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Reference Guide
MethodRuleFit.h
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1 // @(#)root/tmva $Id$
2 // Author: Fredrik Tegenfeldt
3 
4 /**********************************************************************************
5  * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6  * Package: TMVA *
7  * Class : MethodRuleFit *
8  * Web : http://tmva.sourceforge.net *
9  * *
10  * Description: *
11  * Friedman's RuleFit method *
12  * *
13  * Authors (alphabetical): *
14  * Fredrik Tegenfeldt <Fredrik.Tegenfeldt@cern.ch> - Iowa State U., USA *
15  * *
16  * Copyright (c) 2005: *
17  * CERN, Switzerland *
18  * Iowa State U. *
19  * MPI-K Heidelberg, Germany *
20  * *
21  * Redistribution and use in source and binary forms, with or without *
22  * modification, are permitted according to the terms listed in LICENSE *
23  * *
24  **********************************************************************************/
25 
26 #ifndef ROOT_TMVA_MethodRuleFit
27 #define ROOT_TMVA_MethodRuleFit
28 
29 //////////////////////////////////////////////////////////////////////////
30 // //
31 // MethodRuleFit //
32 // //
33 // J Friedman's RuleFit method //
34 // //
35 //////////////////////////////////////////////////////////////////////////
36 
37 #include "TMVA/MethodBase.h"
38 #include "TMatrixDfwd.h"
39 #include "TVectorD.h"
40 #include "TMVA/DecisionTree.h"
41 #include "TMVA/RuleFit.h"
42 #include <vector>
43 
44 namespace TMVA {
45 
46  class SeparationBase;
47 
48  class MethodRuleFit : public MethodBase {
49 
50  public:
51 
52  MethodRuleFit( const TString& jobName,
53  const TString& methodTitle,
54  DataSetInfo& theData,
55  const TString& theOption = "");
56 
57  MethodRuleFit( DataSetInfo& theData,
58  const TString& theWeightFile);
59 
60  virtual ~MethodRuleFit( void );
61 
62  virtual Bool_t HasAnalysisType( Types::EAnalysisType type, UInt_t numberClasses, UInt_t /*numberTargets*/ );
63 
64  // training method
65  void Train( void );
66 
68 
69  // write weights to file
70  void AddWeightsXMLTo ( void* parent ) const;
71 
72  // read weights from file
73  void ReadWeightsFromStream( std::istream& istr );
74  void ReadWeightsFromXML ( void* wghtnode );
75 
76  // calculate the MVA value
77  Double_t GetMvaValue( Double_t* err = 0, Double_t* errUpper = 0 );
78 
79  // write method specific histos to target file
80  void WriteMonitoringHistosToFile( void ) const;
81 
82  // ranking of input variables
83  const Ranking* CreateRanking();
84 
85  Bool_t UseBoost() const { return fUseBoost; }
86 
87  // accessors
88  RuleFit* GetRuleFitPtr() { return &fRuleFit; }
89  const RuleFit* GetRuleFitConstPtr() const { return &fRuleFit; }
90  TDirectory* GetMethodBaseDir() const { return BaseDir(); }
91  const std::vector<TMVA::Event*>& GetTrainingEvents() const { return fEventSample; }
92  const std::vector<TMVA::DecisionTree*>& GetForest() const { return fForest; }
93  Int_t GetNTrees() const { return fNTrees; }
94  Double_t GetTreeEveFrac() const { return fTreeEveFrac; }
95  const SeparationBase* GetSeparationBaseConst() const { return fSepType; }
99  Double_t GetMinFracNEve() const { return fMinFracNEve; }
101  Int_t GetNCuts() const { return fNCuts; }
102  //
103  Int_t GetGDNPathSteps() const { return fGDNPathSteps; }
104  Double_t GetGDPathStep() const { return fGDPathStep; }
105  Double_t GetGDErrScale() const { return fGDErrScale; }
