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class TMVA::MethodRuleFit: public TMVA::MethodBase


 J Friedman's RuleFit method

Function Members (Methods)

public:
virtual~MethodRuleFit()
voidTObject::AbstractMethod(const char* method) const
voidTMVA::Configurable::AddOptionsXMLTo(void* parent) const
voidTMVA::MethodBase::AddOutput(TMVA::Types::ETreeType type, TMVA::Types::EAnalysisType analysisType)
virtual voidAddWeightsXMLTo(void* parent) const
virtual voidTObject::AppendPad(Option_t* option = "")
TDirectory*TMVA::MethodBase::BaseDir() const
virtual voidTObject::Browse(TBrowser* b)
voidTMVA::Configurable::CheckForUnusedOptions() const
virtual voidTMVA::MethodBase::CheckSetup()
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
TMVA::ConfigurableTMVA::Configurable::Configurable(const TString& theOption = "")
virtual voidTObject::Copy(TObject& object) const
virtual const TMVA::Ranking*CreateRanking()
TMVA::DataSet*TMVA::MethodBase::Data() const
TMVA::DataSetInfo&TMVA::MethodBase::DataInfo() const
virtual voidTMVA::MethodBase::DeclareCompatibilityOptions()
virtual voidTObject::Delete(Option_t* option = "")MENU
voidTMVA::MethodBase::DisableWriting(Bool_t setter)
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
Bool_tTMVA::MethodBase::DoMulticlass() const
Bool_tTMVA::MethodBase::DoRegression() const
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
TMVA::Types::EAnalysisTypeTMVA::MethodBase::GetAnalysisType() const
const char*TMVA::Configurable::GetConfigDescription() const
const char*TMVA::Configurable::GetConfigName() const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
virtual Double_tTMVA::MethodBase::GetEfficiency(const TString&, TMVA::Types::ETreeType, Double_t& err)
const TMVA::Event*TMVA::MethodBase::GetEvent() const
const TMVA::Event*TMVA::MethodBase::GetEvent(const TMVA::Event* ev) const
const TMVA::Event*TMVA::MethodBase::GetEvent(Long64_t ievt) const
const TMVA::Event*TMVA::MethodBase::GetEvent(Long64_t ievt, TMVA::Types::ETreeType type) const
const vector<TMVA::Event*>&TMVA::MethodBase::GetEventCollection(TMVA::Types::ETreeType type)
const vector<TMVA::DecisionTree*>&GetForest() const
Double_tGetGDErrScale() const
Int_tGetGDNPathSteps() const
Double_tGetGDPathEveFrac() const
Double_tGetGDPathStep() const
Double_tGetGDValidEveFrac() const
virtual const char*TObject::GetIconName() const
const TString&TMVA::MethodBase::GetInputLabel(Int_t i) const
const TString&TMVA::MethodBase::GetInputTitle(Int_t i) const
const TString&TMVA::MethodBase::GetInputVar(Int_t i) const
const TString&TMVA::MethodBase::GetJobName() const
Double_tGetLinQuantile() const
Double_tGetMaxFracNEve() const
virtual Double_tTMVA::MethodBase::GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
Double_tTMVA::MethodBase::GetMean(Int_t ivar) const
TDirectory*GetMethodBaseDir() const
const TString&TMVA::MethodBase::GetMethodName() const
TMVA::Types::EMVATMVA::MethodBase::GetMethodType() const
TStringTMVA::MethodBase::GetMethodTypeName() const
Double_tGetMinFracNEve() const
