Calculating Covariance Matrix

Functions
MatrixRectangular	Model::evalCovMatrix (Db *db1, Db *db2=nullptr, int ivar0=-1, int jvar0=-1, const VectorInt &nbgh1=VectorInt(), const VectorInt &nbgh2=VectorInt(), const CovCalcMode *mode=nullptr)
MatrixSquareSymmetric	Model::evalCovMatrixSymmetric (Db *db1, int ivar0=-1, const VectorInt &nbgh1=VectorInt(), const CovCalcMode *mode=nullptr)
MatrixSparse *	Model::evalCovMatrixSparse (Db *db1, Db *db2=nullptr, int ivar0=-1, int jvar0=-1, const VectorInt &nbgh1=VectorInt(), const VectorInt &nbgh2=VectorInt(), const CovCalcMode *mode=nullptr, double eps=EPSILON3)
VectorDouble	Model::evalCovMatrixV (Db *db1, Db *db2=nullptr, int ivar0=-1, int jvar0=-1, const VectorInt &nbgh1=VectorInt(), const VectorInt &nbgh2=VectorInt(), const CovCalcMode *mode=nullptr)
MatrixRectangular	Model::evalCovMatrixOptim (const Db *db1, const Db *db2=nullptr, int ivar0=-1, int jvar0=-1, const VectorInt &nbgh1=VectorInt(), const VectorInt &nbgh2=VectorInt(), const CovCalcMode *mode=nullptr)
MatrixSquareSymmetric	Model::evalCovMatrixSymmetricOptim (const Db *db1, int ivar0=-1, const VectorInt &nbgh1=VectorInt(), const CovCalcMode *mode=nullptr)
double	Model::extensionVariance (const Db *db, const VectorDouble &ext, const VectorInt &ndisc, const VectorDouble &angles=VectorDouble(), const VectorDouble &x0=VectorDouble(), int ivar=0, int jvar=0)
double	Model::samplingDensityVariance (const Db *db, const VectorDouble &ext, const VectorInt &ndisc, const VectorDouble &angles=VectorDouble(), const VectorDouble &x0=VectorDouble(), int ivar=0, int jvar=0) const
double	Model::specificVolume (const Db *db, double mean, const VectorDouble &ext, const VectorInt &ndisc, const VectorDouble &angles=VectorDouble(), const VectorDouble &x0=VectorDouble(), int ivar=0, int jvar=0) const
double	Model::coefficientOfVariation (const Db *db, double volume, double mean, const VectorDouble &ext, const VectorInt &ndisc, const VectorDouble &angles=VectorDouble(), const VectorDouble &x0=VectorDouble(), int ivar=0, int jvar=0) const
double	Model::specificVolumeFromCoV (Db *db, double cov, double mean, const VectorDouble &ext, const VectorInt &ndisc, const VectorDouble &angles=VectorDouble(), const VectorDouble &x0=VectorDouble(), int ivar=0, int jvar=0) const
void	Model::evalZAndGradients (const SpacePoint &p1, const SpacePoint &p2, double &covVal, VectorDouble &covGp, VectorDouble &covGG, const CovCalcMode *mode=nullptr, bool flagGrad=false) const
void	Model::evalZAndGradients (const VectorDouble &vec, double &covVal, VectorDouble &covGp, VectorDouble &covGG, const CovCalcMode *mode=nullptr, bool flagGrad=false) const
double	Model::evalCov (const VectorDouble &incr, int icov=0, const ECalcMember &member=ECalcMember::fromKey("LHS")) const
void	Model::setSill (int icov, int ivar, int jvar, double value)
void	Model::setRangeIsotropic (int icov, double range)
void	Model::setMarkovCoeffs (int icov, const VectorDouble &coeffs)
void	Model::setCovaFiltered (int icov, bool filtered)
void	Model::setActiveFactor (int iclass)
int	Model::getActiveFactor () const
int	Model::getAnamNClass () const
const DriftList *	Model::getDriftList () const
	TODO : to be removed (encapsulation of DriftList) More...
const ADrift *	Model::getDrift (int il) const
int	Model::getDriftNumber () const
int	Model::getExternalDriftNumber () const
int	Model::getRankFext (int il) const
int	Model::getDriftEquationNumber () const
bool	Model::isDriftFiltered (unsigned int il) const
int	Model::getDriftMaxIRFOrder (void) const
bool	Model::isDriftDefined (const VectorInt &powers, int rank_fex=0) const
bool	Model::isDriftDifferentDefined (const VectorInt &powers, int rank_fex=-1) const
bool	Model::isDriftSampleDefined (const Db *db, int ib, int nech, const VectorInt &nbgh, const ELoc &loctype) const
void	Model::setDriftFiltered (int il, bool filtered)
VectorVectorDouble	Model::getDrifts (const Db *db, bool useSel=true)
void	Model::setBetaHat (const VectorDouble &betaHat)
double	Model::evalDrift (const Db *db, int iech, int il, const ECalcMember &member=ECalcMember::fromKey("LHS")) const
