Description of the class TMaxiMinLHS. More...

#include <TMaxiMinLHS.h>

Inheritance diagram for URANIE::Sampler::TMaxiMinLHS:

[legend]

Collaboration diagram for URANIE::Sampler::TMaxiMinLHS:

[legend]

Public Member Functions
Constructor and Destructor
	TMaxiMinLHS (URANIE::DataServer::TDataServer *tds, int sampleSize, double sainitTemp, double saCoeff, int saN, int sainnerN)
	Creates a TMaxiMinLHS object from a TDataServer, definig the SA properties

virtual	~TMaxiMinLHS ()
	Default destructor.

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

void	setSeed (Int_t ind=0)
	Set the random generator seed.

void	transformMatrix (TMatrixD &RefMat, bool toProbaSpace)
	transform the sample

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

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

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

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.

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

void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

Public Attributes
main parameters
double	_SAinitTemp
	Initial temperature of the simulated Annealing algorithm for maximin lhs.

double	_SACoeff
	Temperature coefficient of the simulated Annealing algorithm for maximin lhs.

unsigned int	_SAN
	Number of iteration the simulated Annealing algorithm for maximin lhs.

unsigned int	_SAinnerN
	Inner Number of iteration before changing the temperature of the simulated Annealing algorithm for maximin lhs.

internal variables
bool	_bAlreadyFilled
	If the tree is already filled.

TMatrixD	_currentLHS

TMatrixD	_varyingLHS

TMatrixD	_currentDist

TMatrixD	_varyingDist

TMatrixD	_fp

TSampling *	_sampling
	Pointer to a TSampling object if tds is empty;.

unsigned int	_ns

unsigned int	_nx
	Local counter for number of samples and variables.

double *	_vi

double *	_vj
	arrays used throughout many calculations

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.

Method for the LHS_maximin producation
Double_t	getLPNorm (const TMatrixD &Dist_)
	Compute the Lp norm.

Double_t	getUpdatedLPNorm (Double_t LP_norm_, unsigned int nrowRand1_, unsigned int nrowRand2_)
	update the Lp norm

TMatrixD	maximinSA_LHS (const TMatrixD &lhs)
	Generate the new LHS.

void	setSAProperties (double &sainitTemp, double &saCoeff, int &saN, int &sainnerN)
	Set the Simulated Annealing algorithm properties.

void	Permutation (unsigned int &ncolRand, unsigned int &nrowRand1, unsigned int &nrowRand2)
	Permute the content of the lhs plan.

static Double_t	getMinDist (const TMatrixD &lhs)
	Compute the minimal distance between points.

static void	getDistMatrix (TMatrixD &Dist, const TMatrixD &lhs)
	Compute the distance matrix.

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 TMaxiMinLHS.

This class is used to produce a maximin lhs doe. It starts either with an empy tds or an already filled one, with the hypothesis that it has been filled with an LHS doe

An simulated annealing algorithm is implemented here to deal with the permutation. This implementation has be done by Guillaume Damblin.

Constructor & Destructor Documentation

◆ TMaxiMinLHS()

URANIE::Sampler::TMaxiMinLHS::TMaxiMinLHS	(	URANIE::DataServer::TDataServer *	tds,
		int	sampleSize,
		double	sainitTemp,
		double	saCoeff,
		int	saN,
		int	sainnerN
	)

Creates a TMaxiMinLHS object from a TDataServer, definig the SA properties

none of these is given, "lhs" is chosen.

Parameters

tds	(TDataServer *) pointer to the TDataServer.
sampleSize	(Int_t) number of values to generate for each random variable.
sainitTemp	(double): initial temperature for simulated annealing algo (see methodology for explanation)
saCoeff	(double): coefficient used to decrease temperature for simulated annealing algo (see methodology for explanation)
saN	(int): number of iterations in the outer loop for simulated annealing algo (see methodology for explanation)
sinneraN	(int): number of iterations in the inner loop for simulated annealing algo (see methodology for explanation)

Exceptions

UErrorExceptions if sampleSize <= 0

Referenced by ClassImp().

