BornAgain  1.19.79
Open-source research software to simulate and fit neutron and x-ray reflectometry and grazing-incidence small-angle scattering
Public Types | List of all members
TMVA::Types Class Reference

Description

Definition at line 73 of file Types.h.

Public Types

enum  EAnalysisType {
  kClassification = 0 , kRegression , kMulticlass , kNoAnalysisType ,
  kMaxAnalysisType
}
 
enum  EBoostStage {
  kBoostProcBegin =0 , kBeforeTraining , kBeforeBoosting , kAfterBoosting ,
  kBoostProcEnd
}
 
enum  EMVA {
  kVariable = 0 , kCuts , kLikelihood , kPDERS ,
  kHMatrix , kFisher , kKNN , kCFMlpANN ,
  kTMlpANN , kBDT , kDT , kRuleFit ,
  kSVM , kMLP , kBayesClassifier , kFDA ,
  kBoost , kPDEFoam , kLD , kPlugins ,
  kCategory , kDNN , kPyRandomForest , kPyAdaBoost ,
  kPyGTB , kPyKeras , kC50 , kRSNNS ,
  kRSVM , kRXGB , kMaxMethod
}
 
enum  ESBType {
  kSignal = 0 , kBackground , kSBBoth , kMaxSBType ,
  kTrueType
}
 
enum  ETreeType {
  kTraining = 0 , kTesting , kMaxTreeType , kValidation ,
  kTrainingOriginal
}
 
enum  EVariableTransform {
  kIdentity = 0 , kDecorrelated , kNormalized , kPCA ,
  kRearranged , kGauss , kUniform , kMaxVariableTransform
}
 

Member Enumeration Documentation

◆ EAnalysisType

enum TMVA::Types::EAnalysisType
Enumerator
kClassification 
kRegression 
kMulticlass 
kNoAnalysisType 
kMaxAnalysisType 

Definition at line 125 of file Types.h.

125  {
126  kClassification = 0,
127  kRegression,
128  kMulticlass,
129  kNoAnalysisType,
130  kMaxAnalysisType
131  };
TMVA::Types::kMulticlass
@ kMulticlass
Definition: Types.h:128
TMVA::Types::kNoAnalysisType
@ kNoAnalysisType
Definition: Types.h:129
TMVA::Types::kClassification
@ kClassification
Definition: Types.h:126
TMVA::Types::kMaxAnalysisType
@ kMaxAnalysisType
Definition: Types.h:130
TMVA::Types::kRegression
@ kRegression
Definition: Types.h:127

◆ EBoostStage

enum TMVA::Types::EBoostStage
Enumerator
kBoostProcBegin 
kBeforeTraining 
kBeforeBoosting 
kAfterBoosting 
kBoostProcEnd 

Definition at line 149 of file Types.h.

149  {
150  kBoostProcBegin=0,
151  kBeforeTraining,
152  kBeforeBoosting,
153  kAfterBoosting,
154  kBoostProcEnd
155  };
TMVA::Types::kBoostProcBegin
@ kBoostProcBegin
Definition: Types.h:150
TMVA::Types::kBeforeBoosting
@ kBeforeBoosting
Definition: Types.h:152
TMVA::Types::kAfterBoosting
@ kAfterBoosting
Definition: Types.h:153
TMVA::Types::kBoostProcEnd
@ kBoostProcEnd
Definition: Types.h:154
TMVA::Types::kBeforeTraining
@ kBeforeTraining
Definition: Types.h:151

◆ EMVA

enum TMVA::Types::EMVA
Enumerator
kVariable 
kCuts 
kLikelihood 
kPDERS 
kHMatrix 
kFisher 
kKNN 
kCFMlpANN 
kTMlpANN 
kBDT 
kDT 
kRuleFit 
kSVM 
kMLP 
kBayesClassifier 
kFDA 
kBoost 
kPDEFoam 
kLD 
kPlugins 
kCategory 
kDNN 
kPyRandomForest 
kPyAdaBoost 
kPyGTB 
kPyKeras 
kC50 
kRSNNS 
kRSVM 
kRXGB 
kMaxMethod 

Definition at line 78 of file Types.h.