108  //
110 
111  const TString GetRFWorkDir() const { return fRFWorkDir; }
112  Int_t GetRFNrules() const { return fRFNrules; }
113  Int_t GetRFNendnodes() const { return fRFNendnodes; }
114 
115  protected:
116 
117  // make ROOT-independent C++ class for classifier response (classifier-specific implementation)
118  void MakeClassSpecific( std::ostream&, const TString& ) const;
119 
120  void MakeClassRuleCuts( std::ostream& ) const;
121 
122  void MakeClassLinear( std::ostream& ) const;
123 
124  // get help message text
125  void GetHelpMessage() const;
126 
127  // initialize rulefit
128  void Init( void );
129 
130  // copy all training events into a stl::vector
131  void InitEventSample( void );
132 
133  // initialize monitor ntuple
134  void InitMonitorNtuple();
135 
136  void TrainTMVARuleFit();
137  void TrainJFRuleFit();
138 
139  private:
140 
141  // check variable range and set var to lower or upper if out of range
142  template<typename T>
143  inline Bool_t VerifyRange( MsgLogger& mlog, const char *varstr, T& var, const T& vmin, const T& vmax );
144 
145  template<typename T>
146  inline Bool_t VerifyRange( MsgLogger& mlog, const char *varstr, T& var, const T& vmin, const T& vmax, const T& vdef );
147 
148  template<typename T>
149  inline Int_t VerifyRange( const T& var, const T& vmin, const T& vmax );
150 
151  // the option handling methods
152  void DeclareOptions();
153  void ProcessOptions();
154 
155  RuleFit fRuleFit; // RuleFit instance
156  std::vector<TMVA::Event *> fEventSample; // the complete training sample
157  Double_t fSignalFraction; // scalefactor for bkg events to modify initial s/b fraction in training data
158 
159  // ntuple
160  TTree *fMonitorNtuple; // pointer to monitor rule ntuple
161  Double_t fNTImportance; // ntuple: rule importance
162  Double_t fNTCoefficient; // ntuple: rule coefficient
163  Double_t fNTSupport; // ntuple: rule support
164  Int_t fNTNcuts; // ntuple: rule number of cuts
165  Int_t fNTNvars; // ntuple: rule number of vars
166  Double_t fNTPtag; // ntuple: rule P(tag)
167  Double_t fNTPss; // ntuple: rule P(tag s, true s)
168  Double_t fNTPsb; // ntuple: rule P(tag s, true b)
169  Double_t fNTPbs; // ntuple: rule P(tag b, true s)
170  Double_t fNTPbb; // ntuple: rule P(tag b, true b)
171  Double_t fNTSSB; // ntuple: rule S/(S+B)
172  Int_t fNTType; // ntuple: rule type (+1->signal, -1->bkg)
173 
174  // options
175  TString fRuleFitModuleS;// which rulefit module to use
176  Bool_t fUseRuleFitJF; // if true interface with J.Friedmans RuleFit module
177  TString fRFWorkDir; // working directory from Friedmans module
178  Int_t fRFNrules; // max number of rules (only Friedmans module)
179  Int_t fRFNendnodes; // max number of rules (only Friedmans module)
180  std::vector<DecisionTree *> fForest; // the forest
181  Int_t fNTrees; // number of trees in forest
182  Double_t fTreeEveFrac; // fraction of events used for training each tree
183  SeparationBase *fSepType; // the separation used in node splitting
184  Double_t fMinFracNEve; // min fraction of number events
185  Double_t fMaxFracNEve; // ditto max
186  Int_t fNCuts; // grid used in cut applied in node splitting
187  TString fSepTypeS; // forest generation: separation type - see DecisionTree
188  TString fPruneMethodS; // forest generation: prune method - see DecisionTree
189  TMVA::DecisionTree::EPruneMethod fPruneMethod; // forest generation: method used for pruning - see DecisionTree
190  Double_t fPruneStrength; // forest generation: prune strength - see DecisionTree
191  TString fForestTypeS; // forest generation: how the trees are generated
192  Bool_t fUseBoost; // use boosted events for forest generation
193  //
194  Double_t fGDPathEveFrac; // GD path: fraction of subsamples used for the fitting
195  Double_t fGDValidEveFrac; // GD path: fraction of subsamples used for the fitting
196  Double_t fGDTau; // GD path: def threshold fraction [0..1]
197  Double_t fGDTauPrec; // GD path: precision of estimated tau
198  Double_t fGDTauMin; // GD path: min threshold fraction [0..1]
199  Double_t fGDTauMax; // GD path: max threshold fraction [0..1]
200  UInt_t fGDTauScan; // GD path: number of points to scan
201  Double_t fGDPathStep; // GD path: step size in path
202  Int_t fGDNPathSteps; // GD path: number of steps
203  Double_t fGDErrScale; // GD path: stop
204  Double_t fMinimp; // rule/linear: minimum importance
205  //
206  TString fModelTypeS; // rule ensemble: which model (rule,linear or both)
207  Double_t fRuleMinDist; // rule min distance - see RuleEnsemble
208  Double_t fLinQuantile; // quantile cut to remove outliers - see RuleEnsemble
209 
210  ClassDef(MethodRuleFit,0); // Friedman's RuleFit method
211  };
212 
213 } // namespace TMVA
214 
215 
216 //_______________________________________________________________________
217 template<typename T>
218 inline Int_t TMVA::MethodRuleFit::VerifyRange( const T& var, const T& vmin, const T& vmax )
219 {
220  // check range and return +1 if above, -1 if below or 0 if inside
221  if (var>vmax) return 1;
222  if (var<vmin) return -1;
223  return 0;
224 }
225 
226 //_______________________________________________________________________
227 template<typename T>
228 inline Bool_t TMVA::MethodRuleFit::VerifyRange( TMVA::MsgLogger& mlog, const char *varstr, T& var, const T& vmin, const T& vmax )
229 {
230  // verify range and print out message
231  // if outside range, set to closest limit
232  Int_t dir = TMVA::MethodRuleFit::VerifyRange(var,vmin,vmax);
233  Bool_t modif=kFALSE;
234  if (dir==1) {
235  modif = kTRUE;
236  var=vmax;
237  }
238  if (dir==-1) {
239  modif = kTRUE;
240  var=vmin;
241  }
242  if (modif) {
243  mlog << kWARNING << "Option <" << varstr << "> " << (dir==1 ? "above":"below") << " allowed range. Reset to new value = " << var << Endl;
244  }
245  return modif;
246 }
247 
248 //_______________________________________________________________________
249 template<typename T>
250 inline Bool_t TMVA::MethodRuleFit::VerifyRange( TMVA::MsgLogger& mlog, const char *varstr, T& var, const T& vmin, const T& vmax, const T& vdef )
251 {
252  // verify range and print out message
253  // if outside range, set to given default value
254  Int_t dir = TMVA::MethodRuleFit::VerifyRange(var,vmin,vmax);
255  Bool_t modif=kFALSE;
256  if (dir!=0) {
257  modif = kTRUE;
258  var=vdef;
259  }
260  if (modif) {
261  mlog << kWARNING << "Option <" << varstr << "> " << (dir==1 ? "above":"below") << " allowed range. Reset to default value = " << var << Endl;
262  }
263  return modif;
264 }
265 
266 
267 #endif // MethodRuleFit_H
TMatrixDfwd.h
TMVA::MethodRuleFit::Init
void Init(void)
default initialization
Definition: MethodRuleFit.cxx:396
TMVA::MethodRuleFit::HasAnalysisType
virtual Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
RuleFit can handle classification with 2 classes.
