ROOT  6.06/09
Reference Guide
Public Member Functions | Protected Types | Protected Member Functions | Protected Attributes | Private Member Functions | Private Attributes | List of all members
TMVA::RuleFitParams Class Reference

Definition at line 57 of file RuleFitParams.h.

Public Member Functions

 RuleFitParams ()
 constructor More...
 
virtual ~RuleFitParams ()
 destructor More...
 
void Init ()
 Initializes all parameters using the RuleEnsemble and the training tree. More...
 
void SetMsgType (EMsgType t)
 
void SetRuleFit (RuleFit *rf)
 
void SetGDNPathSteps (Int_t np)
 
void SetGDPathStep (Double_t s)
 
void SetGDTauRange (Double_t t0, Double_t t1)
 
void SetGDTauScan (UInt_t n)
 
void SetGDTau (Double_t t)
 
void SetGDErrScale (Double_t s)
 
void SetGDTauPrec (Double_t p)
 
Int_t Type (const Event *e) const
 
UInt_t GetPathIdx1 () const
 
UInt_t GetPathIdx2 () const
 
UInt_t GetPerfIdx1 () const
 
UInt_t GetPerfIdx2 () const
 
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...
 
Double_t Risk (UInt_t ind1, UInt_t ind2, Double_t neff) const
 risk asessment More...
 
Double_t Risk (UInt_t ind1, UInt_t ind2, Double_t neff, UInt_t itau) const
 risk asessment 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...
 
Double_t Penalty () const
 This is the "lasso" penalty To be used for regression. More...
 
void InitGD ()
 Initialize GD path search. More...
 
Int_t FindGDTau ()
 This finds the cutoff parameter tau by scanning several different paths. More...
 
void MakeGDPath ()
 The following finds the gradient directed path in parameter space. More...
 

Protected Types

typedef std::vector< const TMVA::Event * >::const_iterator EventItr
 

Protected Member Functions

void InitNtuple ()
 initializes the ntuple More...
 
void CalcGDNTau ()
 
void FillCoefficients ()
 helper function to store the rule coefficients in local arrays More...
 
void CalcFStar ()
 Estimates F* (optimum scoring function) for all events for the given sets. 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 ErrorRateRocRaw (std::vector< Double_t > &sFsig, std::vector< Double_t > &sFbkg)
 
Double_t ErrorRateRoc ()
 Estimates the error rate with the current set of parameters. More...
 
void ErrorRateRocTst ()
 Estimates the error rate with the current set of parameters. More...
 
Double_t Optimism ()
 implementation of eq. More...
 
void MakeGradientVector ()
 make gradient vector More...
 
void UpdateCoefficients ()
 Establish maximum gradient for rules, linear terms and the offset. More...
 
Double_t CalcAverageResponse ()
 calulate the average response - TODO : rewrite bad dependancy on EvaluateAverage() ! More...
 
Double_t CalcAverageResponseOLD ()
 
Double_t CalcAverageTruth ()
 calulate the average truth 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 MakeTstGradientVector ()
 make test gradient vector for all tau same algorithm as MakeGradientVector() More...
 
void UpdateTstCoefficients ()
 Establish maximum gradient for rules, linear terms and the offset for all taus TODO: do not need index range! More...
 
void CalcTstAverageResponse ()
 calc average response for all test paths - TODO: see comment under CalcAverageResponse() note that 0 offset is used More...
 

Protected Attributes

RuleFit * fRuleFit
 
RuleEnsemble * fRuleEnsemble
 
UInt_t fNRules
 
UInt_t fNLinear
 
UInt_t fPathIdx1
 
UInt_t fPathIdx2
 
UInt_t fPerfIdx1
 
UInt_t fPerfIdx2
 
Double_t fNEveEffPath
 
Double_t fNEveEffPerf
 
std::vector< Double_t > fAverageSelectorPath
 
std::vector< Double_t > fAverageRulePath
 
std::vector< Double_t > fAverageSelectorPerf
 
std::vector< Double_t > fAverageRulePerf
 
std::vector< Double_t > fGradVec
 
std::vector< Double_t > fGradVecLin
 
std::vector< std::vector< Double_t > > fGradVecTst
 
std::vector< std::vector< Double_t > > fGradVecLinTst
 
std::vector< Double_t > fGDErrTst
 
std::vector< Char_t > fGDErrTstOK
 
std::vector< std::vector< Double_t > > fGDCoefTst
 
std::vector< std::vector< Double_t > > fGDCoefLinTst
 
std::vector< Double_t > fGDOfsTst
 
std::vector< Double_t > fGDTauVec
 
UInt_t fGDNTauTstOK
 
UInt_t fGDNTau
 
Double_t fGDTauPrec
 
UInt_t fGDTauScan
 
Double_t fGDTauMin
 
Double_t fGDTauMax
 
Double_t fGDTau
 
Double_t fGDPathStep
 
Int_t fGDNPathSteps
 
Double_t fGDErrScale
 
Double_t fAverageTruth
 
std::vector< Double_t > fFstar
 
Double_t fFstarMedian
 
TTree * fGDNtuple
 
Double_t fNTRisk
 
Double_t fNTErrorRate
 
Double_t fNTNuval
 
Double_t fNTCoefRad
 
Double_t fNTOffset
 
Double_t * fNTCoeff
 
Double_t * fNTLinCoeff
 
Double_t fsigave
 
Double_t fsigrms
 
Double_t fbkgave
 
Double_t fbkgrms
 

Private Member Functions

MsgLogger & Log () const
 message logger More...
 

Private Attributes

MsgLogger * fLogger
 

#include <TMVA/RuleFitParams.h>

+ Collaboration diagram for TMVA::RuleFitParams:

Member Typedef Documentation

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

Definition at line 138 of file RuleFitParams.h.

Constructor & Destructor Documentation

TMVA::RuleFitParams::RuleFitParams ( )

constructor

Definition at line 57 of file RuleFitParams.cxx.

TMVA::RuleFitParams::~RuleFitParams ( )
virtual

destructor

Definition at line 97 of file RuleFitParams.cxx.

Member Function Documentation

Double_t TMVA::RuleFitParams::CalcAverageResponse ( )
protected

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

note that 0 offset is used

Definition at line 1522 of file RuleFitParams.cxx.

Double_t TMVA::RuleFitParams::CalcAverageResponseOLD ( )
protected
Double_t TMVA::RuleFitParams::CalcAverageTruth ( )
protected

calulate the average truth

Definition at line 1537 of file RuleFitParams.cxx.

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 876 of file RuleFitParams.cxx.

void TMVA::RuleFitParams::CalcGDNTau ( )
inlineprotected

Definition at line 144 of file RuleFitParams.h.

Referenced by SetGDTauPrec().

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 1501 of file RuleFitParams.cxx.

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 1004 of file RuleFitParams.cxx.

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 959 of file RuleFitParams.cxx.

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 1107 of file RuleFitParams.cxx.

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

Definition at line 1035 of file RuleFitParams.cxx.

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 1157 of file RuleFitParams.cxx.

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 201 of file RuleFitParams.cxx.

Referenced by EvaluateAveragePath(), and EvaluateAveragePerf().

void TMVA::RuleFitParams::EvaluateAveragePath ( )
inlineprotected

Definition at line 185 of file RuleFitParams.h.

void TMVA::RuleFitParams::EvaluateAveragePerf ( )
inlineprotected

Definition at line 188 of file RuleFitParams.h.

void TMVA::RuleFitParams::FillCoefficients ( )
protected

helper function to store the rule coefficients in local arrays

Definition at line 858 of file RuleFitParams.cxx.

Int_t TMVA::RuleFitParams::FindGDTau ( )

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

Definition at line 442 of file RuleFitParams.cxx.

UInt_t TMVA::RuleFitParams::GetPathIdx1 ( ) const
inline

Definition at line 99 of file RuleFitParams.h.

UInt_t TMVA::RuleFitParams::GetPathIdx2 ( ) const
inline

Definition at line 100 of file RuleFitParams.h.

UInt_t TMVA::RuleFitParams::GetPerfIdx1 ( ) const
inline

Definition at line 101 of file RuleFitParams.h.

UInt_t TMVA::RuleFitParams::GetPerfIdx2 ( ) const
inline

Definition at line 102 of file RuleFitParams.h.

void TMVA::RuleFitParams::Init ( void  )

Initializes all parameters using the RuleEnsemble and the training tree.

Definition at line 107 of file RuleFitParams.cxx.