double	Model::evalDriftValue (const Db *db, int iech, int ivar, int ib, const ECalcMember &member=ECalcMember::fromKey("LHS")) const
VectorDouble	Model::evalDriftBySample (const Db *db, int iech, const ECalcMember &member=ECalcMember::fromKey("LHS")) const
void	Model::evalDriftBySampleInPlace (const Db *db, int iech, const ECalcMember &member, VectorDouble &drftab) const
MatrixRectangular	Model::evalDriftMatrix (const Db *db, int ivar0=-1, const VectorInt &nbgh=VectorInt(), const ECalcMember &member=ECalcMember::fromKey("LHS")) const
double	Model::evalDriftVarCoef (const Db *db, int iech, int ivar, const VectorDouble &coeffs) const
VectorDouble	Model::evalDriftVarCoefs (const Db *db, const VectorDouble &coeffs, int ivar=0, bool useSel=false) const
const CovContext &	Model::getContext () const
	TODO : to be removed (encapsulation of Context) More...
const ASpace *	Model::getASpace () const
const VectorDouble &	Model::getMeans () const
double	Model::getMean (int ivar) const
const VectorDouble &	Model::getCovar0s () const
double	Model::getCovar0 (int ivar, int jvar) const
double	Model::getField () const
int	Model::getDimensionNumber () const
void	Model::setMeans (const VectorDouble &mean)
void	Model::setMean (double mean, int ivar=0)
void	Model::setCovar0s (const VectorDouble &covar0)
void	Model::setCovar0 (int ivar, int jvar, double covar0)
void	Model::setField (double field)
const EModelProperty &	Model::getCovMode () const
Model *	Model::duplicate () const
Model *	Model::createReduce (const VectorInt &validVars) const
int	Model::getVariableNumber () const
int	Model::hasExternalCov () const
VectorDouble	Model::sampleUnitary (const VectorDouble &hh, int ivar=0, int jvar=0, VectorDouble codir=VectorDouble(), const CovCalcMode *mode=nullptr)
VectorDouble	Model::envelop (const VectorDouble &hh, int ivar=0, int jvar=0, int isign=1, VectorDouble codir=VectorDouble(), const CovCalcMode *mode=nullptr)
int	Model::fitFromCovIndices (Vario *vario, const VectorECov &types=ECov::fromKeys({"EXPONENTIAL"}), const Constraints &constraints=Constraints(), const Option_VarioFit &optvar=Option_VarioFit(), const Option_AutoFit &mauto=Option_AutoFit(), bool verbose=false)
int	Model::fit (Vario *vario, const VectorECov &types=ECov::fromKeys({"SPHERICAL"}), const Constraints &constraints=Constraints(), const Option_VarioFit &optvar=Option_VarioFit(), const Option_AutoFit &mauto=Option_AutoFit(), bool verbose=false)
int	Model::fitFromVMap (DbGrid *dbmap, const VectorECov &types=ECov::fromKeys({"SPHERICAL"}), const Constraints &constraints=Constraints(), const Option_VarioFit &optvar=Option_VarioFit(), const Option_AutoFit &mauto=Option_AutoFit(), bool verbose=false)
int	Model::buildVmapOnDbGrid (DbGrid *dbgrid, const NamingConvention &namconv=NamingConvention("VMAP")) const
int	Model::stabilize (double percent, bool verbose=false)
int	Model::standardize (bool verbose=false)
double	Model::gofToVario (const Vario *vario, bool verbose=true)
static void	Model::gofDisplay (double gof, bool byValue=true, const VectorDouble &thresholds={2., 5., 10., 100})
static VectorECov	Model::initCovList (const VectorInt &covranks)
bool	Model::isValid () const
VectorDouble	Model::sample (const VectorDouble &h, const VectorDouble &codir=VectorDouble(), int ivar=0, int jvar=0, const CovCalcMode *mode=nullptr, const CovInternal *covint=nullptr)
double	Model::evaluateOneIncr (double hh, const VectorDouble &codir=VectorDouble(), int ivar=0, int jvar=0, const CovCalcMode *mode=nullptr)
void	Model::evaluateMatInPlace (const CovInternal *covint, const VectorDouble &d1, MatrixSquareGeneral &covtab, bool flag_init=false, double weight=1., const CovCalcMode *mode=nullptr)
double	Model::evaluateOneGeneric (const CovInternal *covint, const VectorDouble &d1=VectorDouble(), double weight=1., const CovCalcMode *mode=nullptr)
VectorDouble	Model::evaluateFromDb (Db *db, int ivar=0, int jvar=0, const CovCalcMode *mode=nullptr)
double	Model::calculateStdev (Db *db1, int iech1, Db *db2, int iech2, bool verbose=false, double factor=1., const CovCalcMode *mode=nullptr)
double	Model::computeLogLikelihood (Db *db, bool verbose=false)