virtual vector<Float_t>TMVA::MethodBase::GetMulticlassEfficiency(vector<std::vector<Float_t> >& purity)
virtual vector<Float_t>TMVA::MethodBase::GetMulticlassTrainingEfficiency(vector<std::vector<Float_t> >& purity)
virtual const vector<Float_t>&TMVA::MethodBase::GetMulticlassValues()
virtual Double_tGetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)
virtual const char*TMVA::MethodBase::GetName() const
Int_tGetNCuts() const
UInt_tTMVA::MethodBase::GetNEvents() const
UInt_tTMVA::MethodBase::GetNTargets() const
Int_tGetNTrees() const
UInt_tTMVA::MethodBase::GetNvar() const
UInt_tTMVA::MethodBase::GetNVariables() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
const TString&TMVA::Configurable::GetOptions() const
virtual Double_tTMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)
const TStringTMVA::MethodBase::GetProbaName() const
TMVA::DecisionTree::EPruneMethodGetPruneMethod() const
Double_tGetPruneStrength() const
virtual Double_tTMVA::MethodBase::GetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
virtual voidTMVA::MethodBase::GetRegressionDeviation(UInt_t tgtNum, TMVA::Types::ETreeType type, Double_t& stddev, Double_t& stddev90Percent) const
virtual const vector<Float_t>&TMVA::MethodBase::GetRegressionValues()
Int_tGetRFNendnodes() const
Int_tGetRFNrules() const
const TStringGetRFWorkDir() const
Double_tTMVA::MethodBase::GetRMS(Int_t ivar) const
virtual Double_tTMVA::MethodBase::GetROCIntegral(TH1F* histS, TH1F* histB) const
virtual Double_tTMVA::MethodBase::GetROCIntegral(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
const TMVA::RuleFit*GetRuleFitConstPtr() const
TMVA::RuleFit*GetRuleFitPtr()
virtual Double_tTMVA::MethodBase::GetSeparation(TH1*, TH1*) const
virtual Double_tTMVA::MethodBase::GetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
TMVA::SeparationBase*GetSeparationBase() const
const TMVA::SeparationBase*GetSeparationBaseConst() const
Double_tTMVA::MethodBase::GetSignalReferenceCut() const
Double_tTMVA::MethodBase::GetSignalReferenceCutOrientation() const
virtual Double_tTMVA::MethodBase::GetSignificance() const
const TMVA::Event*TMVA::MethodBase::GetTestingEvent(Long64_t ievt) const
Double_tTMVA::MethodBase::GetTestTime() const
const TString&TMVA::MethodBase::GetTestvarName() const
virtual const char*TObject::GetTitle() const
virtual Double_tTMVA::MethodBase::GetTrainingEfficiency(const TString&)
const TMVA::Event*TMVA::MethodBase::GetTrainingEvent(Long64_t ievt) const
const vector<TMVA::Event*>&GetTrainingEvents() const
UInt_tTMVA::MethodBase::GetTrainingROOTVersionCode() const
TStringTMVA::MethodBase::GetTrainingROOTVersionString() const
UInt_tTMVA::MethodBase::GetTrainingTMVAVersionCode() const
TStringTMVA::MethodBase::GetTrainingTMVAVersionString() const
Double_tTMVA::MethodBase::GetTrainTime() const
TMVA::TransformationHandler&TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true)
const TMVA::TransformationHandler&TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true) const
Double_tGetTreeEveFrac() const
virtual UInt_tTObject::GetUniqueID() const
TStringTMVA::MethodBase::GetWeightFileName() const
