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TMVA::RuleFitParams Class Reference
TMVA

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]):

  1. Set offset to -average(y(true)) and all coefs=0 => average of F(x)==0
  2. FindGDTau() : start scanning using several paths defined by different tau choose the tau yielding the best path
  3. start the scanning the chosen path
  4. check error rate at a given frequency data used for check: [fPerfIdx1,fPerfIdx2]
  5. 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:

  1. the error rate was still decreasing when loop finished -> increase fGDNPathSteps!
  2. minimum was found at an early stage -> decrease fGDPathStep
  3. 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: