Description of the class TWeightedLSDistanceFunction
The distance is estimated as. More...

#include <TStandardDistanceFunction.h>

Inheritance diagram for URANIE::Calibration::TWeightedLSDistanceFunction:

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Collaboration diagram for URANIE::Calibration::TWeightedLSDistanceFunction:

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Public Member Functions
Constructor and Destructor
	TWeightedLSDistanceFunction (URANIE::DataServer::TDataServer tdsEval, URANIE::DataServer::TDataServer tdsObs, const char reference, const char output, const char *weight="")
	Default constructor.

virtual	~TWeightedLSDistanceFunction ()
	Default destructor.

Estimate
Int_t	localeval (Double_t *res)

Public Member Functions inherited from URANIE::Calibration::TDistanceFunction
	TDistanceFunction (URANIE::DataServer::TDataServer tdsEval, URANIE::DataServer::TDataServer tdsObs, const char reference, const char output, const char *weight="")
	Standard constructor.

virtual	~TDistanceFunction ()
	Default destructor.

void	fillInOutAtt (URANIE::DataServer::TDataServer *tdsPar)
	Internal initialisation method.

void	initParameters (URANIE::DataServer::TDataServer tdsPar, URANIE::Relauncher::TRun run)

void	initParameters (URANIE::DataServer::TDataServer tdsPar, const char funcName, const char varexpinput, const char varexpoutput)

void	initParameters (URANIE::DataServer::TDataServer tdsPar, void(fcn)(Double_t , Double_t ), const char varexpinput, const char varexpoutput)

void	initParameters (URANIE::DataServer::TDataServer tdsPar, URANIE::Launcher::TCode Code)

Int_t	eval (Double_t theta, Double_t res, int=0)
	Official method inherited from TSimpleEval, it is the main method of a DF It calls three methods in a row : runConfiguration, getData and localeval.

void	runConfiguration (double *theta)
	Runs the code provided on the reference input to get estimations.

vector< vector< double > >	getObservationVector ()
	Return the observation vector of vector.

TMatrixD	getObservationMatrix ()
	Return the observation matrix.

void	setSaveAllEvaluations (bool value)
	Choose whether every evaluations should be kept (might slown down the process)

void	changeLauncher (TString tlcName)
	Change the code launcher.

void	changeCodeLauncherOpt (TString opt)
	Change the option of the code launcher.

void	dumpAllDataservers ()

void	keepParametersValue ()

void	dumpDetails ()

void	addCodeLauncherOpt (TString opt)
	add options to the already of the code launcher

void	setUseMatrix (bool value)
	Choose whether vector of vector or matrix should be used in the distance function localeval method.

void	setVarWeights (int nwei, double *wei)
	Set a weight to ponderate variable when _nVar >1.

void	setVarWeights (vector< double > wei)
	Set a weight to ponderate variable when _nVar >1.

void	setObservationCovarianceMatrix (TMatrixD &mat)
	set the observatiton covariance matrix

bool	getSaveAllEvaluation ()
	Check the value of the saveAll option.

bool	getUseMatrix ()
	Check the value of the useMatrix option.

void	setGaussianRandomNoise (const char *stdname)
	Set Normal random noise for an output variable.

void	setSeed (UInt_t nval)
	Set the seed of the random generator if one is used.

void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

virtual void	printLog (Option_t *option="")

Additional Inherited Members
Public Attributes inherited from URANIE::Calibration::TDistanceFunction
URANIE::DataServer::TDataServer *	_tdsEval
	pointer toward the evaluation dataserver

URANIE::DataServer::TDataServer *	_tdsObs
	pointer toward the reference dataserver

URANIE::DataServer::TDSNtupleD *	_allData
	pointer toward the reference dataserver

Protected Member Functions inherited from URANIE::Calibration::TDistanceFunction
void	getData (bool reference=false)
	Get the data either the reference one, or the estimated one for a given parameter configuration.