Double_tTMVA::MethodBase::GetXmax(Int_t ivar) const
Double_tTMVA::MethodBase::GetXmin(Int_t ivar) const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual Bool_tHasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
virtual ULong_tTObject::Hash() const
Bool_tTMVA::MethodBase::HasMVAPdfs() const
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
Bool_tTObject::IsOnHeap() const
virtual Bool_tTMVA::MethodBase::IsSignalLike()
virtual Bool_tTMVA::MethodBase::IsSignalLike(Double_t mvaVal)
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
virtual voidTMVA::MethodBase::MakeClass(const TString& classFileName = TString("")) const
voidTObject::MayNotUse(const char* method) const
TDirectory*TMVA::MethodBase::MethodBaseDir() const
TMVA::MethodRuleFitMethodRuleFit(TMVA::DataSetInfo& theData, const TString& theWeightFile, TDirectory* theTargetDir = NULL)
TMVA::MethodRuleFitMethodRuleFit(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption = "", TDirectory* theTargetDir = 0)
virtual Bool_tTMVA::MethodBase::MonitorBoost(TMVA::MethodBoost*)
virtual Bool_tTObject::Notify()
voidTObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
TMVA::IMethod&TMVA::IMethod::operator=(const TMVA::IMethod&)
virtual map<TString,Double_t>TMVA::MethodBase::OptimizeTuningParameters(TString fomType = "ROCIntegral", TString fitType = "FitGA")
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTMVA::Configurable::ParseOptions()
virtual voidTObject::Pop()
virtual voidTObject::Print(Option_t* option = "") const
virtual voidTMVA::MethodBase::PrintHelpMessage() const
voidTMVA::Configurable::PrintOptions() const
voidTMVA::MethodBase::ProcessSetup()
virtual Int_tTObject::Read(const char* name)
voidTMVA::Configurable::ReadOptionsFromStream(istream& istr)
voidTMVA::Configurable::ReadOptionsFromXML(void* node)
voidTMVA::MethodBase::ReadStateFromFile()
voidTMVA::MethodBase::ReadStateFromStream(istream& tf)
voidTMVA::MethodBase::ReadStateFromStream(TFile& rf)
voidTMVA::MethodBase::ReadStateFromXMLString(const char* xmlstr)
virtual voidReadWeightsFromStream(istream& istr)
virtual voidReadWeightsFromXML(void* wghtnode)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTMVA::MethodBase::RerouteTransformationHandler(TMVA::TransformationHandler* fTargetTransformation)
virtual voidTMVA::MethodBase::Reset()
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
virtual voidTMVA::MethodBase::SetAnalysisType(TMVA::Types::EAnalysisType type)
voidTMVA::MethodBase::SetBaseDir(TDirectory* methodDir)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidTMVA::Configurable::SetConfigDescription(const char* d)
voidTMVA::Configurable::SetConfigName(const char* n)
virtual voidTMVA::MethodBase::SetCurrentEvent(Long64_t ievt) const
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
voidTMVA::MethodBase::SetMethodBaseDir(TDirectory* methodDir)
voidTMVA::MethodBase::SetMethodDir(TDirectory* methodDir)
voidTMVA::Configurable::SetMsgType(TMVA::EMsgType t)
static voidTObject::SetObjectStat(Bool_t stat)
voidTMVA::Configurable::SetOptions(const TString& s)
voidTMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
voidTMVA::MethodBase::SetSignalReferenceCutOrientation(Double_t cutOrientation)
voidTMVA::MethodBase::SetTestTime(Double_t testTime)
voidTMVA::MethodBase::SetTestvarName(const TString& v = "")
voidTMVA::MethodBase::SetTrainTime(Double_t trainTime)
virtual voidTMVA::MethodBase::SetTuneParameters(map<TString,Double_t> tuneParameters)
virtual voidTObject::SetUniqueID(UInt_t uid)
voidTMVA::MethodBase::SetupMethod()
virtual voidShowMembers(TMemberInspector& insp)
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTMVA::MethodBase::TestClassification()
virtual voidTMVA::MethodBase::TestMulticlass()
virtual voidTMVA::MethodBase::TestRegression(Double_t& bias, Double_t& biasT, Double_t& dev, Double_t& devT, Double_t& rms, Double_t& rmsT, Double_t& mInf, Double_t& mInfT, Double_t& corr, TMVA::Types::ETreeType type)
virtual voidTrain()
voidTMVA::MethodBase::TrainMethod()
Bool_tUseBoost() const
virtual voidTObject::UseCurrentStyle()
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = 0, Int_t option = 0, Int_t bufsize = 0) const
virtual voidTMVA::MethodBase::WriteEvaluationHistosToFile(TMVA::Types::ETreeType treetype)
virtual voidWriteMonitoringHistosToFile() const
voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
voidTMVA::MethodBase::WriteStateToFile() const
protected:
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
virtual voidGetHelpMessage() const
const TString&TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
const TString&TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
const TString&TMVA::Configurable::GetReferenceFile() const
static TMVA::MethodBase*TMVA::MethodBase::GetThisBase()
Float_tTMVA::MethodBase::GetTWeight(const TMVA::Event* ev) const
const TString&TMVA::MethodBase::GetWeightFileDir() const
Bool_tTMVA::MethodBase::HasTrainingTree() const
Bool_tTMVA::MethodBase::Help() const
Bool_tTMVA::MethodBase::IgnoreEventsWithNegWeightsInTraining() const
virtual voidInit()
voidInitEventSample()
voidInitMonitorNtuple()
Bool_tTMVA::MethodBase::IsConstructedFromWeightFile() const
Bool_tTMVA::MethodBase::IsNormalised() const
TMVA::MsgLogger&TMVA::Configurable::Log() const
Bool_tTMVA::Configurable::LooseOptionCheckingEnabled() const
voidMakeClassLinear(ostream&) const
voidMakeClassRuleCuts(ostream&) const
virtual voidMakeClassSpecific(ostream&, const TString&) const
virtual voidTMVA::MethodBase::MakeClassSpecificHeader(ostream&, const TString& = "") const
voidTObject::MakeZombie()
voidTMVA::MethodBase::NoErrorCalc(Double_t *const err, Double_t *const errUpper)
voidTMVA::Configurable::ResetSetFlag()
voidTMVA::MethodBase::SetNormalised(Bool_t norm)
voidTMVA::MethodBase::SetWeightFileDir(TString fileDir)
voidTMVA::MethodBase::SetWeightFileName(TString)
voidTMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&)
voidTrainJFRuleFit()
voidTrainTMVARuleFit()
Bool_tTMVA::MethodBase::TxtWeightsOnly() const
Bool_tTMVA::MethodBase::Verbose() const
voidTMVA::Configurable::WriteOptionsReferenceToFile()
private:
virtual voidDeclareOptions()
virtual voidProcessOptions()

