A class doing the actual fitting of a linear model using rules as base functions.

Definition at line 53 of file RuleFitParams.h.

Public Member Functions
	RuleFitParams ()
	constructor More...

virtual	~RuleFitParams ()
	destructor More...

Int_t	FindGDTau ()
	This finds the cutoff parameter tau by scanning several different paths. More...

UInt_t	GetPathIdx1 () const

UInt_t	GetPathIdx2 () const

UInt_t	GetPerfIdx1 () const

UInt_t	GetPerfIdx2 () const

void	Init ()
	Initializes all parameters using the RuleEnsemble and the training tree. More...

void	InitGD ()
	Initialize GD path search. More...

Double_t	LossFunction (const Event &e) const
	Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...

Double_t	LossFunction (UInt_t evtidx) const
	Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...

Double_t	LossFunction (UInt_t evtidx, UInt_t itau) const
	Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg) More...

void	MakeGDPath ()
	The following finds the gradient directed path in parameter space. More...

Double_t	Penalty () const
	This is the "lasso" penalty To be used for regression. More...

Double_t	Risk (UInt_t ind1, UInt_t ind2, Double_t neff) const
	risk assessment More...

Double_t	Risk (UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const
	risk assessment for tau model <itau> More...

Double_t	RiskPath () const

Double_t	RiskPerf () const

Double_t	RiskPerf (UInt_t itau) const

UInt_t	RiskPerfTst ()
	Estimates the error rate with the current set of parameters. More...

void	SetGDErrScale (Double_t s)

void	SetGDNPathSteps (Int_t np)

void	SetGDPathStep (Double_t s)

void	SetGDTau (Double_t t)

void	SetGDTauPrec (Double_t p)

void	SetGDTauRange (Double_t t0, Double_t t1)

void	SetGDTauScan (UInt_t n)

void	SetMsgType (EMsgType t)

void	SetRuleFit (RuleFit *rf)

Int_t	Type (const Event *e) const

Protected Types
typedef std::vector< constTMVA::Event * >::const_iterator	EventItr

Protected Member Functions
Double_t	CalcAverageResponse ()
	calculate the average response - TODO : rewrite bad dependancy on EvaluateAverage() ! More...

Double_t	CalcAverageResponseOLD ()

Double_t	CalcAverageTruth ()
	calculate the average truth More...

void	CalcFStar ()
	Estimates F* (optimum scoring function) for all events for the given sets. More...

void	CalcGDNTau ()

void	CalcTstAverageResponse ()
	calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used More...

Double_t	ErrorRateBin ()
	Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F(x)) (y-F(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —. More...

Double_t	ErrorRateReg ()
	Estimates the error rate with the current set of parameters This code is pretty messy at the moment. More...

Double_t	ErrorRateRoc ()
	Estimates the error rate with the current set of parameters. More...

Double_t	ErrorRateRocRaw (std::vector< Double_t > &sFsig, std::vector< Double_t > &sFbkg)
	Estimates the error rate with the current set of parameters. More...

void	ErrorRateRocTst ()
	Estimates the error rate with the current set of parameters. More...

void	EvaluateAverage (UInt_t ind1, UInt_t ind2, std::vector< Double_t > &avsel, std::vector< Double_t > &avrul)
	evaluate the average of each variable and f(x) in the given range More...

void	EvaluateAveragePath ()

void	EvaluateAveragePerf ()

void	FillCoefficients ()
	helper function to store the rule coefficients in local arrays More...

void	InitNtuple ()
	initializes the ntuple More...

void	MakeGradientVector ()
	make gradient vector More...

void	MakeTstGradientVector ()
	make test gradient vector for all tau same algorithm as MakeGradientVector() More...

Double_t	Optimism ()
	implementation of eq. More...

void	UpdateCoefficients ()
	Establish maximum gradient for rules, linear terms and the offset. More...

void	UpdateTstCoefficients ()
	Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range! More...