Definition: MethodRuleFit.cxx:177
TMVA::MethodRuleFit::fGDPathEveFrac
Double_t fGDPathEveFrac
Definition: MethodRuleFit.h:194
TMVA::MethodRuleFit::GetMinFracNEve
Double_t GetMinFracNEve() const
Definition: MethodRuleFit.h:99
TMVA::MethodBase::BaseDir
TDirectory * BaseDir() const
returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are sto...
Definition: MethodBase.cxx:1980
TMVA::MethodRuleFit::GetForest
const std::vector< TMVA::DecisionTree * > & GetForest() const
Definition: MethodRuleFit.h:92
TMVA::MethodRuleFit::fNTrees
Int_t fNTrees
Definition: MethodRuleFit.h:181
TMVA::MethodRuleFit::GetSeparationBaseConst
const SeparationBase * GetSeparationBaseConst() const
Definition: MethodRuleFit.h:95
TMVA::MethodRuleFit::VerifyRange
Bool_t VerifyRange(MsgLogger &mlog, const char *varstr, T &var, const T &vmin, const T &vmax)
Definition: MethodRuleFit.h:228
TMVA::RuleFit
A class implementing various fits of rule ensembles.
Definition: RuleFit.h:46
kTRUE
const Bool_t kTRUE
Definition: RtypesCore.h:100
TMVA::MethodRuleFit::fEventSample
std::vector< TMVA::Event * > fEventSample
Definition: MethodRuleFit.h:156
TVectorD.h
TMVA::MethodRuleFit::fRFNrules
Int_t fRFNrules
Definition: MethodRuleFit.h:178
TMVA::DecisionTree::EPruneMethod
EPruneMethod
Definition: DecisionTree.h:139
TMVA::MethodRuleFit::WriteMonitoringHistosToFile
void WriteMonitoringHistosToFile(void) const
write special monitoring histograms to file (here ntuple)
Definition: MethodRuleFit.cxx:628
TMVA::MethodRuleFit::fGDNPathSteps
Int_t fGDNPathSteps
Definition: MethodRuleFit.h:202
TMVA::MethodBase::ReadWeightsFromStream
virtual void ReadWeightsFromStream(std::istream &)=0
TMVA::MethodRuleFit::InitMonitorNtuple
void InitMonitorNtuple()
initialize the monitoring ntuple
Definition: MethodRuleFit.cxx:375
TMVA::MethodRuleFit::GetGDErrScale
Double_t GetGDErrScale() const
Definition: MethodRuleFit.h:105
TMVA::Ranking
Ranking for variables in method (implementation)
Definition: Ranking.h:48
TMVA::MethodRuleFit::GetRFNrules
Int_t GetRFNrules() const
Definition: MethodRuleFit.h:112
TMVA::MethodRuleFit::fNTNvars
Int_t fNTNvars
Definition: MethodRuleFit.h:165
TMVA::MethodRuleFit
J Friedman's RuleFit method.
Definition: MethodRuleFit.h:48
TMVA::MethodRuleFit::DeclareOptions
void DeclareOptions()
define the options (their key words) that can be set in the option string know options.
Definition: MethodRuleFit.cxx:228
RuleFit.h
TMVA::MethodRuleFit::MakeClassSpecific
void MakeClassSpecific(std::ostream &, const TString &) const
write specific classifier response
Definition: MethodRuleFit.cxx:638
TTree
A TTree represents a columnar dataset.