Detailed Description

These functions are meant to calculate the covariance Matrix between two Dbs or between a Db and itself. They take into account the presence of a possible selection They also account for heterotopy (if Z-variables are defined in the Db(s)

Parameters

db1	First Db
db2	(Optional second Db)
ivar0	Rank of the selected variable in the first Db (-1 for all variables)
jvar0	Rank of the selected variable in the second Db (-1 for all variables)
nbgh1	Vector of indices of active samples in first Db (optional)
nbgh2	Vector of indices of active samples in second Db (optional)
mode	CovCalcMode structure

Remarks

The returned matrix if dimension to nrows * ncols where

each term is the product of the number of active samples

by the number of samples where the variable is defined

Note

'dbin' and 'dbout' cannot be made 'const' as they can be updated

due to the presence of 'nostat'

Returns

A Matrix either in Dense or Sparse format

Function Documentation

buildVmapOnDbGrid()

int Model::buildVmapOnDbGrid	(	DbGrid *	dbgrid,
		const NamingConvention &	namconv = NamingConvention("VMAP")
	)		const

Calculate the variogram map from a Model (presented as Variogram, not Covariance)

Returns

Error return code

Parameters

[in]	dbgrid	Grid structure
[in]	namconv	Naming convention

calculateStdev()

double Model::calculateStdev	(	Db *	db1,
		int	iech1,
		Db *	db2,
		int	iech2,
		bool	verbose = false,
		double	factor = 1.,
		const CovCalcMode *	mode = nullptr
	)

Returns the standard deviation at a given increment for a given model between two samples of two Dbs

Parameters

[in]	db1	First Db
[in]	iech1	Rank in the first Db
[in]	db2	Second Db
[in]	iech2	Rank in the second Db
[in]	verbose	Verbose flag
[in]	factor	Multiplicative factor for standard deviation
[in]	mode	CovCalcMode structure

coefficientOfVariation()

double Model::coefficientOfVariation ( const Db *  db, double  volume, double  mean, const VectorDouble &  ext, const VectorInt &  ndisc, const VectorDouble &  angles = VectorDouble(), const VectorDouble &  x0 = VectorDouble(), int  ivar = 0, int  jvar = 0  ) const

inline

computeLogLikelihood()

double Model::computeLogLikelihood	(	Db *	db,
		bool	verbose = false
	)

Compute the log-likelihood (based on covariance)

Parameters

db	Db structure where variable are loaded from
verbose	Verbose flag

Remarks

The calculation considers all the active samples.