Data Members

public:
enum TMVA::MethodBase::EWeightFileType { kROOT
kTEXT
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
public:
Bool_tTMVA::MethodBase::fSetupCompletedis method setup
const TMVA::Event*TMVA::MethodBase::fTmpEvent! temporary event when testing on a different DataSet than the own one
protected:
TMVA::Types::EAnalysisTypeTMVA::MethodBase::fAnalysisTypemethod-mode : true --> regression, false --> classification
UInt_tTMVA::MethodBase::fBackgroundClassindex of the Background-class
vector<TString>*TMVA::MethodBase::fInputVarsvector of input variables used in MVA
vector<Float_t>*TMVA::MethodBase::fMulticlassReturnValholds the return-values for the multiclass classification
Int_tTMVA::MethodBase::fNbinsnumber of bins in input variable histograms
Int_tTMVA::MethodBase::fNbinsHnumber of bins in evaluation histograms
Int_tTMVA::MethodBase::fNbinsMVAoutputnumber of bins in MVA output histograms
TMVA::Ranking*TMVA::MethodBase::fRankingpointer to ranking object (created by derived classifiers)
vector<Float_t>*TMVA::MethodBase::fRegressionReturnValholds the return-values for the regression
UInt_tTMVA::MethodBase::fSignalClassindex of the Signal-class
private:
vector<TMVA::Event*>fEventSamplethe complete training sample
vector<TMVA::DecisionTree*,allocator<TMVA::DecisionTree*> >fForestthe forest
TStringfForestTypeSforest generation: how the trees are generated
Double_tfGDErrScaleGD path: stop
Int_tfGDNPathStepsGD path: number of steps
Double_tfGDPathEveFracGD path: fraction of subsamples used for the fitting
Double_tfGDPathStepGD path: step size in path
Double_tfGDTauGD path: def threshhold fraction [0..1]
Double_tfGDTauMaxGD path: max threshhold fraction [0..1]
Double_tfGDTauMinGD path: min threshhold fraction [0..1]
Double_tfGDTauPrecGD path: precision of estimated tau
UInt_tfGDTauScanGD path: number of points to scan
Double_tfGDValidEveFracGD path: fraction of subsamples used for the fitting
Double_tfLinQuantilequantile cut to remove outliers - see RuleEnsemble
Double_tfMaxFracNEveditto max
Double_tfMinFracNEvemin fraction of number events
Double_tfMinimprule/linear: minimum importance
TStringfModelTypeSrule ensemble: which model (rule,linear or both)
TTree*fMonitorNtuplepointer to monitor rule ntuple
Int_tfNCutsgrid used in cut applied in node splitting
Double_tfNTCoefficientntuple: rule coefficient
Double_tfNTImportancentuple: rule importance
Int_tfNTNcutsntuple: rule number of cuts
Int_tfNTNvarsntuple: rule number of vars
Double_tfNTPbbntuple: rule P(tag b, true b)
Double_tfNTPbsntuple: rule P(tag b, true s)
Double_tfNTPsbntuple: rule P(tag s, true b)
Double_tfNTPssntuple: rule P(tag s, true s)
Double_tfNTPtagntuple: rule P(tag)
Double_tfNTSSBntuple: rule S/(S+B)
Double_tfNTSupportntuple: rule support
Int_tfNTTypentuple: rule type (+1->signal, -1->bkg)
Int_tfNTreesnumber of trees in forest
TMVA::DecisionTree::EPruneMethodfPruneMethodforest generation: method used for pruning - see DecisionTree
TStringfPruneMethodSforest generation: prune method - see DecisionTree
Double_tfPruneStrengthforest generation: prune strength - see DecisionTree
Int_tfRFNendnodesmax number of rules (only Friedmans module)
Int_tfRFNrulesmax number of rules (only Friedmans module)
TStringfRFWorkDirworking directory from Friedmans module
TMVA::RuleFitfRuleFitRuleFit instance
TStringfRuleFitModuleSwhich rulefit module to use
Double_tfRuleMinDistrule min distance - see RuleEnsemble
TMVA::SeparationBase*fSepTypethe separation used in node splitting
TStringfSepTypeSforest generation: separation type - see DecisionTree
Double_tfSignalFractionscalefactor for bkg events to modify initial s/b fraction in training data
Double_tfTreeEveFracfraction of events used for traing each tree
Bool_tfUseBoostuse boosted events for forest generation
Bool_tfUseRuleFitJFif true interface with J.Friedmans RuleFit module

Class Charts

Inheritance Inherited Members Includes Libraries
Class Charts

Function documentation

MethodRuleFit(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption = "", TDirectory* theTargetDir = 0)
 standard constructor
MethodRuleFit(TMVA::DataSetInfo& theData, const TString& theWeightFile, TDirectory* theTargetDir = NULL)
 constructor from weight file
~MethodRuleFit( void )
 destructor
Bool_t HasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t )
 RuleFit can handle classification with 2 classes
void DeclareOptions()
 define the options (their key words) that can be set in the option string
 know options.

 general

 RuleFitModule  <string>
    available values are:    RFTMVA      - use TMVA implementation
                             RFFriedman  - use Friedmans original implementation

 Path search (fitting)

 GDTau          <float>      gradient-directed path: fit threshhold, default
 GDTauPrec      <float>      gradient-directed path: precision of estimated tau
 GDStep         <float>      gradient-directed path: step size
 GDNSteps       <float>      gradient-directed path: number of steps
 GDErrScale     <float>      stop scan when error>scale*errmin

