Description of the class TFiniteDifferences. More...

#include <TFiniteDifferences.h>

Inheritance diagram for URANIE::Sensitivity::TFiniteDifferences:

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Collaboration diagram for URANIE::Sensitivity::TFiniteDifferences:

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Public Member Functions
Constructor and Destructor
	TFiniteDifferences ()
	Default constructor.

	TFiniteDifferences (TDataServer tdsNominal, void(fcn)(Double_t , Double_t ), TString sensitiveAtt, TString outputAtt, TString samplingOption="steps=1%")
	Constructor with the TDataServer and function.

	TFiniteDifferences (TDataServer tdsNominal, const char fcn, TString sensitiveAtt, TString outputAtt, TString samplingOption)
	Constructor with the TDataServer and function name.

	TFiniteDifferences (TDataServer tdsNominal, URANIE::Launcher::TCode fcode, TString sensitiveAtt="", TString samplingOption="steps=1%")
	Constructor with the TDataServer and code.

	TFiniteDifferences (TDataServer tdsNominal, URANIE::Relauncher::TRun run, TString sensitiveAtt="", TString samplingOption="steps=1%")
	Constructor with the TDataServer and TRun.

	TFiniteDifferences (TDataServer *tdsNominal, TString inputAtt, TString outputAtt, TString sensitiveAtt=TString(""))
	Constructor with a loaded data server.

virtual	~TFiniteDifferences ()
	Default destructor.

	TFiniteDifferences ()
	Default constructor.

	TFiniteDifferences (TDataServer tdsNominal, void fcn, TString sensitiveAtt, TString ranges, TString inputAtt, TString outputAtt)
	Constructor with the TDataServer and function.

	TFiniteDifferences (TDataServer tdsNominal, URANIE::Launcher::TCode fcode, TString sensitiveAtt, TString ranges)
	Constructor with the TDataServer and code.

	TFiniteDifferences (TDataServer *tdsNominal, TString sensitiveAtt, TString inputAtt, TString outputAtt)
	Constructor with a loaded data server.

virtual	~TFiniteDifferences ()
	Default destructor.

Setting and Getting attributs
TMatrixD	getSensitivityMatrix ()
	Returns the sensitivity matrix.

TDataServer *	getDataServer ()
	Returns a pointer to the internal data server.

TMatrixD	getSensitivityMatrix ()
	Returns the sensitivity matrix.

TDataServer *	getOATDataServer ()
	Returns a pointer to the internal data server.

Generation of the sample
virtual void	generateSample (Option_t *option="")
	Generates the OAT sampling.

virtual void	generateSample (Option_t *option="")
	Generates the OAT sampling.

Compute the indexes
void	evaluateIndexes (Option_t *option="")
	Compute the sensitivity matrix.

void	preTreatment ()
	Resize the matrix results at the very beginning of the algo to the number of output/input.

void	computeIndexes (Option_t *option="")
	Compute the sensitivity matrix.

Printing Log
virtual void	printLog ()

virtual void	printLog ()

Public Member Functions inherited from URANIE::Sensitivity::TSensitivity
	TSensitivity ()
	Default constructor.

	TSensitivity (URANIE::DataServer::TDataServer tds, const char fcn, Int_t ns, const char varexpinput="", const char varexpoutput="", Option_t *option="")
	Default constructor with the name of a function.

	TSensitivity (URANIE::DataServer::TDataServer tds, void(fcn)(Double_t , Double_t ), const char varexpinput, const char varexpoutput, Int_t ns, Option_t *option="")

	TSensitivity (URANIE::DataServer::TDataServer tds, const char varexpinput, const char varexpoutput, Option_t option="")
	Default constructor.

	TSensitivity (URANIE::DataServer::TDataServer tds, URANIE::Launcher::TCode code, Int_t ns, Option_t *option="")
	Default constructor with TCode arg.

	TSensitivity (URANIE::DataServer::TDataServer tds, URANIE::Relauncher::TRun run, Int_t ns, Option_t *option="")
	Default constructor with TRun arg.

virtual	~TSensitivity ()
	Default destructor.