Protected Attributes
std::vector< Double_t >	fAverageRulePath

std::vector< Double_t >	fAverageRulePerf

std::vector< Double_t >	fAverageSelectorPath

std::vector< Double_t >	fAverageSelectorPerf

Double_t	fAverageTruth

Double_t	fbkgave

Double_t	fbkgrms

std::vector< Double_t >	fFstar

Double_t	fFstarMedian

std::vector< std::vector< Double_t > >	fGDCoefLinTst

std::vector< std::vector< Double_t > >	fGDCoefTst

Double_t	fGDErrScale

std::vector< Double_t >	fGDErrTst

std::vector< Char_t >	fGDErrTstOK

Int_t	fGDNPathSteps

UInt_t	fGDNTau

UInt_t	fGDNTauTstOK

TTree *	fGDNtuple

std::vector< Double_t >	fGDOfsTst

Double_t	fGDPathStep

Double_t	fGDTau

Double_t	fGDTauMax

Double_t	fGDTauMin

Double_t	fGDTauPrec

UInt_t	fGDTauScan

std::vector< Double_t >	fGDTauVec

std::vector< Double_t >	fGradVec

std::vector< Double_t >	fGradVecLin

std::vector< std::vector< Double_t > >	fGradVecLinTst

std::vector< std::vector< Double_t > >	fGradVecTst

Double_t	fNEveEffPath

Double_t	fNEveEffPerf

UInt_t	fNLinear

UInt_t	fNRules

Double_t *	fNTCoeff

Double_t	fNTCoefRad

Double_t	fNTErrorRate

Double_t *	fNTLinCoeff

Double_t	fNTNuval

Double_t	fNTOffset

Double_t	fNTRisk

UInt_t	fPathIdx1

UInt_t	fPathIdx2

UInt_t	fPerfIdx1

UInt_t	fPerfIdx2

RuleEnsemble *	fRuleEnsemble

RuleFit *	fRuleFit

Double_t	fsigave

Double_t	fsigrms

Private Member Functions
MsgLogger &	Log () const
	message logger More...

Private Attributes
MsgLogger *	fLogger

#include <TMVA/RuleFitParams.h>

Member Typedef Documentation

◆ EventItr

typedef std::vector<constTMVA::Event*>::const_iterator TMVA::RuleFitParams::EventItr

protected

Definition at line 134 of file RuleFitParams.h.

Constructor & Destructor Documentation

◆ RuleFitParams()

TMVA::RuleFitParams::RuleFitParams ( )

constructor

Definition at line 65 of file RuleFitParams.cxx.

◆ ~RuleFitParams()

TMVA::RuleFitParams::~RuleFitParams ( )

virtual

destructor

Definition at line 105 of file RuleFitParams.cxx.

Member Function Documentation

◆ CalcAverageResponse()

Double_t TMVA::RuleFitParams::CalcAverageResponse ( )

protected

calculate the average response - TODO : rewrite bad dependancy on EvaluateAverage() !

note that 0 offset is used

Definition at line 1519 of file RuleFitParams.cxx.

◆ CalcAverageResponseOLD()

Double_t TMVA::RuleFitParams::CalcAverageResponseOLD ( )

protected

◆ CalcAverageTruth()

Double_t TMVA::RuleFitParams::CalcAverageTruth ( )

protected

calculate the average truth

Definition at line 1534 of file RuleFitParams.cxx.

◆ CalcFStar()

void TMVA::RuleFitParams::CalcFStar ( )

protected

Estimates F* (optimum scoring function) for all events for the given sets.

The result is used in ErrorRateReg(). — NOT USED —

Definition at line 886 of file RuleFitParams.cxx.

◆ CalcGDNTau()

void TMVA::RuleFitParams::CalcGDNTau ( )

inlineprotected

Definition at line 140 of file RuleFitParams.h.

◆ CalcTstAverageResponse()

void TMVA::RuleFitParams::CalcTstAverageResponse ( )

protected

calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used

Definition at line 1498 of file RuleFitParams.cxx.

◆ ErrorRateBin()

Double_t TMVA::RuleFitParams::ErrorRateBin ( )

protected

Estimates the error rate with the current set of parameters It uses a binary estimate of (y-F*(x)) (y-F*(x)) = (Num of events where sign(F)!=sign(y))/Neve y = {+1 if event is signal, -1 otherwise} — NOT USED —.