Definition: TTree.h:79
TMVA::MethodRuleFit::fGDTau
Double_t fGDTau
Definition: MethodRuleFit.h:196
TMVA::MethodRuleFit::GetRuleFitConstPtr
const RuleFit * GetRuleFitConstPtr() const
Definition: MethodRuleFit.h:89
TMVA::MethodRuleFit::GetPruneMethod
TMVA::DecisionTree::EPruneMethod GetPruneMethod() const
Definition: MethodRuleFit.h:97
TMVA::MethodRuleFit::fGDTauMin
Double_t fGDTauMin
Definition: MethodRuleFit.h:198
TMVA::MethodRuleFit::fMinimp
Double_t fMinimp
Definition: MethodRuleFit.h:204
TMVA::MethodRuleFit::GetSeparationBase
SeparationBase * GetSeparationBase() const
Definition: MethodRuleFit.h:96
TMVA::MethodRuleFit::fGDErrScale
Double_t fGDErrScale
Definition: MethodRuleFit.h:203
TMVA::MethodRuleFit::fNTPtag
Double_t fNTPtag
Definition: MethodRuleFit.h:166
TMVA::MethodRuleFit::UseBoost
Bool_t UseBoost() const
Definition: MethodRuleFit.h:85
TMVA::MethodRuleFit::MethodRuleFit
MethodRuleFit(const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="")
standard constructor
Definition: MethodRuleFit.cxx:70
MethodBase.h
TMVA::MethodRuleFit::GetLinQuantile
Double_t GetLinQuantile() const
Definition: MethodRuleFit.h:109
TMVA::MethodRuleFit::fPruneStrength
Double_t fPruneStrength
Definition: MethodRuleFit.h:190
TMVA::MethodRuleFit::GetRFWorkDir
const TString GetRFWorkDir() const
Definition: MethodRuleFit.h:111
TMVA::MethodRuleFit::GetTrainingEvents
const std::vector< TMVA::Event * > & GetTrainingEvents() const
Definition: MethodRuleFit.h:91
TMVA::MethodRuleFit::fMaxFracNEve
Double_t fMaxFracNEve
Definition: MethodRuleFit.h:185
TString
Basic string class.
Definition: TString.h:136
Bool_t
bool Bool_t
Definition: RtypesCore.h:63
TMVA::MethodRuleFit::ProcessOptions
void ProcessOptions()
process the options specified by the user
Definition: MethodRuleFit.cxx:266
TMVA::MethodRuleFit::fLinQuantile
Double_t fLinQuantile
Definition: MethodRuleFit.h:208
TMVA::MethodRuleFit::fNTSupport
Double_t fNTSupport
Definition: MethodRuleFit.h:163
bool
TMVA::MethodRuleFit::ReadWeightsFromStream
virtual void ReadWeightsFromStream(std::istream &)=0
TMVA::MethodRuleFit::fRFNendnodes
Int_t fRFNendnodes
Definition: MethodRuleFit.h:179
TMVA::MethodRuleFit::fNTPbb
Double_t fNTPbb
Definition: MethodRuleFit.h:170
TMVA::MethodRuleFit::fGDPathStep
Double_t fGDPathStep
Definition: MethodRuleFit.h:201
TMVA::DataSetInfo
Class that contains all the data information.
Definition: DataSetInfo.h:62
TMVA::MethodRuleFit::fNTPsb
Double_t fNTPsb
Definition: MethodRuleFit.h:168
TMVA::MethodRuleFit::fSepTypeS
TString fSepTypeS
Definition: MethodRuleFit.h:187
TMVA::MethodRuleFit::GetMaxFracNEve
Double_t GetMaxFracNEve() const
Definition: MethodRuleFit.h:100
DecisionTree.h
TMVA::MethodRuleFit::MakeClassLinear
void MakeClassLinear(std::ostream &) const
print out the linear terms
Definition: MethodRuleFit.cxx:715
TMVA::MethodRuleFit::fNTCoefficient
Double_t fNTCoefficient
Definition: MethodRuleFit.h:162
TMVA::Types::EAnalysisType
EAnalysisType
Definition: Types.h:128
TMVA::MethodRuleFit::fForest