Int_t	getID ()

virtual TTree *	getResultTuple (bool commonresulttuple=true)
	Get the result ntuple (default parameter unused but for Morris method)

double	getValue (const char sorder="", const char sinputname="", const char *sselect="")

vector< int > *	getAttributeElements (string str)

void	setFunction (void(fct)(Double_t , Double_t *), Int_t nx=-1, Int_t ny=1)

TString	getFunctionName ()

void	setSeed (UInt_t nval)

UInt_t	getSeed ()

virtual void	setMethodName (const char *str)
	Set the Method name.

const char *	getMethodName ()
	Get the method name.

Bool_t	getNoIntermediateSaved ()
	Get the noIntermediateSaved flag.

const char *	getIteratorName ()
	Get the name of the iterator attribut of the method.

void	setSensitivityIteratorName (const char *str)
	Set the iterator name devoted to compute sensitivity indexes.

void	setTimeName (TString sname)
	Set the name of the time attribute (only one)

TString	getTimeName ()
	Get the name of the time attribute.

virtual void	setDrawProgressBar (Bool_t bbool=kTRUE)
	Set the "draw progress bar" flag.

void	setUsingErrors (bool thebool)
	Set the "using error results anyway" option.

Bool_t	getDrawProgressBar ()
	Get the clean flag.

Bool_t	isInputCorrelated ()

TMatrixD	getMatrixInputCorrelation ()

Int_t	getNInput ()
	Get the number of input attributes.

Int_t	getNOutput ()
	Get the number of output attributes.

void	setInputCorrelationMatrix (TMatrixD Corr)

virtual void	parseOption (Option_t *option="")
	Read the possible options.

void	checkOutputRequested (string attlist, bool fromoption=false)
	Check the output list requested by the user.

void	computeIndexes (Option_t *option="")
	Compute the Sensitivity Indexes.

void	fillIndex (const char sinputname, const char sorder, Double_t dval, const char *algo="", Double_t dvalCILower=-1.0, Double_t dvalCIUpper=-1.0)
	Method to fill in the tree the value of Sensitivity indexes for an input attribute.

virtual void	createTuple (Option_t *option="")

virtual void	drawIndexes (TString sTitre, const char select="", Option_t option="")
	Draws the indexes.

virtual void	setLog ()

void	unsetLog ()

void	changeLog ()

Bool_t	getLog ()

virtual void	printLog (Option_t *option="")

void	setNLauncher (ELauncher codeLauncher)

Protected Attributes
TMatrixD	_sMatrix
	The sensitivity matrix.

TString	_sSensitiveAtt
	The list of sensitive attributes.

TString	_sSamplingOption
	the options for the OAT sampling

Int_t	_nbIn
	the number of sensitive inputs

Int_t	_nbAux
	the number of non sensitive inputs

Protected Attributes inherited from URANIE::Sensitivity::TSensitivity
Char_t	_sOutputAttribute [MAXLENGTHSTRING]
	The name of the output attribute.

Char_t	_sInputAttribute [MAXLENGTHSTRING]
	The name of the input attribute.

Char_t	_sOrder [MAXLENGTHSTRING]
	The order of sensitivity indexes.

Char_t	_sMethod [MAXLENGTHSTRING]
	The name of the method.

Char_t	_sAlgorithm [MAXLENGTHSTRING]
	The name of the algorithm to compute the index.

Double_t	_valSobolCrt
	The value of sensitivity indexes.

Double_t	_valSobolCILower
	The value of lower Condidence Interval (95)

Double_t	_valSobolCIUpper
	The value of upper Condidence Interval (95)

TMatrixD	_minputCorr
	Input correlation matrix if sample needs to be correlated.

Bool_t	_bisInputCorrelated
	State whether the input correlation matrix is set.

Bool_t	_bgoingThroughError
	State whether the error must not block the computation.

Private Attributes
URANIE::DataServer::TDataServer *	_tdsOAT
	a pointer to the data server containing the OAT sampling

TObjArray *	_lRanges
	the list of ranges of the sensitives attributes

TObjArray *	_lSensitiveAtt
	The list of sensitive attributes.

TObjArray *	_lInputs
	the full list of input variables

TObjArray *	_lOutputs
	the full list of outputs

Bool_t	_generateData
	if TRUE, one need to generate the sampling

URANIE::Launcher::TLauncher *	_codeLauncher
	a pointer to a code launcher

URANIE::Launcher::TLauncherFunction *	_functionLauncher
	a pointer to a function launcher

Int_t	_nbOut
	the number of outputs

Additional Inherited Members
Public Types inherited from URANIE::Sensitivity::TSensitivity
enum	ELauncher { kCode , kCodeRemote , kFunction , kRun , kUnknown }

Public Attributes inherited from URANIE::Sensitivity::TSensitivity
URANIE::DataServer::TDataServer *	_tds
	Pointeur vers un TDS.

