Description of the class TGaussianSampling. More...

#include <TGaussianSampling.h>

Inheritance diagram for URANIE::Sampler::TGaussianSampling:

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Collaboration diagram for URANIE::Sampler::TGaussianSampling:

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Public Types
enum	EType { kLHS , kSRS , kUnknown }

Public Member Functions
Constructor and Destructor
	TGaussianSampling (URANIE::DataServer::TDataServer tds, Option_t option="lhs", Int_t nCalcul=100)
	Constructor from a TDataServer, the name of the method and the size of the sample.

virtual	~TGaussianSampling ()
	Default destructor.

State machine
For the stochastic methods, two states can be created init and terminate which are executed just before the generation of the sample for the first one, and after the generation for the second one. They can be used for pre and post treatment purpose.
virtual void	init ()
	The preprocessing step.

virtual void	terminate ()
	The post-processing step.

void	generateSample (Option_t *option="")
	Generates the sample.

void	generateConditionalSample (TVectorD vecOf01, Int_t no, Int_t ni)

void	setIteratorNameNo (TString sNameNo)

void	setIteratorNameNi (TString sNameNi)

const char *	getIteratorNameNo ()

const char *	getIteratorNameNi ()

Correlation matrix
Methods to specify correlation between attributes
void	setUserCorrelation (Int_t indx, Int_t indy, double value)
	Defines a correlation between two attributes given by their indexes.

void	setUserVarianceCovarianceMatrix (Int_t indx, Int_t indy, double value)

void	setUserCorrelation (TString xname, TString yname, double value)
	Defines a correlation between two attributes given by their names.

void	setUserCorrelation (URANIE::DataServer::TAttribute x, URANIE::DataServer::TAttribute y, Double_t value)
	Defines a correlation between two attributes.

void	setFixedSumCovariance (TString method="simple")
	Defines a special covariance matrix.

TMatrixD	getCorrelationMatrix ()
	Retrieve the correlation matrix.

TMatrixD	getCovarianceMatrix ()
	Retrieve the covariance matrix.

Printing Log
virtual void	printLog (Option_t *option="")
	Prints the log.

Public Member Functions inherited from URANIE::Sampler::TSamplerStochastic
	TSamplerStochastic (URANIE::DataServer::TDataServer tds, Option_t option, Int_t nCalcul)
	Constructor with a dataserver.

virtual	~TSamplerStochastic ()
	Default destructor.

void	setSeed (Int_t ind=0)
	Init the seed.

Int_t	getSeed ()
	Returns the seed value.

Public Member Functions inherited from URANIE::Sampler::TSampler
	TSampler (URANIE::DataServer::TDataServer tds, Option_t option, Int_t nCalcul)
	Constructor with a TDataServer, the options and the size of the sample.

virtual	~TSampler ()
	Default destructor.

Int_t	GetID ()
	Returns the ID of the class.

void	setMethodName (TString str)
	Sets the method name in a global variable.

TString	getMethodName ()
	Gets the method name.

virtual URANIE::DataServer::TDataServer *	getTDS ()
	Return the TDS filling by the sampling algorithm.

void	parseOption (Option_t *option)
	Parse the option.

virtual void	createListOfAttributes ()
	Creates the List of attributes to simulate.

virtual void	createTuple ()
	Creates the TDSNtupleD of data with only the TStochasticAttributes.

URANIE::DataServer::TDSNtupleD *	getTuple ()
	Returns the TDSNtupleD of data.

virtual void	fillOtherAttributes ()
	Fills the TDSNtupleD of data with other TFormulaAttributes.

void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

Public Attributes
TMatrixD	_corrmatrix

TMatrixD	_covmatrix
	The correlation matrix.

EType	_ntype
	The covariance matrix.

Public Attributes inherited from URANIE::Sampler::TSampler
Int_t	_nS
	The size of the sample.

Int_t	_nX
	The size of attributes to sample.

URANIE::DataServer::TDSNtupleD *	_ntsample
	the tntuple of data

TString	_sMethod
	The title of the sampler method.

Bool_t	_blog
	Log Printing.

Bool_t	_bupdateFile
	Update the back up file when generating the attributeformula if there is some.

URANIE::DataServer::TDataServer *	_tds
	Pointer to a TDS.

TList *	_lstOfAttributesToSample
	The list of Stochastic Attributes to sample.

Private Member Functions
void	splitMatrixVarCoVar (TMatrixD matVarCovar, TVectorD vecOfUnit, TMatrixD &matVarCovar0, TMatrixD &matVarCovar1, TMatrixD &matVarCovar01)
	The type of sampling (SRS, LHS)

void	generatenStandardPoints (TMatrixD &matPoints)

Private Attributes
TString	_sIteratorNameNo
	The specific iterator attribute for NO integer.