It can work in multivariate case with or without drift conditions (linked or not)

The algorithm is stopped (with a message) in the heterotopic case // TODO; improve for heterotopic case

createReduce()

Model * Model::createReduce

(

const VectorInt &

validVars

)

const

duplicate()

Model * Model::duplicate

(

)

const

envelop()

VectorDouble Model::envelop	(	const VectorDouble &	hh,
		int	ivar = 0,
		int	jvar = 0,
		int	isign = 1,
		VectorDouble	codir = VectorDouble(),
		const CovCalcMode *	mode = nullptr
	)

evalCov()

double Model::evalCov	(	const VectorDouble &	incr,
		int	icov = 0,
		const ECalcMember &	member = ECalcMember::fromKey("LHS")
	)		const

evalCovMatrix()

MatrixRectangular Model::evalCovMatrix ( Db *  db1, Db *  db2 = nullptr, int  ivar0 = -1, int  jvar0 = -1, const VectorInt &  nbgh1 = VectorInt(), const VectorInt &  nbgh2 = VectorInt(), const CovCalcMode *  mode = nullptr  )

inline

evalCovMatrixOptim()

MatrixRectangular Model::evalCovMatrixOptim ( const Db *  db1, const Db *  db2 = nullptr, int  ivar0 = -1, int  jvar0 = -1, const VectorInt &  nbgh1 = VectorInt(), const VectorInt &  nbgh2 = VectorInt(), const CovCalcMode *  mode = nullptr  )

inline

evalCovMatrixSparse()

MatrixSparse* Model::evalCovMatrixSparse ( Db *  db1, Db *  db2 = nullptr, int  ivar0 = -1, int  jvar0 = -1, const VectorInt &  nbgh1 = VectorInt(), const VectorInt &  nbgh2 = VectorInt(), const CovCalcMode *  mode = nullptr, double  eps = EPSILON3  )

inline

evalCovMatrixSymmetric()

MatrixSquareSymmetric Model::evalCovMatrixSymmetric ( Db *  db1, int  ivar0 = -1, const VectorInt &  nbgh1 = VectorInt(), const CovCalcMode *  mode = nullptr  )

inline

evalCovMatrixSymmetricOptim()

MatrixSquareSymmetric Model::evalCovMatrixSymmetricOptim ( const Db *  db1, int  ivar0 = -1, const VectorInt &  nbgh1 = VectorInt(), const CovCalcMode *  mode = nullptr  )

inline

evalCovMatrixV()

VectorDouble Model::evalCovMatrixV ( Db *  db1, Db *  db2 = nullptr, int  ivar0 = -1, int  jvar0 = -1, const VectorInt &  nbgh1 = VectorInt(), const VectorInt &  nbgh2 = VectorInt(), const CovCalcMode *  mode = nullptr  )

inline

evalDrift()

double Model::evalDrift	(	const Db *	db,
		int	iech,
		int	il,
		const ECalcMember &	member = ECalcMember::fromKey("LHS")
	)		const

Evaluate a given drift function for a given sample

Parameters

db	Db structure
iech	Rank of the target sample
il	Rank of the drift function
member	Member type (used to check filtering)

Returns

evalDriftBySample()

VectorDouble Model::evalDriftBySample	(	const Db *	db,
		int	iech,
		const ECalcMember &	member = ECalcMember::fromKey("LHS")
	)		const

evalDriftBySampleInPlace()

void Model::evalDriftBySampleInPlace	(	const Db *	db,
		int	iech,
		const ECalcMember &	member,
		VectorDouble &	drftab
	)		const

evalDriftMatrix()

MatrixRectangular Model::evalDriftMatrix ( const Db *  db, int  ivar0 = -1, const VectorInt &  nbgh = VectorInt(), const ECalcMember &  member = ECalcMember::fromKey("LHS")  ) const

inline

evalDriftValue()

double Model::evalDriftValue	(	const Db *	db,
		int	iech,
		int	ivar,
		int	ib,
		const ECalcMember &	member = ECalcMember::fromKey("LHS")
	)		const

evalDriftVarCoef()

double Model::evalDriftVarCoef	(	const Db *	db,
		int	iech,
		int	ivar,
		const VectorDouble &	coeffs
	)		const

Evaluate the drift with a given sample and a given variable The value is scaled by 'coeffs'

Parameters

[in]	db	Db structure
[in]	iech	Rank of the sample
[in]	ivar	Rank of the variable
[in]	coeffs	Vector of coefficients

evalDriftVarCoefs()

VectorDouble Model::evalDriftVarCoefs	(	const Db *	db,
		const VectorDouble &	coeffs,
		int	ivar = 0,
		bool	useSel = false
	)		const

A vector of the drift evaluation (for all samples)