 Tree generation

 fEventsMin     <float>      minimum fraction of events in a splittable node
 fEventsMax     <float>      maximum fraction of events in a splittable node
 nTrees         <float>      number of trees in forest.
 ForestType     <string>
    available values are:    Random    - create forest using random subsample and only random variables subset at each node
                             AdaBoost  - create forest with boosted events


 Model creation

 RuleMinDist    <float>      min distance allowed between rules
 MinImp         <float>      minimum rule importance accepted
 Model          <string>     model to be used
    available values are:    ModRuleLinear <default>
                             ModRule
                             ModLinear


 Friedmans module

 RFWorkDir      <string>     directory where Friedmans module (rf_go.exe) is installed
 RFNrules       <int>        maximum number of rules allowed
 RFNendnodes    <int>        average number of end nodes in the forest of trees

void ProcessOptions()
 process the options specified by the user
void InitMonitorNtuple()
 initialize the monitoring ntuple
void Init()
 default initialization
void InitEventSample( void )
 write all Events from the Tree into a vector of Events, that are
 more easily manipulated.
 This method should never be called without existing trainingTree, as it
 the vector of events from the ROOT training tree
void Train( void )
void TrainTMVARuleFit( void )
 training of rules using TMVA implementation
void TrainJFRuleFit( void )
 training of rules using Jerome Friedmans implementation
const TMVA::Ranking* CreateRanking()
 computes ranking of input variables
void AddWeightsXMLTo(void* parent) const
 add the rules to XML node
void ReadWeightsFromStream(istream& istr)
 read rules from an istream
void ReadWeightsFromXML(void* wghtnode)
 read rules from XML node
Double_t GetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)
 returns MVA value for given event
void WriteMonitoringHistosToFile( void )
 write special monitoring histograms to file (here ntuple)
void MakeClassSpecific(ostream& , const TString& ) const
 write specific classifier response
void MakeClassRuleCuts(ostream& ) const
 print out the rule cuts
void MakeClassLinear(ostream& ) const
 print out the linear terms
void GetHelpMessage() const
 get help message text

 typical length of text line:
         "|--------------------------------------------------------------|"
Bool_t UseBoost() const
{ return fUseBoost; }
RuleFit* GetRuleFitPtr()
 accessors
{ return &fRuleFit; }
const RuleFit* GetRuleFitConstPtr() const
{ return &fRuleFit; }
TDirectory* GetMethodBaseDir() const
{ return BaseDir(); }
const std::vector<TMVA::Event*>& GetTrainingEvents() const
{ return fEventSample; }
const std::vector<TMVA::DecisionTree*>& GetForest() const
{ return fForest; }
Int_t GetNTrees() const
{ return fNTrees; }
Double_t GetTreeEveFrac() const
{ return fTreeEveFrac; }
const SeparationBase* GetSeparationBaseConst() const
{ return fSepType; }
SeparationBase* GetSeparationBase() const
{ return fSepType; }
TMVA::DecisionTree::EPruneMethod GetPruneMethod() const
{ return fPruneMethod; }
Double_t GetPruneStrength() const
{ return fPruneStrength; }
Double_t GetMinFracNEve() const
{ return fMinFracNEve; }
Double_t GetMaxFracNEve() const
{ return fMaxFracNEve; }
Int_t GetNCuts() const
{ return fNCuts; }
Int_t GetGDNPathSteps() const
{ return fGDNPathSteps; }
Double_t GetGDPathStep() const
{ return fGDPathStep; }
Double_t GetGDErrScale() const
{ return fGDErrScale; }
Double_t GetGDPathEveFrac() const
{ return fGDPathEveFrac; }
Double_t GetGDValidEveFrac() const
{ return fGDValidEveFrac; }
Double_t GetLinQuantile() const
{ return fLinQuantile; }
const TString GetRFWorkDir() const
{ return fRFWorkDir; }
Int_t GetRFNrules() const
{ return fRFNrules; }
Int_t GetRFNendnodes() const
{ return fRFNendnodes; }