Referenced by RuleFitParams().

void TMVA::RuleFitParams::InitGD ( )

Initialize GD path search.

Definition at line 366 of file RuleFitParams.cxx.

void TMVA::RuleFitParams::InitNtuple ( )
protected

initializes the ntuple

Definition at line 178 of file RuleFitParams.cxx.

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

message logger

Definition at line 262 of file RuleFitParams.h.

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 271 of file RuleFitParams.cxx.

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 283 of file RuleFitParams.cxx.

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 295 of file RuleFitParams.cxx.

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 onditions are fullfilled: a. loop index==fGDNPathSteps b. error > fGDErrScale*errmin c. only in DEBUG mode: risk is not monotoneously decreasing

The algorithm will warn if: I. the error rate was still decreasing when loop finnished -> increase fGDNPathSteps! II. minimum was found at an early stage -> decrease fGDPathStep III. DEBUG: risk > previous risk -> entered caotic region (regularization is too small)

Definition at line 530 of file RuleFitParams.cxx.

void TMVA::RuleFitParams::MakeGradientVector ( )
protected

make gradient vector

Definition at line 1383 of file RuleFitParams.cxx.

void TMVA::RuleFitParams::MakeTstGradientVector ( )
protected

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

Definition at line 1263 of file RuleFitParams.cxx.

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 917 of file RuleFitParams.cxx.

Double_t TMVA::RuleFitParams::Penalty ( ) const

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

— NOT USED —

Definition at line 349 of file RuleFitParams.cxx.

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

risk asessment

Definition at line 307 of file RuleFitParams.cxx.

Referenced by RiskPath(), and RiskPerf().

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

risk asessment for tau model <itau>

Definition at line 327 of file RuleFitParams.cxx.

Double_t TMVA::RuleFitParams::RiskPath ( ) const
inline

Definition at line 116 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::RiskPerf ( ) const
inline

Definition at line 117 of file RuleFitParams.h.

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

Definition at line 118 of file RuleFitParams.h.

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 1205 of file RuleFitParams.cxx.

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

Definition at line 93 of file RuleFitParams.h.

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

Definition at line 73 of file RuleFitParams.h.

Referenced by TMVA::RuleFit::SetGDNPathSteps().

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

Definition at line 76 of file RuleFitParams.h.

Referenced by TMVA::RuleFit::SetGDPathStep().

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

Definition at line 90 of file RuleFitParams.h.

Referenced by TMVA::RuleFit::SetGDTau().

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

Definition at line 94 of file RuleFitParams.h.

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

Definition at line 79 of file RuleFitParams.h.

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

Definition at line 87 of file RuleFitParams.h.

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

Definition at line 1567 of file RuleFitParams.cxx.

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

Definition at line 70 of file RuleFitParams.h.

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

Definition at line 1560 of file RuleFitParams.cxx.

void TMVA::RuleFitParams::UpdateCoefficients ( )
protected

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

Definition at line 1451 of file RuleFitParams.cxx.

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

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

Definition at line 213 of file RuleFitParams.h.

Referenced by EvaluateAveragePath().

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

Definition at line 215 of file RuleFitParams.h.

Referenced by EvaluateAveragePerf().

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

Definition at line 212 of file RuleFitParams.h.

Referenced by EvaluateAveragePath().

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

Definition at line 214 of file RuleFitParams.h.

Referenced by EvaluateAveragePerf().

Double_t TMVA::RuleFitParams::fAverageTruth
protected

Definition at line 240 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fbkgave
protected

Definition at line 256 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fbkgrms
protected

Definition at line 257 of file RuleFitParams.h.

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

Definition at line 242 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fFstarMedian
protected

Definition at line 243 of file RuleFitParams.h.

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

Definition at line 226 of file RuleFitParams.h.

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

Definition at line 225 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fGDErrScale
protected

Definition at line 238 of file RuleFitParams.h.

Referenced by SetGDErrScale().

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

Definition at line 223 of file RuleFitParams.h.

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

Definition at line 224 of file RuleFitParams.h.

Int_t TMVA::RuleFitParams::fGDNPathSteps
protected

Definition at line 237 of file RuleFitParams.h.

Referenced by SetGDNPathSteps().

UInt_t TMVA::RuleFitParams::fGDNTau
protected

Definition at line 230 of file RuleFitParams.h.