◆ ~TMaxiMinLHS()

virtual URANIE::Sampler::TMaxiMinLHS::~TMaxiMinLHS ( )

virtual

Default destructor.

Referenced by ClassImp().

Member Function Documentation

◆ createTuple()

void URANIE::Sampler::TMaxiMinLHS::createTuple ( )

virtual

Creates the TDSNtupleD of data with only the TStochasticAttributes.

Reimplemented from URANIE::Sampler::TSampler.

Referenced by ClassImp().

◆ fillOtherAttributes()

virtual void URANIE::Sampler::TMaxiMinLHS::fillOtherAttributes ( )

virtual

Fills the TAttributeFormula and totally correlated attributes.

Reimplemented from URANIE::Sampler::TSampler.

Referenced by ClassImp().

◆ generateSample()

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

virtual

Generates the sample.

Generates the random data and fills the TDataServer with it.

Parameters

option (Option_t *) option string (Default = "")

Implements URANIE::Sampler::TSampler.

Referenced by ClassImp().

◆ getDistMatrix()

static void URANIE::Sampler::TMaxiMinLHS::getDistMatrix	(	TMatrixD &	Dist,
		const TMatrixD &	lhs
	)

static

Compute the distance matrix.

Compute the distance matrix

Parameters

Dist	(TMatrixD) the distance matrix of the lhs plan under study
lhs	(TMatrixD) the lhs plan under study

Referenced by ClassImp().

◆ getLPNorm()

Double_t URANIE::Sampler::TMaxiMinLHS::getLPNorm ( const TMatrixD & Dist_ )

Compute the Lp norm.

Compute the Lp norm

Parameters

lhs	(TMatrixD) the distance matrix of the lhs plan under study

Referenced by ClassImp().

◆ getMinDist()

static Double_t URANIE::Sampler::TMaxiMinLHS::getMinDist ( const TMatrixD & lhs )

static

Compute the minimal distance between points.

Compute the minimal distance between points

Parameters

lhs	(TMatrixD) the lhs plan under study

Referenced by ClassImp().

◆ getUpdatedLPNorm()

Double_t URANIE::Sampler::TMaxiMinLHS::getUpdatedLPNorm	(	Double_t	LP_norm_,
		unsigned int	nrowRand1_,
		unsigned int	nrowRand2_
	)

update the Lp norm

Update the Lp norm

Parameters

lhs	(TMatrixD) the distance matrix of the lhs plan under study

Referenced by ClassImp().

◆ maximinSA_LHS()

TMatrixD URANIE::Sampler::TMaxiMinLHS::maximinSA_LHS ( const TMatrixD & lhs )

Generate the new LHS.

Generate a new lhs doe by doing all the permutation allowed by the Simulated Annealing algorithm. The method requires four parameters that shoud be set by setSAProperties()

Parameters

lhs	(TMatrixD) the lhs plan under study

Referenced by ClassImp().

◆ Permutation()

void URANIE::Sampler::TMaxiMinLHS::Permutation	(	unsigned int &	ncolRand,
		unsigned int &	nrowRand1,
		unsigned int &	nrowRand2
	)

Permute the content of the lhs plan.

Permute the content of the lhs plan

Parameters

lhs_new	(TMatrixD) the new lhs after permutation
lhs	(TMatrixD) the lhs plan under study

Referenced by ClassImp().

◆ printLog()

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

virtual

Prints the log.

Parameters

option (Option_t *) option string (Default = "")

Reimplemented from URANIE::Sampler::TSamplerStochastic.

Referenced by ClassImp().

◆ setSAProperties()

void URANIE::Sampler::TMaxiMinLHS::setSAProperties	(	double &	sainitTemp,
		double &	saCoeff,
		int &	saN,
		int &	sainnerN
	)

Set the Simulated Annealing algorithm properties.

Set the Simulated Annealing algorithm properties

Parameters

sainitTemp	(double) Initial temperature of the simulated Annealing algorithm for maximin lhs
saCoeff	(double) Temperature coefficient of the simulated Annealing algorithm for maximin lhs
saN	(double) Number of iteration of the simulated Annealing algorithm for maximin lhs
sainnerN	(double) Inner Number of iteration before changing the temperature of the simulated Annealing algorithm for maximin lhs

Referenced by ClassImp().