Definition at line 1011 of file RuleFitParams.cxx.

◆ ErrorRateReg()

Double_t TMVA::RuleFitParams::ErrorRateReg ( )

protected

Estimates the error rate with the current set of parameters This code is pretty messy at the moment.

Cleanup is needed. – NOT USED —

Definition at line 967 of file RuleFitParams.cxx.

◆ ErrorRateRoc()

Double_t TMVA::RuleFitParams::ErrorRateRoc ( )

protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area. This works but is less efficient than calculating the Risk using RiskPerf().

Definition at line 1112 of file RuleFitParams.cxx.

◆ ErrorRateRocRaw()

Double_t TMVA::RuleFitParams::ErrorRateRocRaw	(	std::vector< Double_t > &	sFsig,
		std::vector< Double_t > &	sFbkg
	)

protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.

Definition at line 1045 of file RuleFitParams.cxx.

◆ ErrorRateRocTst()

void TMVA::RuleFitParams::ErrorRateRocTst ( )

protected

Estimates the error rate with the current set of parameters.

It calculates the area under the bkg rejection vs signal efficiency curve. The value returned is 1-area.

See comment under ErrorRateRoc().

Definition at line 1160 of file RuleFitParams.cxx.

◆ EvaluateAverage()

void TMVA::RuleFitParams::EvaluateAverage	(	UInt_t	ind1,
		UInt_t	ind2,
		std::vector< Double_t > &	avsel,
		std::vector< Double_t > &	avrul
	)

protected

evaluate the average of each variable and f(x) in the given range

Definition at line 209 of file RuleFitParams.cxx.

◆ EvaluateAveragePath()

void TMVA::RuleFitParams::EvaluateAveragePath ( )

inlineprotected

Definition at line 181 of file RuleFitParams.h.

◆ EvaluateAveragePerf()

void TMVA::RuleFitParams::EvaluateAveragePerf ( )

inlineprotected

Definition at line 184 of file RuleFitParams.h.

◆ FillCoefficients()

void TMVA::RuleFitParams::FillCoefficients ( )

protected

helper function to store the rule coefficients in local arrays

Definition at line 869 of file RuleFitParams.cxx.

◆ FindGDTau()

Int_t TMVA::RuleFitParams::FindGDTau ( )

This finds the cutoff parameter tau by scanning several different paths.

Definition at line 450 of file RuleFitParams.cxx.

◆ GetPathIdx1()

UInt_t TMVA::RuleFitParams::GetPathIdx1 ( ) const

inline

Definition at line 95 of file RuleFitParams.h.

◆ GetPathIdx2()

UInt_t TMVA::RuleFitParams::GetPathIdx2 ( ) const

inline

Definition at line 96 of file RuleFitParams.h.

◆ GetPerfIdx1()

UInt_t TMVA::RuleFitParams::GetPerfIdx1 ( ) const

inline

Definition at line 97 of file RuleFitParams.h.

◆ GetPerfIdx2()

UInt_t TMVA::RuleFitParams::GetPerfIdx2 ( ) const

inline

Definition at line 98 of file RuleFitParams.h.

◆ Init()

void TMVA::RuleFitParams::Init ( void )

Initializes all parameters using the RuleEnsemble and the training tree.

Definition at line 115 of file RuleFitParams.cxx.

◆ InitGD()

void TMVA::RuleFitParams::InitGD ( )

Initialize GD path search.

Definition at line 374 of file RuleFitParams.cxx.

◆ InitNtuple()

void TMVA::RuleFitParams::InitNtuple ( )

protected

initializes the ntuple

Definition at line 186 of file RuleFitParams.cxx.

◆ Log()

MsgLogger & TMVA::RuleFitParams::Log ( ) const

inlineprivate

message logger

Definition at line 258 of file RuleFitParams.h.

◆ LossFunction() [1/3]

Double_t TMVA::RuleFitParams::LossFunction ( const Event & e ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 279 of file RuleFitParams.cxx.