std::vector< DecisionTree * > fForest
Definition: MethodRuleFit.h:180
TMVA::MethodRuleFit::fNTPss
Double_t fNTPss
Definition: MethodRuleFit.h:167
TMVA::MethodRuleFit::fGDValidEveFrac
Double_t fGDValidEveFrac
Definition: MethodRuleFit.h:195
TMVA::MethodRuleFit::GetPruneStrength
Double_t GetPruneStrength() const
Definition: MethodRuleFit.h:98
TMVA::MethodRuleFit::ReadWeightsFromXML
void ReadWeightsFromXML(void *wghtnode)
read rules from XML node
Definition: MethodRuleFit.cxx:609
TMVA::MethodRuleFit::~MethodRuleFit
virtual ~MethodRuleFit(void)
destructor
Definition: MethodRuleFit.cxx:168
TMVA::MethodRuleFit::GetHelpMessage
void GetHelpMessage() const
get help message text
Definition: MethodRuleFit.cxx:751
TMVA::MethodRuleFit::fNTType
Int_t fNTType
Definition: MethodRuleFit.h:172
kFALSE
const Bool_t kFALSE
Definition: RtypesCore.h:101
TMVA::MethodRuleFit::CreateRanking
const Ranking * CreateRanking()
computes ranking of input variables
Definition: MethodRuleFit.cxx:578
TMVA::MethodRuleFit::GetRFNendnodes
Int_t GetRFNendnodes() const
Definition: MethodRuleFit.h:113
TMVA::MethodRuleFit::AddWeightsXMLTo
void AddWeightsXMLTo(void *parent) const
add the rules to XML node
Definition: MethodRuleFit.cxx:593
TMVA::MethodRuleFit::Train
void Train(void)
Definition: MethodRuleFit.cxx:443
TMVA::MethodRuleFit::fForestTypeS
TString fForestTypeS
Definition: MethodRuleFit.h:191
TMVA::MethodRuleFit::GetGDValidEveFrac
Double_t GetGDValidEveFrac() const
Definition: MethodRuleFit.h:107
TMVA::MethodBase
Virtual base Class for all MVA method.
Definition: MethodBase.h:111
TMVA::MethodRuleFit::fSepType
SeparationBase * fSepType
Definition: MethodRuleFit.h:183
TMVA::MethodRuleFit::fGDTauMax
Double_t fGDTauMax
Definition: MethodRuleFit.h:199
TMVA::MethodRuleFit::fPruneMethod
TMVA::DecisionTree::EPruneMethod fPruneMethod
Definition: MethodRuleFit.h:189
TMVA::MethodRuleFit::GetNCuts
Int_t GetNCuts() const
Definition: MethodRuleFit.h:101
TMVA::MethodRuleFit::fRuleFit
RuleFit fRuleFit
Definition: MethodRuleFit.h:155
TMVA::MethodRuleFit::fPruneMethodS
TString fPruneMethodS
Definition: MethodRuleFit.h:188
TMVA::MethodRuleFit::fUseRuleFitJF
Bool_t fUseRuleFitJF
Definition: MethodRuleFit.h:176
TMVA::Endl
MsgLogger & Endl(MsgLogger &ml)
Definition: MsgLogger.h:158
unsigned int
TMVA::MethodRuleFit::fNCuts
Int_t fNCuts
Definition: MethodRuleFit.h:186
TMVA::MethodRuleFit::InitEventSample
void InitEventSample(void)
write all Events from the Tree into a vector of Events, that are more easily manipulated.
Definition: MethodRuleFit.cxx:421
TMVA::MethodRuleFit::MakeClassRuleCuts
void MakeClassRuleCuts(std::ostream &) const
print out the rule cuts
Definition: MethodRuleFit.cxx:657
TMVA::MethodRuleFit::GetRuleFitPtr
RuleFit * GetRuleFitPtr()
Definition: MethodRuleFit.h:88
TMVA::MethodRuleFit::fMonitorNtuple
TTree * fMonitorNtuple
Definition: MethodRuleFit.h:160
TMVA::SeparationBase
An interface to calculate the "SeparationGain" for different separation criteria used in various trai...