TList *	_listOfInputAttributes
	List of the input branches.

TList *	_listOfOutputAttributes
	List of the input branches.

TString	_sTimeAttribute
	The name of the Time attribute.

Int_t	_nS
	The number of simulation or other information depend on the used method.

Int_t	_nX
	Dimension of the input.

UInt_t	_nY
	Dimension of the target.

UInt_t	_nElY
	Number of element for one selected output.

Int_t	_nbOut
	Total number of Output to be considered.

Int_t	_iOut
	counter for output

unsigned int	_iy
	iterator over number of element

unsigned int	_iely
	iterator over number of element

ELauncher	_nLauncher
	The type of launcher.

TString	_sFunctionName
	The Name of the evaluatuor.

URANIE::Launcher::TCode *	_code
	The tcode.

URANIE::Relauncher::TRun *	_run

TObjArray *	_drawingGarbageCollector
	Garbage collector for prints.

Int_t	_nSeed
	The seed of the random generator.

Bool_t	_bChosenOutputs
	Fact that the input list is provided or not.

Bool_t	_blog
	Boolean for edit the log.

Bool_t	_bdrawProgressBar
	Boolean to know if the progress bar has to be drawn.

Bool_t	_bnoIntermediateSaved
	Boolean to know if the progress bar has to be drawn.

TString	_sIteratorName
	The specific iterator attribute for the method.

TString	_sMethodName
	The method name.

TString	_sSelectedOutput
	The output.

TString	_sSelectedInput
	The input.

map< string, unsigned int >	_mAttributeSize
	Map of size of element for attribute;.

map< string, vector< int > >	_mAttributeElements
	Map of Elements number to run (if vector subselection is requested)

vector< string >	_vOutputNames
	Name of the output.

TCanvas *	_canvas
	Canvas object to deal with.

void(*	_pFunction )(double , double )

TTree *	_ntresult
	The TTree of results.

Protected Member Functions inherited from URANIE::Sensitivity::TSensitivity
void	checkCanvasCreation (bool newcan)
	Create a canvas if needed.

void	drawIndexesHistogram (TString sTitre, const char select="", Option_t option="")
	Draws indexes with an histogram.

void	drawIndexesPie (TString sTitre, const char select="", Option_t option="")
	Draws indexes with an pie chart.

virtual void	postTreatment ()
	PostTreatment for every output.

void	setNoIntermediateSaved (Bool_t bbool=kTRUE)
	Set the "only final file" flag.

Detailed Description

Description of the class TFiniteDifferences.

The finite differences method of sensitivity analysis is among the simplest ones. The resulting sensitivity index of an input variable with respect to an output is an estimation of $\frac{\partial Y}{\partial X}$ .

In this implementation of the method, the estimation is obtained by applying an OAT design of experiments (cf. Sampler::TOATSampling) to the studied model. Thus, for each input varying of $\pm\delta x$ , one can compute:

$\frac{\partial Y}{\partial X} \approx \frac{Y(X+\delta x) - Y(X-\delta x)}{2*\delta x}$

To use the class TFiniteDifferences, one needs to give it an empty TDataServer containing a list of input attributes with default values, and a model. The user will then call the computeIndexes() method which will:

generate the OAT sampling,
call the model on it,
compute the sensitivity indexes,
store them in a sensitivity matrix.

This matrix can be retrieved using the method getSensitivityMatrix() .

(Nicolas Gilardi 2011.06.17)

The finite differences method of sensitivity analysis is among the simplest ones. The resulting sensitivity index of an input variable $x \in X$ with respect to an output is an estimation of $\frac{\partial f}{\partial x}$ around a nominal value x_{nom}.

