Description of the class TMaternIICorrFunction. More...

#include <TMaternIICorrFunction.h>

Inheritance diagram for URANIE::Modeler::TMaternIICorrFunction:

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Collaboration diagram for URANIE::Modeler::TMaternIICorrFunction:

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Public Member Functions
Constructor and Destructor
	TMaternIICorrFunction (Int_t nbDim, Double_t nu=1.5, Double_t *corrLength=NULL)
	Default constructor.

virtual	~TMaternIICorrFunction ()
	Default destructor.

Get and Set methods
Double_t	getParameterNu ()
	Returns the regularity coefficients $\nu$ .

virtual void	setParameters (Double_t *newParams)
	Sets the parameters of the MaternII correlation function.

void	setParameterNu (Double_t nu)
	Sets the correlation length parameter.

void	setCorrLengths (Double_t *corrLength)
	Sets the correlation length parameter.

Tools
virtual TCorrelationFunction *	clone ()
	Return a clone of the correlation function.

virtual void	eval (Int_t n1, Double_t x1, Int_t n2, Double_t x2, Double_t *C)
	Compute the correlation between x1 and x2.

Printing Log
virtual void	printLog (Option_t *option="")

Public Member Functions inherited from URANIE::Modeler::TCorrelationFunction
	TCorrelationFunction ()
	Default constructor.

virtual	~TCorrelationFunction ()
	Default destructor.

Int_t	getNParameters ()
	Return the number of parameters of the correlation function.

Int_t	getNDimensions ()
	Return the number of dimensions of the input space.

Int_t	getNCorrLengths ()
	Return the number of correlation lengths.

Double_t *	getParameters ()
	Returns an array containing the parameters of the function.

Double_t *	getCorrLengths ()
	Returns an array containing only the correlation lengths.

void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

Public Attributes
Double_t	_dNu
	regularity coefficient $\nu$

Public Attributes inherited from URANIE::Modeler::TCorrelationFunction
Bool_t	_blog
	Boolean to decide if the log information is shown or not.

Double_t *	parameters
	array containing the values of the function parameters. Correlation lengths are stored last.

Double_t *	_lCorrLengths
	array containing only the correlation lengths.

Int_t	nbDimensions
	number of dimensions of the input space

Int_t	nbParameters
	total number of parameters

Int_t	nbCorrLengths
	number of correlation lengths

Detailed Description

Description of the class TMaternIICorrFunction.

This class defines the shape of the correlation between two sets of variables as the Matï¿½rn II function.

Constructor & Destructor Documentation

◆ TMaternIICorrFunction()

URANIE::Modeler::TMaternIICorrFunction::TMaternIICorrFunction	(	Int_t	nbDim,
		Double_t	nu = `1.5`,
		Double_t *	corrLength = `NULL`
	)

Default constructor.

Parameters

nbDim	(Int_t): number of dimension of the input variables
nu	(Double_t): regularity(?) parameter $\nu$ . (default = 1.5)
corrLength	(Double_t*): array of the correlation lengths for the function (size: nbDim). If NULL, the correlation lengths are initialised to 0.1. (default = NULL).

Referenced by ClassImp().

◆ ~TMaternIICorrFunction()

virtual URANIE::Modeler::TMaternIICorrFunction::~TMaternIICorrFunction ( )

virtual

Default destructor.

Referenced by ClassImp().

Member Function Documentation

◆ clone()

virtual TCorrelationFunction * URANIE::Modeler::TMaternIICorrFunction::clone ( )

virtual

Return a clone of the correlation function.

The clone must be destroyed by the object (or user) calling the function.

Implements URANIE::Modeler::TCorrelationFunction.

Referenced by ClassImp().

◆ eval()

virtual void URANIE::Modeler::TMaternIICorrFunction::eval	(	Int_t	n1,
		Double_t *	x1,
		Int_t	n2,
		Double_t *	x2,
		Double_t *	C
	)

virtual

Compute the correlation between x1 and x2.

The order of the indices in the flat arrays are the following: x[i+j*d] = X[i,j] where X is a matrix of size (d x n), i is the line index, and j the column index. The results are stored in the flat array C.

Warning: Be aware that the matrix corresponding to flat arrays x1 and x2 have input dimensions as LINE, not columns. This means that for a set of point {(a1,b1),(a2,b2),(a3,b3)}, the corresponding matrix X will be: $\left( \begin{array}{ccc} a1 & a2 & a3 \\ b1 & b2 & b3 \end{array} \right)$ and the corresponding flat array x = {a1,b1,a2,b2,a3,b3}.

Parameters

n1	(Int_t): number of elements of x1
x1	(Double_t): data set x1 represented as a flat array (size: d n1)
n2	(Int_t): number of elements of x2
x2	(Double_t): data set x2 represented as a flat array (size: d n2)
C	(Double_t): correlation matrix represented as a flat array (size: n1 n2)