Definition: SeparationBase.h:82
TMVA::MethodRuleFit::fRFWorkDir
TString fRFWorkDir
Definition: MethodRuleFit.h:177
TMVA::MethodRuleFit::fRuleFitModuleS
TString fRuleFitModuleS
Definition: MethodRuleFit.h:175
TMVA::MethodRuleFit::fNTPbs
Double_t fNTPbs
Definition: MethodRuleFit.h:169
TMVA::MethodRuleFit::fNTImportance
Double_t fNTImportance
Definition: MethodRuleFit.h:161
TMVA::MethodRuleFit::TrainJFRuleFit
void TrainJFRuleFit()
training of rules using Jerome Friedmans implementation
Definition: MethodRuleFit.cxx:535
Double_t
double Double_t
Definition: RtypesCore.h:59
TMVA::MsgLogger
ostringstream derivative to redirect and format output
Definition: MsgLogger.h:59
TMVA::MethodRuleFit::GetNTrees
Int_t GetNTrees() const
Definition: MethodRuleFit.h:93
TMVA::MethodRuleFit::fNTSSB
Double_t fNTSSB
Definition: MethodRuleFit.h:171
TMVA::MethodRuleFit::fTreeEveFrac
Double_t fTreeEveFrac
Definition: MethodRuleFit.h:182
TMVA::MethodRuleFit::fMinFracNEve
Double_t fMinFracNEve
Definition: MethodRuleFit.h:184
ClassDef
#define ClassDef(name, id)
Definition: Rtypes.h:325
TMVA::MethodRuleFit::GetMethodBaseDir
TDirectory * GetMethodBaseDir() const
Definition: MethodRuleFit.h:90
TMVA::MethodRuleFit::fNTNcuts
Int_t fNTNcuts
Definition: MethodRuleFit.h:164
TMVA::MethodRuleFit::fGDTauScan
UInt_t fGDTauScan
Definition: MethodRuleFit.h:200
ROOT::Math::Chebyshev::T
double T(double x)
Definition: ChebyshevPol.h:34
TDirectory
Describe directory structure in memory.
Definition: TDirectory.h:45
TMVA::MethodRuleFit::GetGDPathEveFrac
Double_t GetGDPathEveFrac() const
Definition: MethodRuleFit.h:106
TMVA::MethodRuleFit::GetGDPathStep
Double_t GetGDPathStep() const
Definition: MethodRuleFit.h:104
TMVA::MethodRuleFit::GetTreeEveFrac
Double_t GetTreeEveFrac() const
Definition: MethodRuleFit.h:94
TMVA::MethodRuleFit::fModelTypeS
TString fModelTypeS
Definition: MethodRuleFit.h:206
type
int type
Definition: TGX11.cxx:121
TMVA::MethodRuleFit::TrainTMVARuleFit
void TrainTMVARuleFit()
training of rules using TMVA implementation
Definition: MethodRuleFit.cxx:467
TMVA::MethodRuleFit::fGDTauPrec
Double_t fGDTauPrec
Definition: MethodRuleFit.h:197
TMVA::MethodRuleFit::fUseBoost
Bool_t fUseBoost
Definition: MethodRuleFit.h:192
TMVA::kWARNING
@ kWARNING
Definition: Types.h:61
TMVA
create variable transformations
Definition: GeneticMinimizer.h:22
TMVA::MethodRuleFit::GetGDNPathSteps
Int_t GetGDNPathSteps() const
Definition: MethodRuleFit.h:103
int
TMVA::MethodRuleFit::fRuleMinDist
Double_t fRuleMinDist
Definition: MethodRuleFit.h:207
TMVA::MethodRuleFit::fSignalFraction
Double_t fSignalFraction
Definition: MethodRuleFit.h:157
TMVA::MethodRuleFit::GetMvaValue
Double_t GetMvaValue(Double_t *err=0, Double_t *errUpper=0)
returns MVA value for given event
Definition: MethodRuleFit.cxx:617