◆ LossFunction() [2/3]

Double_t TMVA::RuleFitParams::LossFunction ( UInt_t evtidx ) const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 291 of file RuleFitParams.cxx.

◆ LossFunction() [3/3]

Double_t TMVA::RuleFitParams::LossFunction	(	UInt_t	evtidx,
		UInt_t	itau
	)		const

Implementation of squared-error ramp loss function (eq 39,40 in ref 1) This is used for binary Classifications where y = {+1,-1} for (sig,bkg)

Definition at line 303 of file RuleFitParams.cxx.

◆ MakeGDPath()

void TMVA::RuleFitParams::MakeGDPath ( )

The following finds the gradient directed path in parameter space.

More work is needed... FT, 24/9/2006

The algorithm is currently as follows (if not otherwise stated, the sample used below is [fPathIdx1,fPathIdx2]):

Set offset to -average(y(true)) and all coefs=0 => average of F(x)==0
FindGDTau() : start scanning using several paths defined by different tau choose the tau yielding the best path
start the scanning the chosen path
check error rate at a given frequency data used for check: [fPerfIdx1,fPerfIdx2]
stop when either of the following conditions are fullfilled:
1. loop index==fGDNPathSteps
2. error > fGDErrScale*errmin
3. only in DEBUG mode: risk is not monotonously decreasing

The algorithm will warn if:

the error rate was still decreasing when loop finished -> increase fGDNPathSteps!
minimum was found at an early stage -> decrease fGDPathStep
DEBUG: risk > previous risk -> entered chaotic region (regularization is too small)

Definition at line 539 of file RuleFitParams.cxx.

◆ MakeGradientVector()

void TMVA::RuleFitParams::MakeGradientVector ( )

protected

make gradient vector

Definition at line 1382 of file RuleFitParams.cxx.

◆ MakeTstGradientVector()

void TMVA::RuleFitParams::MakeTstGradientVector ( )

protected

make test gradient vector for all tau same algorithm as MakeGradientVector()

Definition at line 1264 of file RuleFitParams.cxx.

◆ Optimism()

Double_t TMVA::RuleFitParams::Optimism ( )

protected

implementation of eq.

7.17 in Hastie,Tibshirani & Friedman book this is the covariance between the estimated response yhat and the true value y. NOT REALLY SURE IF THIS IS CORRECT! — THIS IS NOT USED —

Definition at line 926 of file RuleFitParams.cxx.

◆ Penalty()

Double_t TMVA::RuleFitParams::Penalty ( ) const

This is the "lasso" penalty To be used for regression.

— NOT USED —

Definition at line 357 of file RuleFitParams.cxx.

◆ Risk() [1/2]

Double_t TMVA::RuleFitParams::Risk	(	UInt_t	ind1,
		UInt_t	ind2,
		Double_t	neff
	)		const

risk assessment

Definition at line 315 of file RuleFitParams.cxx.

◆ Risk() [2/2]

Double_t TMVA::RuleFitParams::Risk	(	UInt_t	ind1,
		UInt_t	ind2,
		Double_t	neff,
		UInt_t	itau
	)		const

risk assessment for tau model <itau>

Definition at line 335 of file RuleFitParams.cxx.

◆ RiskPath()

Double_t TMVA::RuleFitParams::RiskPath ( ) const

inline

Definition at line 112 of file RuleFitParams.h.

◆ RiskPerf() [1/2]

Double_t TMVA::RuleFitParams::RiskPerf ( ) const

inline

Definition at line 113 of file RuleFitParams.h.

◆ RiskPerf() [2/2]

Double_t TMVA::RuleFitParams::RiskPerf ( UInt_t itau ) const

inline

Definition at line 114 of file RuleFitParams.h.

◆ RiskPerfTst()

UInt_t TMVA::RuleFitParams::RiskPerfTst ( )

Estimates the error rate with the current set of parameters.

using the <Perf> subsample. Return the tau index giving the lowest error

Definition at line 1206 of file RuleFitParams.cxx.