Protected Attributes inherited from URANIE::Calibration::TDistanceFunction
vector< string >	_vrefName
	Vector of reference name.

vector< string >	_voutName
	Vector of output name.

vector< string >	_vparName
	Vector of parameters name.

vector< vector< double > >	_yObs
	Vector of observations outputs (size: _nVar < _nObs >)

vector< vector< double > >	_yExp
	Vector of tested values (size: _nVar <_nObs>)

TMatrixD	_mObsCovMat
	Observation Covariance matrix.

TMatrixD	_myObs
	Vector of observations outputs (size: _nVar < _nObs >)

TMatrixD	_myExp
	Vector of tested values (size: _nVar <_nObs>)

unsigned int	_nObs
	Number of observations.

unsigned int	_nVar
	Number of variables;.

unsigned int	_nPar
	Number of Parameters;.

vector< double >	_vParValues

vector< double >	_varWeights
	Vector of variable weights (size: _nVar)

vector< vector< double > >	_obsWeights
	Vector of observation weights outputs (size: _nObs)

URANIE::Relauncher::TRun *	_run

TString	_tlcType

TString	_codeLauncherOpt
	Option for code launcher.

URANIE::Launcher::TCode *	_code

TString	_funcName

void(*	_pFunction )(double , double )
	Function pointer.

TString	_varexpinput

TString	_varexpoutput

bool	_baprioriSet
	Whether apriori is set to evaluator.

bool	_bsaveAll
	Whether all evaluations should be saved, not only a priori and a posteriori.

bool	_buseMatrix
	Use matrix instead of vectors.

bool	_blog
	Boolean to decide if the log information is shown or not.

bool	_dumpAllTds
	dump all tested dataserver;

bool	_keepParValue
	Keep the parameters values in runner case.

bool	_bdumpDetails
	Dump the details in Distance Computation.

int	_icalc
	Calcul iterator;.

unsigned int	_ivar

unsigned int	_iobs

TList *	_listOfParameters
	list of parameters

TRandom3 *	_rand
	The random generator.

int	_nSeed
	The seed of the random generator.

TMatrixD	_mSigma
	Vector of variance of errors.

bool	_boolNoise
	Whether noise is add to the output.

Detailed Description

Description of the class TWeightedLSDistanceFunction
The distance is estimated as.

$Dist = \sum_{i=1}^{n_{Var}} \alpha_i \times \sqrt{ \sum_{j=1}^{n_{Obs}} \beta_{j} (\mathbf{y^{j}_{i,Obs}} - \mathbf{y^{j}_{i,Est}})^{2}}$

where

$n_{Var}$ and $n_{Obs}$ are respectively the number of output variables and the number of events in the reference dataset
$\mathbf{y^{j}_{i,Obs}}$ and $\mathbf{y^{j}_{i,Est}}$ are respectively the reference and estimated value of output for event
$\alpha_i$ is a weight that might be used to ponderate one variable with respect to the others
$\beta_j$ is a weight that might be used to ponderate one event with respect to the others (uncertainty for instance)

Constructor & Destructor Documentation

◆ TWeightedLSDistanceFunction()

URANIE::Calibration::TWeightedLSDistanceFunction::TWeightedLSDistanceFunction	(	URANIE::DataServer::TDataServer *	tdsEval,
		URANIE::DataServer::TDataServer *	tdsObs,
		const char *	reference,
		const char *	output,
		const char *	weight = `""`
	)

Default constructor.

the evaluation dataserver
the reference dataserver
the list of variable that are the output reference (_nVar in total)
the outputs of the code (_nVar as well)
the observation weight (uncertainty for instance)

◆ ~TWeightedLSDistanceFunction()

virtual URANIE::Calibration::TWeightedLSDistanceFunction::~TWeightedLSDistanceFunction ( )

virtual

Default destructor.

Member Function Documentation

◆ localeval()

Int_t URANIE::Calibration::TWeightedLSDistanceFunction::localeval ( Double_t * res )

virtual

Evaluate the distance function

Implements URANIE::Calibration::TDistanceFunction.

Documentation / Developer's manual

Available modules