Ranks of exact pivots
[in]	method2	Criterion for choosing ACP pivots 1 : Local evaluation 2 : Global evaluation
[in]	nsize2_max	Maximum number of ACP pivots
[in]	nsize2	Number of ACP pivots currently selected
[in]	ranks2	Ranks of ACP pivots
[in]	verbose	1 for a verbose output

void set_DBIN

(

Db *

dbin

)

void set_DBOUT

(

Db *

dbout

)

int st_crit_global	(	Db *	db,
		Model *	model,
		int	nsize1,
		int *	ranks1,
		int *	rother,
		double *	crit
	)

Evaluate the improvement in adding a new pivot on the global score

Returns

Error retun code

Parameters

[in]	db	Db structure
[in]	model	Model structure
[in]	nsize1	Number of exact pivots currently selected
[in]	ranks1	Ranks of exact pivots
[in]	rother	Ranks of the idle samples
[out]	crit	Array of criterion

int st_krige_data	(	Db *	db,
		Model *	model,
		double	beta,
		int	nsize1,
		int *	ranks1,
		int	nsize2,
		int *	ranks2,
		int *	rother,
		int	flag_abs,
		double *	data_est,
		double *	data_var
	)

Perform the estimation at the data points

Returns

Error retun code

Parameters

[in]	db	Db structure
[in]	model	Model structure
[in]	beta	Thresholding value
[in]	nsize1	Number of exact pivots currently selected
[in]	ranks1	Ranks of exact pivots
[in]	nsize2	Number of ACP pivots currently selected
[in]	ranks2	Ranks of ACP pivots
[in]	rother	Ranks of the idle samples
[in]	flag_abs	1 Modify 'daata_est' to store the estimation error
[out]	data_est	Array of estimation at samples
[out]	data_var	Array of estimation variance at samples