Referenced by CalcGDNTau(), and SetGDTauPrec().

UInt_t TMVA::RuleFitParams::fGDNTauTstOK
protected

Definition at line 229 of file RuleFitParams.h.

TTree* TMVA::RuleFitParams::fGDNtuple
protected

Definition at line 245 of file RuleFitParams.h.

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

Definition at line 227 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fGDPathStep
protected

Definition at line 236 of file RuleFitParams.h.

Referenced by SetGDPathStep().

Double_t TMVA::RuleFitParams::fGDTau
protected

Definition at line 235 of file RuleFitParams.h.

Referenced by SetGDTau().

Double_t TMVA::RuleFitParams::fGDTauMax
protected

Definition at line 234 of file RuleFitParams.h.

Referenced by SetGDTauRange().

Double_t TMVA::RuleFitParams::fGDTauMin
protected

Definition at line 233 of file RuleFitParams.h.

Referenced by SetGDTauRange().

Double_t TMVA::RuleFitParams::fGDTauPrec
protected

Definition at line 231 of file RuleFitParams.h.

Referenced by CalcGDNTau(), and SetGDTauPrec().

UInt_t TMVA::RuleFitParams::fGDTauScan
protected

Definition at line 232 of file RuleFitParams.h.

Referenced by SetGDTauScan().

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

Definition at line 228 of file RuleFitParams.h.

Referenced by SetGDTauPrec().

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

Definition at line 217 of file RuleFitParams.h.

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

Definition at line 218 of file RuleFitParams.h.

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

Definition at line 221 of file RuleFitParams.h.

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

Definition at line 220 of file RuleFitParams.h.

MsgLogger* TMVA::RuleFitParams::fLogger
mutableprivate

Definition at line 261 of file RuleFitParams.h.

Referenced by Log().

Double_t TMVA::RuleFitParams::fNEveEffPath
protected

Definition at line 209 of file RuleFitParams.h.

Referenced by RiskPath().

Double_t TMVA::RuleFitParams::fNEveEffPerf
protected

Definition at line 210 of file RuleFitParams.h.

Referenced by RiskPerf().

UInt_t TMVA::RuleFitParams::fNLinear
protected

Definition at line 200 of file RuleFitParams.h.

UInt_t TMVA::RuleFitParams::fNRules
protected

Definition at line 199 of file RuleFitParams.h.

Double_t* TMVA::RuleFitParams::fNTCoeff
protected

Definition at line 251 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fNTCoefRad
protected

Definition at line 249 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fNTErrorRate
protected

Definition at line 247 of file RuleFitParams.h.

Double_t* TMVA::RuleFitParams::fNTLinCoeff
protected

Definition at line 252 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fNTNuval
protected

Definition at line 248 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fNTOffset
protected

Definition at line 250 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fNTRisk
protected

Definition at line 246 of file RuleFitParams.h.

UInt_t TMVA::RuleFitParams::fPathIdx1
protected

Definition at line 205 of file RuleFitParams.h.

Referenced by EvaluateAveragePath(), GetPathIdx1(), and RiskPath().

UInt_t TMVA::RuleFitParams::fPathIdx2
protected

Definition at line 206 of file RuleFitParams.h.

Referenced by EvaluateAveragePath(), GetPathIdx2(), and RiskPath().

UInt_t TMVA::RuleFitParams::fPerfIdx1
protected

Definition at line 207 of file RuleFitParams.h.

Referenced by EvaluateAveragePerf(), GetPerfIdx1(), and RiskPerf().

UInt_t TMVA::RuleFitParams::fPerfIdx2
protected

Definition at line 208 of file RuleFitParams.h.

Referenced by EvaluateAveragePerf(), GetPerfIdx2(), and RiskPerf().

RuleEnsemble* TMVA::RuleFitParams::fRuleEnsemble
protected

Definition at line 197 of file RuleFitParams.h.

RuleFit* TMVA::RuleFitParams::fRuleFit
protected

Definition at line 196 of file RuleFitParams.h.

Referenced by SetRuleFit().

Double_t TMVA::RuleFitParams::fsigave
protected

Definition at line 254 of file RuleFitParams.h.

Double_t TMVA::RuleFitParams::fsigrms
protected

Definition at line 255 of file RuleFitParams.h.

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