#include <TMelange.h>

Collaboration diagram for TMelange:

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Public Member Functions
	TMelange (Int_t n)
	Generator of random number.

	~TMelange ()

void	_printlog ()

void	setTaille (Int_t n)

Int_t	getTaille ()

Int_t	getTailleMelange ()

Double_t	eval (TVectorD X)

Double_t	eval (Double_t x)

Int_t	getDimMelange ()

TNtupleD *	getTuple ()

TVectorD	getSeuil ()

Double_t	getProb (Int_t i)

TVectorD	getMean (Int_t i)

TVectorD	getMean ()

TVectorD	getVariance ()

TVectorD	getSigma ()

TMatrixD	getMatEcartTypeCorrelation (Int_t i)

TMatrixD	getMatEcartTypeCorrelation ()

TVectorD	getSigma (Int_t i)

TMatrixD	getMatCorrelation (Int_t i)

TMatrixD	getMatCovariance (Int_t i)

TMatrixD	getMatCovariance ()

TMatrixD	getMatCorrelation ()

Double_t	getObs ()

void	simulationMelange ()
	Classic simulation of a mixing.

void	addLoi (TVectorD moy, TMatrixD ecartTypeCorr, Double_t proba)

void	removeLoi (Int_t i)

void	addTuple (TNtupleD *t)

TVectorD	LoiDeZ (TVectorD X)
	Computes the conditional repartition function for X belonging to a mode.

TVectorD	discrimination (TVectorD X)
	Computes for each mode, the 'a posteriori' probability that X belongs to the mode. Enables to undertake a discriminant analysis.

void	reset ()

void	remplacer (Int_t i, TVectorD M, TMatrixD A)
	Replaces the component i by a component N(M,A)

void	afficher ()
	Prints the characteristics of mixing modes.

Private Attributes
TNormale *	f

TList *	lLoi
	Normal law used for intermediary calculations.

Int_t	_tailleMelange
	Chained list used to go through the mixing.

Int_t	_taille

Int_t	_dimMelange
	Sample size to be simulated.

TNtupleD *	_nt

TRandom *	_rand
	Tuple in which the sample is stocked.

Constructor & Destructor Documentation

◆ TMelange()

TMelange::TMelange ( Int_t n )

Generator of random number.

References _dimMelange, _nt, _rand, _taille, _tailleMelange, f, and lLoi.

◆ ~TMelange()

TMelange::~TMelange ( )

References lLoi.

Member Function Documentation

◆ _printlog()

void TMelange::_printlog ( )

References _taille, and _tailleMelange.

◆ addLoi()

void TMelange::addLoi	(	TVectorD	moy,
		TMatrixD	ecartTypeCorr,
		Double_t	proba
	)

References _dimMelange, _tailleMelange, TDistribution::getDim(), lLoi, and TDistribution::setProb().

Referenced by TMCMC::NKCSampling().

◆ addTuple()

void TMelange::addTuple ( TNtupleD * t )

References _nt.

Referenced by simulationMelange().

◆ afficher()

void TMelange::afficher ( )

Prints the characteristics of mixing modes.

References getMatEcartTypeCorrelation(), getMean(), and getTailleMelange().

◆ discrimination()

TVectorD TMelange::discrimination ( TVectorD X )

Computes for each mode, the 'a posteriori' probability that X belongs to the mode. Enables to undertake a discriminant analysis.

References _tailleMelange, eval(), TNormale::eval(), f, getMatEcartTypeCorrelation(), getMean(), getProb(), TDistribution::setMatEcartTypeCorrelation(), and TDistribution::setMean().

Referenced by TMCMC::gibbsSampling(), TMCMC::randomWalkMetropolisSampling(), and TMCMC::varTimeMetropolisSampling().

◆ eval() [1/2]

Double_t TMelange::eval ( Double_t x )

References _tailleMelange, TNormale::eval(), f, getMatEcartTypeCorrelation(), getMean(), getProb(), TDistribution::setMatEcartTypeCorrelation(), and TDistribution::setMean().

◆ eval() [2/2]

Double_t TMelange::eval ( TVectorD X )

References _tailleMelange, TNormale::eval(), f, getMatEcartTypeCorrelation(), getMean(), getProb(), TDistribution::setMatEcartTypeCorrelation(), and TDistribution::setMean().

Referenced by discrimination(), LoiDeZ(), TMCMC::NKCSampling(), TMCMC::randomWalkMetropolisSampling(), and TMCMC::varTimeMetropolisSampling().

◆ getDimMelange()

Int_t TMelange::getDimMelange ( )

inline

References _dimMelange.

Referenced by TMCMC::Draw(), TMCMC::gibbsSampling(), TMCMC::NKCSampling(), TMCMC::NKCSampling2(), TMCMC::randomWalkMetropolisSampling(), and TMCMC::varTimeMetropolisSampling().

◆ getMatCorrelation() [1/2]

TMatrixD TMelange::getMatCorrelation ( )

References _dimMelange, getMatCovariance(), and getSigma().

Referenced by getMatEcartTypeCorrelation().

◆ getMatCorrelation() [2/2]

TMatrixD TMelange::getMatCorrelation ( Int_t i )

References _tailleMelange, TDistribution::getMatCorrelation(), and lLoi.