Parameters

db	Db structure
coeffs	Vector of drift coefficients
ivar	Variable rank (used for constant drift value)
useSel	When TRUE, only non masked samples are returned

Returns

The vector of values

Remarks

When no drift is defined, a vector is returned filled with the variable mean

evaluateFromDb()

VectorDouble Model::evaluateFromDb	(	Db *	db,
		int	ivar = 0,
		int	jvar = 0,
		const CovCalcMode *	mode = nullptr
	)

Evaluate the model on a Db

Parameters

[in]	db	Db structure
[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	mode	CovCalcMode structure

evaluateMatInPlace()

void Model::evaluateMatInPlace	(	const CovInternal *	covint,
		const VectorDouble &	d1,
		MatrixSquareGeneral &	covtab,
		bool	flag_init = false,
		double	weight = 1.,
		const CovCalcMode *	mode = nullptr
	)

Returns the covariances for an increment This is the generic internal function It can be called for stationary or non-stationary case

Parameters

[in]	covint	Internal structure for non-stationarityAddress for the next term after the drift or NULL (for stationary case)
[in]	mode	CovCalcMode structure
[in]	flag_init	Initialize the array beforehand
[in]	weight	Multiplicative weight
[in]	d1	Distance vector
[out]	covtab	Covariance array

evaluateOneGeneric()

double Model::evaluateOneGeneric	(	const CovInternal *	covint,
		const VectorDouble &	d1 = VectorDouble(),
		double	weight = 1.,
		const CovCalcMode *	mode = nullptr
	)

Returns the covariance for an increment This is the generic internal function It can be called for stationary or non-stationary case

Parameters

[in]	covint	Internal structure for non-stationarityAddress for the next term after the drift or NULL (for stationary case)
[in]	mode	CovCalcMode structure
[in]	weight	Multiplicative weight
[in]	d1	Distance vector

evaluateOneIncr()

double Model::evaluateOneIncr	(	double	hh,
		const VectorDouble &	codir = VectorDouble(),
		int	ivar = 0,
		int	jvar = 0,
		const CovCalcMode *	mode = nullptr
	)

Calculate the value of the model for a set of distances

Returns

The model value

Parameters

[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	mode	CovCalcMode structure
[in]	codir	Array giving the direction coefficients (optional)
[in]	hh	Vector of increments

evalZAndGradients() [1/2]

void Model::evalZAndGradients	(	const SpacePoint &	p1,
		const SpacePoint &	p2,
		double &	covVal,
		VectorDouble &	covGp,
		VectorDouble &	covGG,
		const CovCalcMode *	mode = nullptr,
		bool	flagGrad = false
	)		const

evalZAndGradients() [2/2]

void Model::evalZAndGradients	(	const VectorDouble &	vec,
		double &	covVal,
		VectorDouble &	covGp,
		VectorDouble &	covGG,
		const CovCalcMode *	mode = nullptr,
		bool	flagGrad = false
	)		const

extensionVariance()

double Model::extensionVariance ( const Db *  db, const VectorDouble &  ext, const VectorInt &  ndisc, const VectorDouble &  angles = VectorDouble(), const VectorDouble &  x0 = VectorDouble(), int  ivar = 0, int  jvar = 0  )

inline

fit()

int Model::fit	(	Vario *	vario,
		const VectorECov &	types = ECov::fromKeys({"SPHERICAL"}),
		const Constraints &	constraints = Constraints(),
		const Option_VarioFit &	optvar = Option_VarioFit(),
		const Option_AutoFit &	mauto = Option_AutoFit(),
		bool	verbose = false
	)

Automatic Fitting procedure from an experimental Variogram

Parameters

vario	Experimental variogram to be fitted
types	Vector of ECov (see remarks)
constraints	Set of Constraints
optvar	Set of options
mauto	Special parameters for Automatic fitting procedure (instance of Option_AutoFit), for exemple wmode (type of weighting function)
verbose	Verbose option

Remarks

If no list of specific basic structure is specified, the automatic fitting is performed using a single spherical structure by default.