TString	_sIteratorNameNi
	The specific iterator attribute for NI integer.

Additional Inherited Members
State machine
For the stochastic methods, two2 states can be created init and terminate which are executed just before the generation of the sample for the first one, and after the generation for the second one. They can be used for pre and post treatment purpose.
Protected Attributes inherited from URANIE::Sampler::TSamplerStochastic
Int_t	_nSeed

Detailed Description

Description of the class TGaussianSampling.

This class provides a data generator for gaussian random variables.

To generate values from gaussian random variables, we proceed as follows:

the user defines a list of gaussian random variables .
a covariance matrix $C \left(n \times n\right)$ is constructed from the variances of the , and possibly modified by the user (adding covariances)
we apply a Cholesky decomposition on the matrix , such that , where $U \left(n \times n\right)$ is an upper triangular matrix.
we build a data matrix $A \left(k \times n\right)$ of random values, generated from gaussian random variables , with mean 0 and variance 1
we apply the formula , where $M \left(k \times n\right)$ is a matrix containing the means of the variables .

The output matrix $Y \left(k \times n\right)$ contains the realisation of the random variables , following the covariance matrix .

Member Enumeration Documentation

◆ EType

enum URANIE::Sampler::TGaussianSampling::EType

Enumerator
kLHS
kSRS
kUnknown

Constructor & Destructor Documentation

◆ TGaussianSampling()

URANIE::Sampler::TGaussianSampling::TGaussianSampling	(	URANIE::DataServer::TDataServer *	tds,
		Option_t *	option = `"lhs"`,
		Int_t	nCalcul = `100`
	)

Constructor from a TDataServer, the name of the method and the size of the sample.

Parameters

tds	(TDataServer *) the pointer of the TDataServer. It must contains objects inheriting from TStochasticAttribute
option	(Option_t *) Method of sampling. It can be "lhs" (Latin Hypercube Sampling) or "srs" (Simple Random Sampling) (Default = "lhs").
nCalcul	(Int_t) number of values to generate for each attribute (Default = 100)

Referenced by ClassImp().

◆ ~TGaussianSampling()

virtual URANIE::Sampler::TGaussianSampling::~TGaussianSampling ( )

virtual

Default destructor.

Referenced by ClassImp().

Member Function Documentation

◆ generateConditionalSample()

void URANIE::Sampler::TGaussianSampling::generateConditionalSample	(	TVectorD	vecOf01,
		Int_t	no,
		Int_t	ni
	)

Referenced by ClassImp().

◆ generatenStandardPoints()

void URANIE::Sampler::TGaussianSampling::generatenStandardPoints ( TMatrixD & matPoints )

private

Referenced by ClassImp().

◆ generateSample()

void URANIE::Sampler::TGaussianSampling::generateSample ( Option_t * option = "" )

virtual

Generates the sample.

Implements URANIE::Sampler::TSampler.

Referenced by ClassImp().

◆ getCorrelationMatrix()

TMatrixD URANIE::Sampler::TGaussianSampling::getCorrelationMatrix ( )

inline

Retrieve the correlation matrix.

References _corrmatrix.

◆ getCovarianceMatrix()

TMatrixD URANIE::Sampler::TGaussianSampling::getCovarianceMatrix ( )

inline

Retrieve the covariance matrix.

References _covmatrix.

◆ getIteratorNameNi()

const char * URANIE::Sampler::TGaussianSampling::getIteratorNameNi ( )

inline

References _sIteratorNameNi.

◆ getIteratorNameNo()

const char * URANIE::Sampler::TGaussianSampling::getIteratorNameNo ( )

inline

References _sIteratorNameNo.

◆ init()

virtual void URANIE::Sampler::TGaussianSampling::init ( )

virtual

The preprocessing step.

Reimplemented from URANIE::Sampler::TSamplerStochastic.

Referenced by ClassImp().

◆ printLog()

virtual void URANIE::Sampler::TGaussianSampling::printLog ( Option_t * option = "" )

virtual

Prints the log.

Reimplemented from URANIE::Sampler::TSamplerStochastic.

Referenced by ClassImp().

◆ setFixedSumCovariance()

void URANIE::Sampler::TGaussianSampling::setFixedSumCovariance ( TString method = "simple" )

Defines a special covariance matrix.

The covariance matrix is defined such that the sum of the generated values is always equal to the sum of the random variables' means.

Warning: THIS METHOD IS EXPERIMENTAL. Its advantage is to preserve the "gaussian nature" of the generated data. This might be important when working on a small number of variables.

Parameters

method (TString) method to compute the covariance matrix. Two are available: the "simple" method is very simple to implement but may be loose on standard deviations reconstruction. The "exact" method is more tricky but should preserve the standard deviations.
(Default = "simple")

Referenced by ClassImp().