In this implementation of the method, the estimation is obtained by applying an OAT design of experiments (cf. Sampler::TOATSampling2) to the studied model. For each input's nominal value $x_{nom}$ , we define a range $2 * \pm\delta_x$ and the values:

$x_{sup} = x_{nom} + \pm\delta_x$
$x_{inf} = x_{nom} - \pm\delta_x$

Then we can compute:

$d_1 = \frac{f(x_{norm}) - f(x_{inf})}{\pm\delta_x}$
$d_2 = \frac{f(x_{sup}) - f(x_{norm})}{\pm\delta_x}$

We can then give an estimate of the partial derivative around $x_{nom}$ as:

$\frac{\partial f}{\partial x_{|x = x_{nom}}} \approx d_x = \frac{d1+d2}{2} = \frac{f(x_{sup}) - f(x_{inf})}{2 * \pm\delta_x}$

An indicator of the "linearity" of the function around $x_{nom}$ is given by:

$e_x = |d1-d2| = \frac{|f(x_{sup}) + f(x_{inf}) - 2 * f(x_{nom})|}{\pm\delta_x}$

If varies linearly around $x_{nom}$ , it is expected that will be small compared to . However, this indicator only has a meaning if the function f is "smooth" on the interval $[x_{inf},x_{sup}]$ , and if $d_x \neq 0$ .

To use the class TFiniteDifferences, one needs to give it a TDataServer containing a list nominal values, and a model to compute the values of the outputs. The user will then call the computeIndexes() method which will:

generate the OAT sampling,
call the model on it,
compute the sensitivity indexes,
store them in a sensitivity matrix.

This matrix can be retrieved using the method getSensitivityMatrix() .

(Nicolas Gilardi 2012.10.31)

Constructor & Destructor Documentation

◆ TFiniteDifferences() [1/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences ( )

Default constructor.

Referenced by ClassImp().

◆ TFiniteDifferences() [2/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		void()(Double_t , Double_t *)	fcn,
		TString	sensitiveAtt,
		TString	outputAtt,
		TString	samplingOption = `"steps=1%"`
	)

Constructor with the TDataServer and function.

This constructor is used when the model is a function.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *) an empty dataserver containing the input variables. Each attribute representing an input variables must have a default value set. It will be used as the nominal value.
fcn	(void *) The function to analyse
outputAtt	(TString) this is a list of attribute names separated by colons. They represent all the responses of the problem. They must refer to existing attributes in the "tdsNominal".
sensitiveAtt	(TString) A list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the attributes are taken into account (Default = "").
samplingOption	(TString) The options for the OAT sampling (Default = "steps=1%")

◆ TFiniteDifferences() [3/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		const char *	fcn,
		TString	sensitiveAtt,
		TString	outputAtt,
		TString	samplingOption
	)

Constructor with the TDataServer and function name.

This constructor is used when the model is a function.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *) an empty dataserver containing the input variables. Each attribute representing an input variables must have a default value set. It will be used as the nominal value.
const	char *fcn The function to analyse
outputAtt	(TString) this is a list of attribute names separated by colons. They represent all the responses of the problem. They must refer to existing attributes in the "tdsNominal".
sensitiveAtt	(TString) A list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the attributes are taken into account (Default = "").
samplingOption	(TString) The options for the OAT sampling (Default = "steps=1%")

◆ TFiniteDifferences() [4/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		URANIE::Launcher::TCode *	fcode,
		TString	sensitiveAtt = `""`,
		TString	samplingOption = `"steps=1%"`
	)

Constructor with the TDataServer and code.

This constructor is used when the model is an external code.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *) an empty dataserver containing the input variables. Each attribute representing an input variables must have a default value set. It will be used as the nominal value.
fcode	(URANIE::Launcher::TCode *) The code to analyse
sensitiveAtt	(TString) A list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the attributes are taken into account (Default = "").
samplingOption	(TString) The options for the OAT sampling (Default = "steps=1%")

◆ TFiniteDifferences() [5/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		URANIE::Relauncher::TRun *	run,
		TString	sensitiveAtt = `""`,
		TString	samplingOption = `"steps=1%"`
	)

Constructor with the TDataServer and TRun.

This constructor is used when the model is a TRun/TEval model

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *) an empty dataserver containing the input variables. Each attribute representing an input variables must have a default value set. It will be used as the nominal value.
fcode	(URANIE::Relauncher::TRun *) The TRun to use for analyse
sensitiveAtt	(TString) A list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the attributes are taken into account (Default = "").
samplingOption	(TString) The options for the OAT sampling (Default = "steps=1%")

◆ TFiniteDifferences() [6/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		TString	inputAtt,
		TString	outputAtt,
		TString	sensitiveAtt = `TString("")`
	)

Constructor with a loaded data server.