78  {
79  kVariable = 0,
80  kCuts ,
81  kLikelihood ,
82  kPDERS ,
83  kHMatrix ,
84  kFisher ,
85  kKNN ,
86  kCFMlpANN ,
87  kTMlpANN ,
88  kBDT ,
89  kDT ,
90  kRuleFit ,
91  kSVM ,
92  kMLP ,
93  kBayesClassifier,
94  kFDA ,
95  kBoost ,
96  kPDEFoam ,
97  kLD ,
98  kPlugins ,
99  kCategory ,
100  kDNN ,
101  kPyRandomForest ,
102  kPyAdaBoost ,
103  kPyGTB ,
104  kPyKeras ,
105  kC50 ,
106  kRSNNS ,
107  kRSVM ,
108  kRXGB ,
109  kMaxMethod
110  };
TMVA::Types::kFisher
@ kFisher
Definition: Types.h:84
TMVA::Types::kCategory
@ kCategory
Definition: Types.h:99
TMVA::Types::kPyRandomForest
@ kPyRandomForest
Definition: Types.h:101
TMVA::Types::kPyKeras
@ kPyKeras
Definition: Types.h:104
TMVA::Types::kTMlpANN
@ kTMlpANN
Definition: Types.h:87
TMVA::Types::kFDA
@ kFDA
Definition: Types.h:94
TMVA::Types::kBDT
@ kBDT
Definition: Types.h:88
TMVA::Types::kPDERS
@ kPDERS
Definition: Types.h:82
TMVA::Types::kRSNNS
@ kRSNNS
Definition: Types.h:106
TMVA::Types::kPDEFoam
@ kPDEFoam
Definition: Types.h:96
TMVA::Types::kLikelihood
@ kLikelihood
Definition: Types.h:81
TMVA::Types::kCuts
@ kCuts
Definition: Types.h:80
TMVA::Types::kPyAdaBoost
@ kPyAdaBoost
Definition: Types.h:102
TMVA::Types::kVariable
@ kVariable
Definition: Types.h:79
TMVA::Types::kBayesClassifier
@ kBayesClassifier
Definition: Types.h:93
TMVA::Types::kHMatrix
@ kHMatrix
Definition: Types.h:83
TMVA::Types::kBoost
@ kBoost
Definition: Types.h:95
TMVA::Types::kSVM
@ kSVM
Definition: Types.h:91
TMVA::Types::kRuleFit
@ kRuleFit
Definition: Types.h:90
TMVA::Types::kCFMlpANN
@ kCFMlpANN
Definition: Types.h:86
TMVA::Types::kMaxMethod
@ kMaxMethod
Definition: Types.h:109
TMVA::Types::kPyGTB
@ kPyGTB
Definition: Types.h:103
TMVA::Types::kKNN
@ kKNN
Definition: Types.h:85
TMVA::Types::kMLP
@ kMLP
Definition: Types.h:92
TMVA::Types::kPlugins
@ kPlugins
Definition: Types.h:98

◆ ESBType

enum TMVA::Types::ESBType
Enumerator
kSignal 
kBackground 
kSBBoth 
kMaxSBType 
kTrueType 

Definition at line 133 of file Types.h.

133  {
134  kSignal = 0, // Never change this number - it is elsewhere assumed to be zero !
135  kBackground,
136  kSBBoth,
137  kMaxSBType,
138  kTrueType
139  };
TMVA::Types::kSignal
@ kSignal
Definition: Types.h:134
TMVA::Types::kTrueType
@ kTrueType
Definition: Types.h:138
TMVA::Types::kBackground
@ kBackground
Definition: Types.h:135
TMVA::Types::kSBBoth
@ kSBBoth
Definition: Types.h:136
TMVA::Types::kMaxSBType
@ kMaxSBType
Definition: Types.h:137

◆ ETreeType

enum TMVA::Types::ETreeType
Enumerator
kTraining 
kTesting 
kMaxTreeType 
kValidation 
kTrainingOriginal 

Definition at line 141 of file Types.h.

141  {
142  kTraining = 0,
143  kTesting,
144  kMaxTreeType, // also used as temporary storage for trees not yet assigned for testing;training...
145  kValidation, // these are placeholders... currently not used, but could be moved "forward" if
146  kTrainingOriginal // ever needed
147  };
TMVA::Types::kMaxTreeType
@ kMaxTreeType
Definition: Types.h:144
TMVA::Types::kTrainingOriginal
@ kTrainingOriginal
Definition: Types.h:146
TMVA::Types::kTraining
@ kTraining
Definition: Types.h:142
TMVA::Types::kValidation
@ kValidation
Definition: Types.h:145
TMVA::Types::kTesting
@ kTesting
Definition: Types.h:143

◆ EVariableTransform

enum TMVA::Types::EVariableTransform
Enumerator
kIdentity 
kDecorrelated 
kNormalized 
kPCA 
kRearranged 
kGauss 
kUniform 
kMaxVariableTransform 

Definition at line 113 of file Types.h.

113  {
114  kIdentity = 0,
115  kDecorrelated,
116  kNormalized,
117  kPCA,
118  kRearranged,
119  kGauss,
120  kUniform,
121  kMaxVariableTransform
122  };
TMVA::Types::kUniform
@ kUniform
Definition: Types.h:120
TMVA::Types::kNormalized
@ kNormalized
Definition: Types.h:116
TMVA::Types::kRearranged
@ kRearranged
Definition: Types.h:118
TMVA::Types::kGauss
@ kGauss
Definition: Types.h:119
TMVA::Types::kIdentity
@ kIdentity
Definition: Types.h:114
TMVA::Types::kMaxVariableTransform
@ kMaxVariableTransform
Definition: Types.h:121
TMVA::Types::kDecorrelated
@ kDecorrelated
Definition: Types.h:115
The documentation for this class was generated from the following file: