Description of the class TBasicSampling. More...

#include <TBasicSampling.h>

Inheritance diagram for URANIE::Sampler::TBasicSampling:

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Collaboration diagram for URANIE::Sampler::TBasicSampling:

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Public Types
enum	EType { kLHS , kSRS , kUnknown }

Public Member Functions
Constructor and Destructor
	TBasicSampling (URANIE::DataServer::TDataServer tds, Option_t option="lhs", Int_t sampleSize=1000)

virtual	~TBasicSampling ()
	Default destructor.

Correlations
void	setTolerance (Double_t tolerance)
	Set the precision of similarity

void	setCorrelationMatrix (TMatrixD corrMat)
	Set the correlation matrix.

void	setUserCorrelation (TString attName1, TString attName2, Double_t corrValue)
	Set user correlation for two attributes.

void	findSimilar ()
	Locate totally correlated attributes.

Generate the sample
void	generateCorrSample (Option_t *option="")
	Generates correlated data following a user defined correlation matrix.

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

virtual void	fillOtherAttributes ()
	Fills the TAttributeFormula and totally correlated attributes.

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.

void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

Public Attributes
LHSInput *	_fLhsInput

EType	_ntype
	The type of sampling (SRS, LHS)

TMatrixD	userCorrMat
	correlation matrix defined by the user

TMatrixD	_corrMat
	correlation matrix used internally

TList *	_listOfSimilar
	list of attributes totally correlated with other attributes.

TList *	_listOfFormulas
	list of formulas linking two totally correlated attributes.

Double_t	_simTolerance
	precision to decide if a correlation is equal to 1.0 (or -1.0).

Bool_t	bApplyUserCorrelation
	if true, correlated data are produced.

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.

Additional Inherited Members
Protected Member Functions inherited from URANIE::Sampler::TSamplerStochastic
virtual void	init ()
	The preprocessing step.

virtual void	terminate ()
	The post-processing step.

Protected Attributes inherited from URANIE::Sampler::TSamplerStochastic
Int_t	_nSeed

Detailed Description

Description of the class TBasicSampling.

This class defines a TSamplerStochastic object for fast random data generation.

This is a very simple implementation of the random sampling. It will generate a set of n values for each random variable defined in the given TDataServer object.

The main differences with the TSampling class are:

No correlation can be imposed between the variables.
The strict independence between the generated variables is not garanteed.
There is no lower limit in the number of random samples that can be generated.
The generation is much faster when dealing with a large number of attributes.

On any other aspects, the two classes have the same properties.

In short, if you have a large number of independent random variables to sample in a short time, you should use this class. Otherwise, use TSampling.

Member Enumeration Documentation

◆ EType

enum URANIE::Sampler::TBasicSampling::EType

Enumerator
kLHS
kSRS
kUnknown

Constructor & Destructor Documentation

◆ TBasicSampling()

URANIE::Sampler::TBasicSampling::TBasicSampling	(	URANIE::DataServer::TDataServer *	tds,
		Option_t *	option = `"lhs"`,
		Int_t	sampleSize = `1000`
	)

Creates a TBasicSampling object from a TDataServer, a sampling method and the size of the sample to generate

The random variables to sample are the attributes of the TDataServer. These attributes must derive from the TStochasticAttribute class. The sampling method, given as an option, can be either "lhs" (Latin Hypercube Sampling) or "srs" (Simple Random Sampling). If none of these is given, "lhs" is chosen.

Parameters

tds	(TDataServer *) pointer to the TDataServer.
option	(Option_t *) option string containing the sampling method (Default = "lhs").
sampleSize	(Int_t) number of values to generate for each random variable (Default = 1000).

Exceptions

UErrorExceptions if sampleSize <= 0

References URANIE::Sampler::TSampler::_blog, _listOfFormulas, _listOfSimilar, _ntype, kLHS, kSRS, and URANIE::Sampler::TSampler::setMethodName().

◆ ~TBasicSampling()

URANIE::Sampler::TBasicSampling::~TBasicSampling ( )

virtual

Default destructor.

References URANIE::Sampler::TSampler::_blog, _fLhsInput, _listOfFormulas, and _listOfSimilar.

Member Function Documentation

◆ fillOtherAttributes()

void URANIE::Sampler::TBasicSampling::fillOtherAttributes ( )

virtual

Fills the TAttributeFormula and totally correlated attributes.

Reimplemented from URANIE::Sampler::TSampler.

References _listOfFormulas, _listOfSimilar, URANIE::Sampler::TSampler::_tds, and URANIE::Sampler::TSampler::fillOtherAttributes().

Referenced by generateCorrSample(), and generateSample().

◆ findSimilar()

void URANIE::Sampler::TBasicSampling::findSimilar ( )

Locate totally correlated attributes.

References _corrMat, _listOfFormulas, _listOfSimilar, URANIE::Sampler::TSampler::_lstOfAttributesToSample, URANIE::Sampler::TSampler::_nX, _simTolerance, and userCorrMat.

Referenced by generateCorrSample().

◆ generateCorrSample()

void URANIE::Sampler::TBasicSampling::generateCorrSample ( Option_t * option = "" )

Generates correlated data following a user defined correlation matrix.

This function uses the "normal copula" method (cf. S. Wang. "Aggregation of correlated risk portfolios: Models and algorithms". Proceedings of the Casualty Actuarial Society, LXXXV:848-939, 1998) to create correlated data. This method is equivalent to the Iman & Connover one, but is faster and easier to implement. However, it does not preserve the "LHS" distribution, and might perform worse than I&C on the reconstruction of the theoretical correlation.

In addition, this function automatically detects totally correlated variables, and handle them correctly.

References URANIE::Sampler::TSampler::_blog, _corrMat, URANIE::Sampler::TSampler::_lstOfAttributesToSample, URANIE::Sampler::TSampler::_nS, URANIE::Sampler::TSampler::_ntsample, _ntype, URANIE::Sampler::TSampler::_nX, URANIE::Sampler::TSampler::_tds, URANIE::Sampler::TSampler::createTuple(), fillOtherAttributes(), findSimilar(), kLHS, kSRS, URANIE::Sampler::TSampler::parseOption(), and shuffleInteger().