This constructor is used when the user already have a dataset.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *) this TDS contains the input and output values necessary for the finite differences calculation. It must also contain the attributes "__nominal_set__" and "__modified_att__" which are necessary to know which attribute is modified.
inputAtt	(TString) this is a list of attribute names separated by colons. They represent all the inputs of the problem. They must refer to existing attributes in the "tdsNominal".
outputAtt	(TString) this is a list of attribute names separated by colons. They represent all the responses of the problem. They must refer to existing attributes in the "tdsNominal".
sensitiveAtt	(TString) A list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the input attributes are taken into account (Default = "").

Warning: When using this constructor with CINT, do not forget to EXPLICITELY pass TString objects for "listOfInputs" and "listOfOutputs" parameters. Otherwise, you may end up calling another constructor !

◆ ~TFiniteDifferences() [1/2]

virtual URANIE::Sensitivity::TFiniteDifferences::~TFiniteDifferences ( )

virtual

Default destructor.

Referenced by ClassImp().

◆ TFiniteDifferences() [7/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences ( )

Default constructor.

◆ TFiniteDifferences() [8/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		void *	fcn,
		TString	sensitiveAtt,
		TString	ranges,
		TString	inputAtt,
		TString	outputAtt
	)

Constructor with the TDataServer and function.

This constructor is used when the model is a function.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *): the dataserver containing the nominal values of the input variables.
fcn	(void *): the function to analyse.
sensitiveAtt	(TString): a list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the attributes are taken into account.
ranges	(TString): a list of range values or range attribute names. It must have either one or as many element as sensitiveAtt. Each numerical value of the list is interpreted as the range of the OAT sampling. Each name is interpreted as the one of the attribute refering to this range in the data server. If a "%" character follows the value or name, the range will be a percentage of the nominal value. If only one value (or attribute name) is given, the corresponding range will be applied to all sensitive attributes. Example: sensitiveAtt = "x1:x2:x3:x4", ranges = "0.33:5%:rx3:rx4%": range for variable x1 is 0.33, range for variable x2 is 5% of the nominal value of x2, range for variable x3 is the nominal value of attribute rx3, range for variable x4 is the nominal value of attribute rx4 interpeted as a percentage of the nominal value of x4, i.e. if rx4 = 0.1, range for x4 is 0.1% of x4.
inputAtt	(TString): a list of attribute names separated by colons. They represent all the inputs of the problem. They must refer to existing attributes in the "tdsNominal". If the string is equal to "" or "*", all the attributes are taken into account.
outputAtt	(TString): a list of names separated by colons. They represent all the responses of the problem. Attributes with these names will be added to the data server.

◆ TFiniteDifferences() [9/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		URANIE::Launcher::TCode *	fcode,
		TString	sensitiveAtt,
		TString	ranges
	)

Constructor with the TDataServer and code.

This constructor is used when the model is an external code.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *):the dataserver containing the nominal values of the input variables.
fcode	(URANIE::Launcher::TCode *): the code to analyse.
sensitiveAtt	(TString): a list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the attributes are taken into account.
ranges	(TString): a list of range values or range attribute names. It must have either one or as many element as sensitiveAtt. Each numerical value of the list is interpreted as the range of the OAT sampling. Each name is interpreted as the one of the attribute refering to this range in the data server. If a "%" character follows the value or name, the range will be a percentage of the nominal value. If only one value (or attribute name) is given, the corresponding range will be applied to all sensitive attributes. Example: sensitiveAtt = "x1:x2:x3:x4", ranges = "0.33:5%:rx3:rx4%": range for variable x1 is 0.33, range for variable x2 is 5% of the nominal value of x2, range for variable x3 is the nominal value of attribute rx3, range for variable x4 is the nominal value of attribute rx4 interpeted as a percentage of the nominal value of x4, i.e. if rx4 = 0.1, range for x4 is 0.1% of x4.

◆ TFiniteDifferences() [10/10]

URANIE::Sensitivity::TFiniteDifferences::TFiniteDifferences	(	TDataServer *	tdsNominal,
		TString	sensitiveAtt,
		TString	inputAtt,
		TString	outputAtt
	)

Constructor with a loaded data server.

This constructor is used when the user already have a dataset.

