#include <LRSensitivity.H>

Public Member Functions
	LRSensitivity ()

	LRSensitivity (label Npara, label Np)

	~LRSensitivity ()

void	buildSamplingSet (std::vector< std::string > &pdflist, Eigen::MatrixXd plist)
	Method to build MatX, it internally calls ITHACAsampling::samplingMC pdflist is the list with the names of the distributions of the parameters plist contains the parameters of the distributions, therefore its size must be (No_parameters,2)

void	load_output ()
	Method to load the output of FOM/ROM inside member LRsensitivity member y.

void	getXstats ()
	Method to compute EX and VX, it sets Xdone=true.

void	getYstat ()
	Method to compute Ey and Vy, it sets Ydone=true.

void	getBetas ()
	Method to compute SRCs, it sets bdone=true.

void	assessQuality ()
	Method to compute the quality of linear regression approximation.

Public Attributes
Eigen::MatrixXd	trainingRange
	Matrix to store the range used in training/offline stage.

Eigen::MatrixXd	MatX
	Matrices storing the independent variables' values.

Eigen::MatrixXd	MatXn
	Matrices storing the independent variables' normalized values.

autoPtr< FofM >	M
	Figure of merit object.

label	No_parameters
	Number of parameters used for the analysis.

label	Npoints
	Number of points used for Monte Carlo sampling.

Eigen::VectorXd	y
	Vectors to store the output of the model.

Eigen::VectorXd	yn
	Vector to store the normalized output of the model.

Eigen::VectorXd	ylin
	Vector to store the result of linear approximation.

double	Ey
	Mean value of the output.

double	Vy
	Variance of the output.

Eigen::VectorXd	EX
	Vector to store the meanvalues of the parameters.

Eigen::VectorXd	VX
	Vector to store the variances of the parameters.

Eigen::VectorXd	betas
	Vector to store the standardized regression coefficient (SRC)

double	QI
	double to quantify the goodness of linear regression from 0 (no linearity) to 1 (perfect linearity) also known as R^2

bool	Xdone = false
	boolean variable to check if EX and VX are computed

bool	Ydone = false
	boolean variable to check if Ey and Vy are computed

bool	bdone = false
	boolean variable to check if SRCs are computed

Detailed Description

Definition at line 42 of file LRSensitivity.H.

Constructor & Destructor Documentation

◆ LRSensitivity() [1/2]

LRSensitivity::LRSensitivity ( )

Definition at line 8 of file LRSensitivity.C.

◆ LRSensitivity() [2/2]

LRSensitivity::LRSensitivity	(	label	Npara,
		label	Np )

explicit

Definition at line 10 of file LRSensitivity.C.

◆ ~LRSensitivity()

LRSensitivity::~LRSensitivity ( )

inline

Definition at line 48 of file LRSensitivity.H.

Member Function Documentation

◆ assessQuality()

void LRSensitivity::assessQuality ( )

Method to compute the quality of linear regression approximation.

Definition at line 127 of file LRSensitivity.C.

◆ buildSamplingSet()

void LRSensitivity::buildSamplingSet	(	std::vector< std::string > &	pdflist,
		Eigen::MatrixXd	plist )

Method to build MatX, it internally calls ITHACAsampling::samplingMC pdflist is the list with the names of the distributions of the parameters plist contains the parameters of the distributions, therefore its size must be (No_parameters,2)

Definition at line 18 of file LRSensitivity.C.

◆ getBetas()

void LRSensitivity::getBetas ( )

Method to compute SRCs, it sets bdone=true.

Definition at line 99 of file LRSensitivity.C.

◆ getXstats()

void LRSensitivity::getXstats ( )

Method to compute EX and VX, it sets Xdone=true.

Definition at line 80 of file LRSensitivity.C.

◆ getYstat()

void LRSensitivity::getYstat ( )

Method to compute Ey and Vy, it sets Ydone=true.

Definition at line 65 of file LRSensitivity.C.

◆ load_output()

void LRSensitivity::load_output ( )

Method to load the output of FOM/ROM inside member LRsensitivity member y.

Definition at line 51 of file LRSensitivity.C.

Member Data Documentation

◆ bdone

bool LRSensitivity::bdone = false

boolean variable to check if SRCs are computed

Definition at line 87 of file LRSensitivity.H.

◆ betas

Eigen::VectorXd LRSensitivity::betas

Vector to store the standardized regression coefficient (SRC)

Definition at line 78 of file LRSensitivity.H.

◆ EX

Eigen::VectorXd LRSensitivity::EX

Vector to store the meanvalues of the parameters.

Definition at line 74 of file LRSensitivity.H.

◆ Ey

double LRSensitivity::Ey

Mean value of the output.

Definition at line 70 of file LRSensitivity.H.

◆ M

autoPtr<FofM> LRSensitivity::M

Figure of merit object.

Definition at line 58 of file LRSensitivity.H.

◆ MatX

Eigen::MatrixXd LRSensitivity::MatX

Matrices storing the independent variables' values.

Definition at line 54 of file LRSensitivity.H.

◆ MatXn

Eigen::MatrixXd LRSensitivity::MatXn

Matrices storing the independent variables' normalized values.

Definition at line 56 of file LRSensitivity.H.

◆ No_parameters

label LRSensitivity::No_parameters

Number of parameters used for the analysis.

Definition at line 60 of file LRSensitivity.H.

◆ Npoints

label LRSensitivity::Npoints

Number of points used for Monte Carlo sampling.

Definition at line 62 of file LRSensitivity.H.

◆ QI

double LRSensitivity::QI

double to quantify the goodness of linear regression from 0 (no linearity) to 1 (perfect linearity) also known as R^2

Definition at line 81 of file LRSensitivity.H.

◆ trainingRange

Eigen::MatrixXd LRSensitivity::trainingRange

Matrix to store the range used in training/offline stage.

Definition at line 52 of file LRSensitivity.H.

◆ VX

Eigen::VectorXd LRSensitivity::VX

Vector to store the variances of the parameters.

Definition at line 76 of file LRSensitivity.H.

◆ Vy

double LRSensitivity::Vy

Variance of the output.

Definition at line 72 of file LRSensitivity.H.

◆ Xdone

bool LRSensitivity::Xdone = false

boolean variable to check if EX and VX are computed

Definition at line 83 of file LRSensitivity.H.

◆ y

Eigen::VectorXd LRSensitivity::y

Vectors to store the output of the model.

Definition at line 64 of file LRSensitivity.H.

◆ Ydone

bool LRSensitivity::Ydone = false

boolean variable to check if Ey and Vy are computed

Definition at line 85 of file LRSensitivity.H.

◆ ylin

Eigen::VectorXd LRSensitivity::ylin

Vector to store the result of linear approximation.

Definition at line 68 of file LRSensitivity.H.

◆ yn

Eigen::VectorXd LRSensitivity::yn

Vector to store the normalized output of the model.

Definition at line 66 of file LRSensitivity.H.

The documentation for this class was generated from the following files:

/github/workspace/src/ITHACA_CORE/ITHACAsensitivity/LRSensitivity.H
/github/workspace/src/ITHACA_CORE/ITHACAsensitivity/LRSensitivity.C

Public Member Functions

Public Attributes