◆ SetGDErrScale()

void TMVA::RuleFitParams::SetGDErrScale ( Double_t s )

inline

Definition at line 89 of file RuleFitParams.h.

◆ SetGDNPathSteps()

void TMVA::RuleFitParams::SetGDNPathSteps ( Int_t np )

inline

Definition at line 69 of file RuleFitParams.h.

◆ SetGDPathStep()

void TMVA::RuleFitParams::SetGDPathStep ( Double_t s )

inline

Definition at line 72 of file RuleFitParams.h.

◆ SetGDTau()

void TMVA::RuleFitParams::SetGDTau ( Double_t t )

inline

Definition at line 86 of file RuleFitParams.h.

◆ SetGDTauPrec()

void TMVA::RuleFitParams::SetGDTauPrec ( Double_t p )

inline

Definition at line 90 of file RuleFitParams.h.

◆ SetGDTauRange()

void TMVA::RuleFitParams::SetGDTauRange	(	Double_t	t0,
		Double_t	t1
	)

inline

Definition at line 75 of file RuleFitParams.h.

◆ SetGDTauScan()

void TMVA::RuleFitParams::SetGDTauScan ( UInt_t n )

inline

Definition at line 83 of file RuleFitParams.h.

◆ SetMsgType()

void TMVA::RuleFitParams::SetMsgType ( EMsgType t )

Definition at line 1563 of file RuleFitParams.cxx.

◆ SetRuleFit()

void TMVA::RuleFitParams::SetRuleFit ( RuleFit * rf )

inline

Definition at line 66 of file RuleFitParams.h.

◆ Type()

Int_t TMVA::RuleFitParams::Type ( const Event * e ) const

Definition at line 1557 of file RuleFitParams.cxx.

◆ UpdateCoefficients()

void TMVA::RuleFitParams::UpdateCoefficients ( )

protected

Establish maximum gradient for rules, linear terms and the offset.

Definition at line 1448 of file RuleFitParams.cxx.

◆ UpdateTstCoefficients()

void TMVA::RuleFitParams::UpdateTstCoefficients ( )

protected

Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range!

Definition at line 1334 of file RuleFitParams.cxx.

Member Data Documentation

◆ fAverageRulePath

std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePath

protected

Definition at line 209 of file RuleFitParams.h.

◆ fAverageRulePerf

std::vector<Double_t> TMVA::RuleFitParams::fAverageRulePerf

protected

Definition at line 211 of file RuleFitParams.h.

◆ fAverageSelectorPath

std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPath

protected

Definition at line 208 of file RuleFitParams.h.

◆ fAverageSelectorPerf

std::vector<Double_t> TMVA::RuleFitParams::fAverageSelectorPerf

protected

Definition at line 210 of file RuleFitParams.h.

◆ fAverageTruth

Double_t TMVA::RuleFitParams::fAverageTruth

protected

Definition at line 236 of file RuleFitParams.h.

◆ fbkgave

Double_t TMVA::RuleFitParams::fbkgave

protected

Definition at line 252 of file RuleFitParams.h.

◆ fbkgrms

Double_t TMVA::RuleFitParams::fbkgrms

protected

Definition at line 253 of file RuleFitParams.h.

◆ fFstar

std::vector<Double_t> TMVA::RuleFitParams::fFstar

protected

Definition at line 238 of file RuleFitParams.h.

◆ fFstarMedian

Double_t TMVA::RuleFitParams::fFstarMedian

protected

Definition at line 239 of file RuleFitParams.h.

◆ fGDCoefLinTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefLinTst

protected

Definition at line 222 of file RuleFitParams.h.

◆ fGDCoefTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGDCoefTst

protected

Definition at line 221 of file RuleFitParams.h.

◆ fGDErrScale

Double_t TMVA::RuleFitParams::fGDErrScale

protected

Definition at line 234 of file RuleFitParams.h.

◆ fGDErrTst

std::vector<Double_t> TMVA::RuleFitParams::fGDErrTst

protected

Definition at line 219 of file RuleFitParams.h.

◆ fGDErrTstOK

std::vector<Char_t> TMVA::RuleFitParams::fGDErrTstOK

protected

Definition at line 220 of file RuleFitParams.h.