Parameters

tdsNominal	(URANIE::DataServer::TDataServer *): this TDS contains the input and output values necessary for the finite differences calculation. It must also contain the attributes "__nominal_set__" and "__modified_att__" which are necessary to know which attribute is modified.
sensitiveAtt	(TString): a list of attribute names separated by colons. These attributes are the one that will be taken into account for the sensitivity analysis. If the string is equal to "" or "*", all the input attributes are taken into account.
inputAtt	(TString): a list of attribute names separated by colons. They represent all the inputs of the problem. They must refer to existing attributes in the "tdsNominal".
outputAtt	(TString): a list of attribute names separated by colons. They represent all the responses of the problem. They must refer to existing attributes in the "tdsNominal".

Warning: When using this constructor with CINT, do not forget to EXPLICITELY pass TString objects for "inputAtt" and "outputAtt" parameters. Otherwise, you may end up calling another constructor !

◆ ~TFiniteDifferences() [2/2]

virtual URANIE::Sensitivity::TFiniteDifferences::~TFiniteDifferences ( )

virtual

Default destructor.

Member Function Documentation

◆ computeIndexes()

void URANIE::Sensitivity::TFiniteDifferences::computeIndexes ( Option_t * option = "" )

Compute the sensitivity matrix.

This function construct the sensitivity matrix from the OAT design of experiment. The resulting matrix can be retrieved using the getSensitivityMatrix() function.

Parameters

option (Option_t *) just in case we need option in the future... (Default = "").

Referenced by ClassImp().

◆ evaluateIndexes()

void URANIE::Sensitivity::TFiniteDifferences::evaluateIndexes ( Option_t * option = "" )

virtual

Compute the sensitivity matrix.

This function construct the sensitivity matrix from the OAT design of experiment. The resulting matrix can be retrieved using the getSensitivityMatrix() function.

Parameters

option (Option_t *) just in case we need option in the future... (Default = "").

Implements URANIE::Sensitivity::TSensitivity.

Referenced by ClassImp().

◆ generateSample() [1/2]

virtual void URANIE::Sensitivity::TFiniteDifferences::generateSample ( Option_t * option = "" )

virtual

Generates the OAT sampling.

This function fills a TDS with appropriate variables, creates and uses a TOATSampling to generate the design of experiment.

Parameters

option (Option_t *) just in case we need option in the future... (Default = "").

Implements URANIE::Sensitivity::TSensitivity.

Reimplemented in URANIE::Sensitivity::TDGSM.

Referenced by ClassImp().

◆ generateSample() [2/2]

virtual void URANIE::Sensitivity::TFiniteDifferences::generateSample ( Option_t * option = "" )

virtual

Generates the OAT sampling.

This function fills a TDS with appropriate variables, creates and uses a TOATSampling to generate the design of experiment.

Parameters

option (Option_t *) just in case we need option in the future... (Default = "").

Implements URANIE::Sensitivity::TSensitivity.

Reimplemented in URANIE::Sensitivity::TDGSM.

◆ getDataServer()

TDataServer * URANIE::Sensitivity::TFiniteDifferences::getDataServer ( )

inline

Returns a pointer to the internal data server.

References URANIE::Sensitivity::TSensitivity::_tds.

◆ getOATDataServer()

TDataServer * URANIE::Sensitivity::TFiniteDifferences::getOATDataServer ( )

inline

Returns a pointer to the internal data server.

References _tdsOAT.

◆ getSensitivityMatrix() [1/2]

TMatrixD URANIE::Sensitivity::TFiniteDifferences::getSensitivityMatrix ( )

inline

Returns the sensitivity matrix.

References _sMatrix.

◆ getSensitivityMatrix() [2/2]

TMatrixD URANIE::Sensitivity::TFiniteDifferences::getSensitivityMatrix ( )

inline

Returns the sensitivity matrix.

References _sMatrix.

◆ preTreatment()

void URANIE::Sensitivity::TFiniteDifferences::preTreatment ( )

virtual

Resize the matrix results at the very beginning of the algo to the number of output/input.

One should wait this moment to know the number of output as it depends on the presence of vectors as well as the newoption "output=" implemented in TSensitivity::computeIndexes()

Reimplemented from URANIE::Sensitivity::TSensitivity.

Referenced by ClassImp().

◆ printLog() [1/2]

virtual void URANIE::Sensitivity::TFiniteDifferences::printLog ( )

virtual

Reimplemented in URANIE::Sensitivity::TDGSM.

Referenced by ClassImp().