Returns

0 if no error, 1 otherwise

TODO : What to do with that ?

fitFromCovIndices()

int Model::fitFromCovIndices	(	Vario *	vario,
		const VectorECov &	types = ECov::fromKeys({"EXPONENTIAL"}),
		const Constraints &	constraints = Constraints(),
		const Option_VarioFit &	optvar = Option_VarioFit(),
		const Option_AutoFit &	mauto = Option_AutoFit(),
		bool	verbose = false
	)

Automatic Fitting procedure

Parameters

vario	Experimental variogram to be fitted
types	Vector of ECov integer values
constraints	Set of Constraints
optvar	Set of options
mauto	Special parameters for Automatic fitting procedure
verbose	Verbose option

Returns

0 if no error, 1 otherwise

TODO : What to do with that ?

fitFromVMap()

int Model::fitFromVMap	(	DbGrid *	dbmap,
		const VectorECov &	types = ECov::fromKeys({"SPHERICAL"}),
		const Constraints &	constraints = Constraints(),
		const Option_VarioFit &	optvar = Option_VarioFit(),
		const Option_AutoFit &	mauto = Option_AutoFit(),
		bool	verbose = false
	)

Automatic Fitting procedure from A Variogram Map stored on a DbGrid

Parameters

dbmap	DbGrid containing the Variogram Map
types	Vector of ECov
constraints	Set of Constraints
optvar	Set of options
mauto	Special parameters for Automatic fitting procedure (instance of Option_AutoFit), for exemple wmode (type of weighting function)
verbose	Verbose option

Returns

0 if no error, 1 otherwise

getActiveFactor()

int Model::getActiveFactor

(

)

const

getAnamNClass()

int Model::getAnamNClass

(

)

const

getASpace()

const ASpace* Model::getASpace ( ) const

inline

getContext()

const CovContext& Model::getContext ( ) const

inline

TODO : to be removed (encapsulation of Context)

getCovar0()

double Model::getCovar0 ( int  ivar, int  jvar  ) const

inline

getCovar0s()

const VectorDouble& Model::getCovar0s ( ) const

inline

getCovMode()

const EModelProperty & Model::getCovMode

(

)

const

getDimensionNumber()

int Model::getDimensionNumber ( ) const

inline

getDrift()

const ADrift * Model::getDrift

(

int

il

)

const

getDriftEquationNumber()

int Model::getDriftEquationNumber

(

)

const

getDriftList()

const DriftList * Model::getDriftList

(

)

const

TODO : to be removed (encapsulation of DriftList)

getDriftMaxIRFOrder()

int Model::getDriftMaxIRFOrder

(

void

)

const

getDriftNumber()

int Model::getDriftNumber

(

)

const

getDrifts()

VectorVectorDouble Model::getDrifts	(	const Db *	db,
		bool	useSel = true
	)

getExternalDriftNumber()

int Model::getExternalDriftNumber

(

)

const

getField()

double Model::getField ( ) const

inline

getMean()

double Model::getMean ( int  ivar ) const

inline

getMeans()

const VectorDouble& Model::getMeans ( ) const

inline

getRankFext()

int Model::getRankFext

(

int

il

)

const

getVariableNumber()

int Model::getVariableNumber ( ) const

inline

gofDisplay()

void Model::gofDisplay ( double  gof, bool  byValue = true, const VectorDouble &  thresholds = {2., 5., 10., 100}  )

static

Printout of statement concerning the Quality of the GOF

Parameters

gof	Value of the Gof
byValue	true: display GOF value; false: print its quality level
thresholds	Vector giving the Quality thresholds

gofToVario()

double Model::gofToVario	(	const Vario *	vario,
		bool	verbose = true
	)

Evaluate the Goodness-of_fit of the Model on the Experimental Variogram It is expressed as the average departure between Model and Variogram scaled to the variance. As this variance may be poorly calculated (< gmax / 5), it may be replaced by the largest value (gmax) divided by 2 (highly non_stationary cases).