◆ fGDNPathSteps

Int_t TMVA::RuleFitParams::fGDNPathSteps

protected

Definition at line 233 of file RuleFitParams.h.

◆ fGDNTau

UInt_t TMVA::RuleFitParams::fGDNTau

protected

Definition at line 226 of file RuleFitParams.h.

◆ fGDNTauTstOK

UInt_t TMVA::RuleFitParams::fGDNTauTstOK

protected

Definition at line 225 of file RuleFitParams.h.

◆ fGDNtuple

TTree* TMVA::RuleFitParams::fGDNtuple

protected

Definition at line 241 of file RuleFitParams.h.

◆ fGDOfsTst

std::vector<Double_t> TMVA::RuleFitParams::fGDOfsTst

protected

Definition at line 223 of file RuleFitParams.h.

◆ fGDPathStep

Double_t TMVA::RuleFitParams::fGDPathStep

protected

Definition at line 232 of file RuleFitParams.h.

◆ fGDTau

Double_t TMVA::RuleFitParams::fGDTau

protected

Definition at line 231 of file RuleFitParams.h.

◆ fGDTauMax

Double_t TMVA::RuleFitParams::fGDTauMax

protected

Definition at line 230 of file RuleFitParams.h.

◆ fGDTauMin

Double_t TMVA::RuleFitParams::fGDTauMin

protected

Definition at line 229 of file RuleFitParams.h.

◆ fGDTauPrec

Double_t TMVA::RuleFitParams::fGDTauPrec

protected

Definition at line 227 of file RuleFitParams.h.

◆ fGDTauScan

UInt_t TMVA::RuleFitParams::fGDTauScan

protected

Definition at line 228 of file RuleFitParams.h.

◆ fGDTauVec

std::vector< Double_t > TMVA::RuleFitParams::fGDTauVec

protected

Definition at line 224 of file RuleFitParams.h.

◆ fGradVec

std::vector<Double_t> TMVA::RuleFitParams::fGradVec

protected

Definition at line 213 of file RuleFitParams.h.

◆ fGradVecLin

std::vector<Double_t> TMVA::RuleFitParams::fGradVecLin

protected

Definition at line 214 of file RuleFitParams.h.

◆ fGradVecLinTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecLinTst

protected

Definition at line 217 of file RuleFitParams.h.

◆ fGradVecTst

std::vector< std::vector<Double_t> > TMVA::RuleFitParams::fGradVecTst

protected

Definition at line 216 of file RuleFitParams.h.

◆ fLogger

MsgLogger* TMVA::RuleFitParams::fLogger

mutableprivate

Definition at line 257 of file RuleFitParams.h.

◆ fNEveEffPath

Double_t TMVA::RuleFitParams::fNEveEffPath

protected

Definition at line 205 of file RuleFitParams.h.

◆ fNEveEffPerf

Double_t TMVA::RuleFitParams::fNEveEffPerf

protected

Definition at line 206 of file RuleFitParams.h.

◆ fNLinear

UInt_t TMVA::RuleFitParams::fNLinear

protected

Definition at line 196 of file RuleFitParams.h.

◆ fNRules

UInt_t TMVA::RuleFitParams::fNRules

protected

Definition at line 195 of file RuleFitParams.h.

◆ fNTCoeff

Double_t* TMVA::RuleFitParams::fNTCoeff

protected

Definition at line 247 of file RuleFitParams.h.

◆ fNTCoefRad

Double_t TMVA::RuleFitParams::fNTCoefRad

protected

Definition at line 245 of file RuleFitParams.h.

◆ fNTErrorRate

Double_t TMVA::RuleFitParams::fNTErrorRate

protected

Definition at line 243 of file RuleFitParams.h.

◆ fNTLinCoeff

Double_t* TMVA::RuleFitParams::fNTLinCoeff

protected

Definition at line 248 of file RuleFitParams.h.

◆ fNTNuval

Double_t TMVA::RuleFitParams::fNTNuval

protected

Definition at line 244 of file RuleFitParams.h.