◆ setIteratorNameNi()

void URANIE::Sampler::TGaussianSampling::setIteratorNameNi ( TString sNameNi )

inline

References _sIteratorNameNi.

◆ setIteratorNameNo()

void URANIE::Sampler::TGaussianSampling::setIteratorNameNo ( TString sNameNo )

inline

References _sIteratorNameNo.

◆ setUserCorrelation() [1/3]

void URANIE::Sampler::TGaussianSampling::setUserCorrelation	(	Int_t	indx,
		Int_t	indy,
		double	value
	)

Defines a correlation between two attributes given by their indexes.

Warning: The indexes' range is from 0 to n-1.

Parameters

indx	(Int_t) the index of the first attribute
indy	(Int_t) the index of the second attribute
value	(Double_t) the correlation factor between the two attributes

Referenced by ClassImp().

◆ setUserCorrelation() [2/3]

void URANIE::Sampler::TGaussianSampling::setUserCorrelation	(	TString	xname,
		TString	yname,
		double	value
	)

Defines a correlation between two attributes given by their names.

Parameters

xname	(TString) the first attribute
yname	(TString) the second attribute
value	(Double_t) the correlation factor between x and y

◆ setUserCorrelation() [3/3]

void URANIE::Sampler::TGaussianSampling::setUserCorrelation	(	URANIE::DataServer::TAttribute *	x,
		URANIE::DataServer::TAttribute *	y,
		Double_t	value
	)

Defines a correlation between two attributes.

Parameters

x	(URANIE::DataServer::TAttribute *) the first attribute
y	(URANIE::DataServer::TAttribute *) the second attribute
value	(Double_t) the correlation factor between x and y

◆ setUserVarianceCovarianceMatrix()

void URANIE::Sampler::TGaussianSampling::setUserVarianceCovarianceMatrix	(	Int_t	indx,
		Int_t	indy,
		double	value
	)

Referenced by ClassImp().

◆ splitMatrixVarCoVar()

void URANIE::Sampler::TGaussianSampling::splitMatrixVarCoVar	(	TMatrixD	matVarCovar,
		TVectorD	vecOfUnit,
		TMatrixD &	matVarCovar0,
		TMatrixD &	matVarCovar1,
		TMatrixD &	matVarCovar01
	)

private

The type of sampling (SRS, LHS)

Referenced by ClassImp().

◆ terminate()

virtual void URANIE::Sampler::TGaussianSampling::terminate ( )

virtual

The post-processing step.

Reimplemented from URANIE::Sampler::TSamplerStochastic.

Referenced by ClassImp().

Member Data Documentation

◆ _corrmatrix

TMatrixD URANIE::Sampler::TGaussianSampling::_corrmatrix

Referenced by ClassImp(), and getCorrelationMatrix().

◆ _covmatrix

TMatrixD URANIE::Sampler::TGaussianSampling::_covmatrix

The correlation matrix.

Referenced by ClassImp(), and getCovarianceMatrix().

◆ _ntype

EType URANIE::Sampler::TGaussianSampling::_ntype

The covariance matrix.

Referenced by ClassImp().

◆ _sIteratorNameNi

TString URANIE::Sampler::TGaussianSampling::_sIteratorNameNi

private

The specific iterator attribute for NI integer.

Referenced by ClassImp(), getIteratorNameNi(), and setIteratorNameNi().

◆ _sIteratorNameNo

TString URANIE::Sampler::TGaussianSampling::_sIteratorNameNo

private

The specific iterator attribute for NO integer.

Referenced by ClassImp(), getIteratorNameNo(), and setIteratorNameNo().

Documentation / Developer's manual

Available modules

Public Types

Public Member Functions

Public Attributes

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Enumeration Documentation

◆ EType

Constructor & Destructor Documentation

◆ TGaussianSampling()

◆ ~TGaussianSampling()

Member Function Documentation

◆ generateConditionalSample()

◆ generatenStandardPoints()

◆ generateSample()

◆ getCorrelationMatrix()

◆ getCovarianceMatrix()

◆ getIteratorNameNi()

◆ getIteratorNameNo()

◆ init()

◆ printLog()

◆ setFixedSumCovariance()

◆ setIteratorNameNi()

◆ setIteratorNameNo()

◆ setUserCorrelation() [1/3]

◆ setUserCorrelation() [2/3]

◆ setUserCorrelation() [3/3]

◆ setUserVarianceCovarianceMatrix()

◆ splitMatrixVarCoVar()

◆ terminate()

Member Data Documentation

◆ _corrmatrix

◆ _covmatrix

◆ _ntype

◆ _sIteratorNameNi

◆ _sIteratorNameNo