Parameters

vario	Experimental variogram
verbose	Verbose flag

Returns

Value for the Goodness-of_fit (as percentage of the total sill)

hasExternalCov()

int Model::hasExternalCov

(

)

const

initCovList()

VectorECov Model::initCovList ( const VectorInt &  covranks )

static

isDriftDefined()

bool Model::isDriftDefined	(	const VectorInt &	powers,
		int	rank_fex = 0
	)		const

isDriftDifferentDefined()

bool Model::isDriftDifferentDefined	(	const VectorInt &	powers,
		int	rank_fex = -1
	)		const

isDriftFiltered()

bool Model::isDriftFiltered

(

unsigned int

il

)

const

isDriftSampleDefined()

bool Model::isDriftSampleDefined	(	const Db *	db,
		int	ib,
		int	nech,
		const VectorInt &	nbgh,
		const ELoc &	loctype
	)		const

isValid()

bool Model::isValid

(

)

const

sample()

VectorDouble Model::sample	(	const VectorDouble &	h,
		const VectorDouble &	codir = VectorDouble(),
		int	ivar = 0,
		int	jvar = 0,
		const CovCalcMode *	mode = nullptr,
		const CovInternal *	covint = nullptr
	)

Calculate the value of the model for a set of distances

Returns

Array containing the model values

Parameters

[in]	ivar	Rank of the first variable
[in]	jvar	Rank of the second variable
[in]	codir	Array giving the direction coefficients (optional)
[in]	h	Vector of increments
[in]	mode	CovCalcMode structure
[in]	covint	Non-stationary parameters

sampleUnitary()

VectorDouble Model::sampleUnitary	(	const VectorDouble &	hh,
		int	ivar = 0,
		int	jvar = 0,
		VectorDouble	codir = VectorDouble(),
		const CovCalcMode *	mode = nullptr
	)

Returns the value of the normalized covariance (by the variance/covariance value) for a given pair of variables

Parameters

hh	Vector of distances
ivar	Rank of the first variable
jvar	Rank of the second variable
codir	Direction coefficients
mode	CovCalcMode structure

Returns

samplingDensityVariance()

double Model::samplingDensityVariance ( const Db *  db, const VectorDouble &  ext, const VectorInt &  ndisc, const VectorDouble &  angles = VectorDouble(), const VectorDouble &  x0 = VectorDouble(), int  ivar = 0, int  jvar = 0  ) const

inline

setActiveFactor()

void Model::setActiveFactor

(

int

iclass

)

setBetaHat()

void Model::setBetaHat

(

const VectorDouble &

betaHat

)

setCovaFiltered()

void Model::setCovaFiltered	(	int	icov,
		bool	filtered
	)

setCovar0()

void Model::setCovar0	(	int	ivar,
		int	jvar,
		double	covar0
	)

setCovar0s()

void Model::setCovar0s

(

const VectorDouble &

covar0

)

setDriftFiltered()

void Model::setDriftFiltered	(	int	il,
		bool	filtered
	)

setField()

void Model::setField

(

double

field

)

setMarkovCoeffs()

void Model::setMarkovCoeffs	(	int	icov,
		const VectorDouble &	coeffs
	)

setMean()

void Model::setMean	(	double	mean,
		int	ivar = 0
	)

setMeans()

void Model::setMeans

(

const VectorDouble &

mean

)

setRangeIsotropic()

void Model::setRangeIsotropic	(	int	icov,
		double	range
	)

setSill()

void Model::setSill	(	int	icov,
		int	ivar,
		int	jvar,
		double	value
	)

specificVolume()

double Model::specificVolume ( const Db *  db, double  mean, const VectorDouble &  ext, const VectorInt &  ndisc, const VectorDouble &  angles = VectorDouble(), const VectorDouble &  x0 = VectorDouble(), int  ivar = 0, int  jvar = 0  ) const

inline

specificVolumeFromCoV()

double Model::specificVolumeFromCoV ( Db *  db, double  cov, double  mean, const VectorDouble &  ext, const VectorInt &  ndisc, const VectorDouble &  angles = VectorDouble(), const VectorDouble &  x0 = VectorDouble(), int  ivar = 0, int  jvar = 0  ) const

inline

stabilize()

int Model::stabilize	(	double	percent,
		bool	verbose = false
	)

Stabilize the model (in the monovariate case)

Returns

Error returned code

Parameters

[in]	percent	Percentage of nugget effect added
[in]	verbose	true for a verbose output

Remarks

If the model only contains GAUSSIAN structures, add

a NUGGET EFFECT structure with a sill equal to a percentage

of the total sill of the GAUSSIAN component(s)

This function does not do anything in the multivariate case

standardize()

int Model::standardize

(

bool

verbose = false

)

Normalize the model

Parameters

[in]

verbose

true for a verbose output