◆ getMatCovariance() [1/2]

TMatrixD TMelange::getMatCovariance ( )

References _dimMelange, _tailleMelange, getMatCovariance(), and getProb().

Referenced by getMatCorrelation(), getMatCovariance(), and getVariance().

◆ getMatCovariance() [2/2]

TMatrixD TMelange::getMatCovariance ( Int_t i )

References _tailleMelange, TDistribution::getMatCovariance(), and lLoi.

◆ getMatEcartTypeCorrelation() [1/2]

TMatrixD TMelange::getMatEcartTypeCorrelation ( )

References _dimMelange, getMatCorrelation(), and getSigma().

Referenced by afficher(), discrimination(), eval(), eval(), and LoiDeZ().

◆ getMatEcartTypeCorrelation() [2/2]

TMatrixD TMelange::getMatEcartTypeCorrelation ( Int_t i )

References _tailleMelange, TDistribution::getMatEcartTypeCorrelation(), and lLoi.

Referenced by TMCMC::Draw(), TMCMC::gibbsSampling(), TMCMC::NKCSampling(), TMCMC::NKCSampling2(), and TMCMC::randomWalkMetropolisSampling().

◆ getMean() [1/2]

TVectorD TMelange::getMean ( )

References _dimMelange, _tailleMelange, getMean(), and getProb().

Referenced by afficher(), discrimination(), eval(), eval(), getMean(), and LoiDeZ().

◆ getMean() [2/2]

TVectorD TMelange::getMean ( Int_t i )

References _tailleMelange, TDistribution::getMean(), and lLoi.

Referenced by TMCMC::Draw(), TMCMC::gibbsSampling(), and TMCMC::NKCSampling2().

◆ getObs()

Double_t TMelange::getObs ( )

References _rand, TNormale::getObs(), getSeuil(), and lLoi.

◆ getProb()

Double_t TMelange::getProb ( Int_t i )

References _tailleMelange, TDistribution::getProb(), and lLoi.

Referenced by discrimination(), eval(), eval(), getMatCovariance(), getMean(), LoiDeZ(), TMCMC::NKCSampling2(), and remplacer().

◆ getSeuil()

TVectorD TMelange::getSeuil ( )

References _tailleMelange, TDistribution::getProb(), and lLoi.

Referenced by getObs(), and simulationMelange().

◆ getSigma() [1/2]

TVectorD TMelange::getSigma ( )

References _dimMelange, and getVariance().

Referenced by TMCMC::Draw(), getMatCorrelation(), getMatEcartTypeCorrelation(), and TMCMC::varTimeMetropolisSampling().

◆ getSigma() [2/2]

TVectorD TMelange::getSigma ( Int_t i )

References _tailleMelange, TDistribution::getSigma(), and lLoi.

◆ getTaille()

Int_t TMelange::getTaille ( )

inline

References _taille.

◆ getTailleMelange()

Int_t TMelange::getTailleMelange ( )

inline

References _tailleMelange.

Referenced by afficher(), TMCMC::Draw(), TMCMC::gibbsSampling(), TMCMC::NKCSampling2(), TMCMC::randomWalkMetropolisSampling(), and TMCMC::varTimeMetropolisSampling().

◆ getTuple()

TNtupleD * TMelange::getTuple ( )

inline

References _nt.

Referenced by TMCMC::gibbsSampling(), TMCMC::NKCSampling(), TMCMC::NKCSampling2(), TMCMC::randomWalkMetropolisSampling(), and TMCMC::varTimeMetropolisSampling().

◆ getVariance()

TVectorD TMelange::getVariance ( )

References getMatCovariance().

Referenced by getSigma().

◆ LoiDeZ()

TVectorD TMelange::LoiDeZ ( TVectorD X )

Computes the conditional repartition function for X belonging to a mode.

References _tailleMelange, eval(), TNormale::eval(), f, getMatEcartTypeCorrelation(), getMean(), getProb(), TDistribution::setMatEcartTypeCorrelation(), and TDistribution::setMean().

Referenced by TMCMC::gibbsSampling().

◆ removeLoi()

void TMelange::removeLoi ( Int_t i )

References _tailleMelange, and lLoi.

◆ remplacer()

void TMelange::remplacer	(	Int_t	i,
		TVectorD	M,
		TMatrixD	A
	)

Replaces the component i by a component N(M,A)

References _dimMelange, getProb(), lLoi, and TDistribution::setProb().

◆ reset()

void TMelange::reset ( )

References _tailleMelange, and lLoi.

Referenced by TMCMC::NKCSampling().

◆ setTaille()

void TMelange::setTaille ( Int_t n )

inline

References _taille.

◆ simulationMelange()

void TMelange::simulationMelange ( )

Classic simulation of a mixing.

In a firt step, the classic simulation consists in selecting ramdomly an integer, then choosing a sample corresponding to the law which contains the index. The law is recovered from thresholds $0 < s_{1} < \cdots < s_{N} = 1.0$ . For each law, we count the number of times that an integer $U \in [0, 1]$ is located betweeen $s_{l} < u < s_{l+1}$ . Finally, for each law , a $n_{l}$ sample is chosen, with the constraint that the sum $\sum_{l}{n_{l}}$ is equal to the desired size.