◆ printLog() [2/2]

virtual void URANIE::Sensitivity::TFiniteDifferences::printLog ( )

virtual

Reimplemented in URANIE::Sensitivity::TDGSM.

Member Data Documentation

◆ _codeLauncher

URANIE::Launcher::TLauncher* URANIE::Sensitivity::TFiniteDifferences::_codeLauncher

private

a pointer to a code launcher

Referenced by ClassImp().

◆ _functionLauncher

URANIE::Launcher::TLauncherFunction* URANIE::Sensitivity::TFiniteDifferences::_functionLauncher

private

a pointer to a function launcher

Referenced by ClassImp().

◆ _generateData

Bool_t URANIE::Sensitivity::TFiniteDifferences::_generateData

private

if TRUE, one need to generate the sampling

Referenced by ClassImp().

◆ _lInputs

TObjArray* URANIE::Sensitivity::TFiniteDifferences::_lInputs

private

the full list of input variables

◆ _lOutputs

TObjArray* URANIE::Sensitivity::TFiniteDifferences::_lOutputs

private

the full list of outputs

◆ _lRanges

TObjArray* URANIE::Sensitivity::TFiniteDifferences::_lRanges

private

the list of ranges of the sensitives attributes

Referenced by ClassImp().

◆ _lSensitiveAtt

TObjArray* URANIE::Sensitivity::TFiniteDifferences::_lSensitiveAtt

private

The list of sensitive attributes.

Referenced by ClassImp().

◆ _nbAux

Int_t URANIE::Sensitivity::TFiniteDifferences::_nbAux

protected

the number of non sensitive inputs

Referenced by ClassImp(), and ClassImp().

◆ _nbIn

Int_t URANIE::Sensitivity::TFiniteDifferences::_nbIn

protected

the number of sensitive inputs

Referenced by ClassImp(), and ClassImp().

◆ _nbOut

Int_t URANIE::Sensitivity::TFiniteDifferences::_nbOut

private

the number of outputs

Referenced by ClassImp(), and ClassImp().

◆ _sMatrix

TMatrixD URANIE::Sensitivity::TFiniteDifferences::_sMatrix

protected

The sensitivity matrix.

Referenced by ClassImp(), ClassImp(), and getSensitivityMatrix().

◆ _sSamplingOption

TString URANIE::Sensitivity::TFiniteDifferences::_sSamplingOption

protected

the options for the OAT sampling

Referenced by ClassImp(), and ClassImp().

◆ _sSensitiveAtt

TString URANIE::Sensitivity::TFiniteDifferences::_sSensitiveAtt

protected

The list of sensitive attributes.

Referenced by ClassImp().

◆ _tdsOAT

URANIE::DataServer::TDataServer* URANIE::Sensitivity::TFiniteDifferences::_tdsOAT

private

a pointer to the data server containing the OAT sampling

Referenced by ClassImp(), and getOATDataServer().

Documentation / Developer's manual

Available modules

Public Member Functions

Protected Attributes

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ TFiniteDifferences() [1/10]

◆ TFiniteDifferences() [2/10]

◆ TFiniteDifferences() [3/10]

◆ TFiniteDifferences() [4/10]

◆ TFiniteDifferences() [5/10]

◆ TFiniteDifferences() [6/10]

◆ ~TFiniteDifferences() [1/2]

◆ TFiniteDifferences() [7/10]

◆ TFiniteDifferences() [8/10]

◆ TFiniteDifferences() [9/10]

◆ TFiniteDifferences() [10/10]

◆ ~TFiniteDifferences() [2/2]

Member Function Documentation

◆ computeIndexes()

◆ evaluateIndexes()

◆ generateSample() [1/2]

◆ generateSample() [2/2]

◆ getDataServer()

◆ getOATDataServer()

◆ getSensitivityMatrix() [1/2]

◆ getSensitivityMatrix() [2/2]

◆ preTreatment()

◆ printLog() [1/2]

◆ printLog() [2/2]

Member Data Documentation

◆ _codeLauncher

◆ _functionLauncher

◆ _generateData

◆ _lInputs

◆ _lOutputs

◆ _lRanges

◆ _lSensitiveAtt

◆ _nbAux

◆ _nbIn

◆ _nbOut

◆ _sMatrix

◆ _sSamplingOption

◆ _sSensitiveAtt

◆ _tdsOAT