◆ fNTOffset

Double_t TMVA::RuleFitParams::fNTOffset

protected

Definition at line 246 of file RuleFitParams.h.

◆ fNTRisk

Double_t TMVA::RuleFitParams::fNTRisk

protected

Definition at line 242 of file RuleFitParams.h.

◆ fPathIdx1

UInt_t TMVA::RuleFitParams::fPathIdx1

protected

Definition at line 201 of file RuleFitParams.h.

◆ fPathIdx2

UInt_t TMVA::RuleFitParams::fPathIdx2

protected

Definition at line 202 of file RuleFitParams.h.

◆ fPerfIdx1

UInt_t TMVA::RuleFitParams::fPerfIdx1

protected

Definition at line 203 of file RuleFitParams.h.

◆ fPerfIdx2

UInt_t TMVA::RuleFitParams::fPerfIdx2

protected

Definition at line 204 of file RuleFitParams.h.

◆ fRuleEnsemble

RuleEnsemble* TMVA::RuleFitParams::fRuleEnsemble

protected

Definition at line 193 of file RuleFitParams.h.

◆ fRuleFit

RuleFit* TMVA::RuleFitParams::fRuleFit

protected

Definition at line 192 of file RuleFitParams.h.

◆ fsigave

Double_t TMVA::RuleFitParams::fsigave

protected

Definition at line 250 of file RuleFitParams.h.

◆ fsigrms

Double_t TMVA::RuleFitParams::fsigrms

protected

Definition at line 251 of file RuleFitParams.h.

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

tmva/tmva/inc/TMVA/RuleFitParams.h
tmva/tmva/src/RuleFitParams.cxx

Public Member Functions

Protected Types

Protected Member Functions

Protected Attributes

Private Member Functions

Private Attributes

Member Typedef Documentation

◆ EventItr

Constructor & Destructor Documentation

◆ RuleFitParams()

◆ ~RuleFitParams()

Member Function Documentation

◆ CalcAverageResponse()

◆ CalcAverageResponseOLD()

◆ CalcAverageTruth()

◆ CalcFStar()

◆ CalcGDNTau()

◆ CalcTstAverageResponse()

◆ ErrorRateBin()

◆ ErrorRateReg()

◆ ErrorRateRoc()

◆ ErrorRateRocRaw()

◆ ErrorRateRocTst()

◆ EvaluateAverage()

◆ EvaluateAveragePath()

◆ EvaluateAveragePerf()

◆ FillCoefficients()

◆ FindGDTau()

◆ GetPathIdx1()

◆ GetPathIdx2()

◆ GetPerfIdx1()

◆ GetPerfIdx2()

◆ Init()

◆ InitGD()

◆ InitNtuple()

◆ Log()

◆ LossFunction() [1/3]

◆ LossFunction() [2/3]

◆ LossFunction() [3/3]

◆ MakeGDPath()

◆ MakeGradientVector()

◆ MakeTstGradientVector()

◆ Optimism()

◆ Penalty()

◆ Risk() [1/2]

◆ Risk() [2/2]

◆ RiskPath()

◆ RiskPerf() [1/2]

◆ RiskPerf() [2/2]

◆ RiskPerfTst()

◆ SetGDErrScale()

◆ SetGDNPathSteps()

◆ SetGDPathStep()

◆ SetGDTau()

◆ SetGDTauPrec()

◆ SetGDTauRange()

◆ SetGDTauScan()

◆ SetMsgType()

◆ SetRuleFit()

◆ Type()

◆ UpdateCoefficients()

◆ UpdateTstCoefficients()

Member Data Documentation

◆ fAverageRulePath

◆ fAverageRulePerf

◆ fAverageSelectorPath

◆ fAverageSelectorPerf

◆ fAverageTruth

◆ fbkgave

◆ fbkgrms

◆ fFstar

◆ fFstarMedian

◆ fGDCoefLinTst

◆ fGDCoefTst

◆ fGDErrScale

◆ fGDErrTst

◆ fGDErrTstOK

◆ fGDNPathSteps

◆ fGDNTau

◆ fGDNTauTstOK