◆ setSeed()

void URANIE::Sampler::TMaxiMinLHS::setSeed ( Int_t ind = 0 )

Set the random generator seed.

Referenced by ClassImp().

◆ transformMatrix()

void URANIE::Sampler::TMaxiMinLHS::transformMatrix	(	TMatrixD &	RefMat,
		bool	toProbaSpace
	)

transform the sample

Tranform the sample from the probability space to the physical one depending on the boolean

Parameters

RefMat	: the reference TMatrixD containing the information
toProbaSpace	a boolean to state whether transformation should be done to the probability space or from it

Referenced by ClassImp().

Member Data Documentation

◆ _bAlreadyFilled

bool URANIE::Sampler::TMaxiMinLHS::_bAlreadyFilled

If the tree is already filled.

Referenced by ClassImp().

◆ _currentDist

TMatrixD URANIE::Sampler::TMaxiMinLHS::_currentDist

Referenced by ClassImp().

◆ _currentLHS

TMatrixD URANIE::Sampler::TMaxiMinLHS::_currentLHS

Referenced by ClassImp().

◆ _fp

TMatrixD URANIE::Sampler::TMaxiMinLHS::_fp

Referenced by ClassImp().

◆ _ns

unsigned int URANIE::Sampler::TMaxiMinLHS::_ns

Referenced by ClassImp().

◆ _nx

unsigned int URANIE::Sampler::TMaxiMinLHS::_nx

Local counter for number of samples and variables.

Referenced by ClassImp().

◆ _SACoeff

double URANIE::Sampler::TMaxiMinLHS::_SACoeff

Temperature coefficient of the simulated Annealing algorithm for maximin lhs.

Referenced by ClassImp().

◆ _SAinitTemp

double URANIE::Sampler::TMaxiMinLHS::_SAinitTemp

Initial temperature of the simulated Annealing algorithm for maximin lhs.

Referenced by ClassImp().

◆ _SAinnerN

unsigned int URANIE::Sampler::TMaxiMinLHS::_SAinnerN

Inner Number of iteration before changing the temperature of the simulated Annealing algorithm for maximin lhs.

Referenced by ClassImp().

◆ _sampling

TSampling* URANIE::Sampler::TMaxiMinLHS::_sampling

Pointer to a TSampling object if tds is empty;.

Referenced by ClassImp().

◆ _SAN

unsigned int URANIE::Sampler::TMaxiMinLHS::_SAN

Number of iteration the simulated Annealing algorithm for maximin lhs.

Referenced by ClassImp().

◆ _varyingDist

TMatrixD URANIE::Sampler::TMaxiMinLHS::_varyingDist

Referenced by ClassImp().

◆ _varyingLHS

TMatrixD URANIE::Sampler::TMaxiMinLHS::_varyingLHS

Referenced by ClassImp().

◆ _vi

double* URANIE::Sampler::TMaxiMinLHS::_vi

Referenced by ClassImp().

◆ _vj

double * URANIE::Sampler::TMaxiMinLHS::_vj

arrays used throughout many calculations

Referenced by ClassImp().

Documentation / Manuel développeur

Modules disponibles

Public Member Functions

Public Attributes

Method for the LHS_maximin producation

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ TMaxiMinLHS()

◆ ~TMaxiMinLHS()

Member Function Documentation

◆ createTuple()

◆ fillOtherAttributes()

◆ generateSample()

◆ getDistMatrix()

◆ getLPNorm()

◆ getMinDist()

◆ getUpdatedLPNorm()

◆ maximinSA_LHS()

◆ Permutation()

◆ printLog()

◆ setSAProperties()

◆ setSeed()

◆ transformMatrix()

Member Data Documentation

◆ _bAlreadyFilled

◆ _currentDist

◆ _currentLHS

◆ _fp

◆ _ns

◆ _nx

◆ _SACoeff

◆ _SAinitTemp

◆ _SAinnerN

◆ _sampling

◆ _SAN

◆ _varyingDist

◆ _varyingLHS

◆ _vi

◆ _vj