Logo ROOT   6.19/01
Reference Guide
Namespaces | Classes | Functions
TMVA Namespace Reference

create variable transformations More...

Namespaces

 DNN
 
 Experimental
 
 Internal
 
 kNN
 
 TMVAGlob
 

Classes

class  AbsoluteDeviationLossFunction
 Absolute Deviation Loss Function. More...
 
class  AbsoluteDeviationLossFunctionBDT
 Absolute Deviation BDT Loss Function. More...
 
class  AbsValue
 
class  BDTEventWrapper
 
class  BinarySearchTree
 A simple Binary search tree including a volume search method. More...
 
class  BinarySearchTreeNode
 Node for the BinarySearch or Decision Trees. More...
 
class  BinaryTree
 Base class for BinarySearch and Decision Trees. More...
 
class  CCPruner
 A helper class to prune a decision tree using the Cost Complexity method (see Classification and Regression Trees by Leo Breiman et al) More...
 
class  CCTreeWrapper
 
class  Classification
 Class to perform two class classification. More...
 
class  ClassificationResult
 Class to save the results of the classifier. More...
 
class  ClassifierFactory
 This is the MVA factory. More...
 
class  ClassInfo
 Class that contains all the information of a class. More...
 
class  compose_binary_t
 
class  compose_unary_t
 
class  Config
 Singleton class for global configuration settings used by TMVA. More...
 
class  Configurable
 
class  ConvergenceTest
 Check for convergence. More...
 
class  CostComplexityPruneTool
 A class to prune a decision tree using the Cost Complexity method. More...
 
class  CrossEntropy
 Implementation of the CrossEntropy as separation criterion. More...
 
class  CrossValidation
 Class to perform cross validation, splitting the dataloader into folds. More...
 
class  CrossValidationFoldResult
 
class  CrossValidationResult
 Class to save the results of cross validation, the metric for the classification ins ROC and you can ROC curves ROC integrals, ROC average and ROC standard deviation. More...
 
class  CvSplit
 
class  CvSplitKFolds
 
class  CvSplitKFoldsExpr
 
class  DataInputHandler
 Class that contains all the data information. More...
 
class  DataLoader
 
class  DataSet
 Class that contains all the data information. More...
 
class  DataSetFactory
 Class that contains all the data information. More...
 
class  DataSetInfo
 Class that contains all the data information. More...
 
class  DataSetManager
 Class that contains all the data information. More...
 
class  DecisionTree
 Implementation of a Decision Tree. More...
 
class  DecisionTreeNode
 
struct  DeleteFunctor_t
 
class  DTNodeTrainingInfo
 
class  Envelope
 Abstract base class for all high level ml algorithms, you can book ml methods like BDT, MLP. More...
 
class  Event
 
class  Executor
 Base Excutor class. More...
 
class  ExpectedErrorPruneTool
 A helper class to prune a decision tree using the expected error (C4.5) method. More...
 
class  Factory
 This is the main MVA steering class. More...
 
class  FitterBase
 Base class for TMVA fitters. More...
 
class  GeneticAlgorithm
 Base definition for genetic algorithm. More...
 
class  GeneticFitter
 Fitter using a Genetic Algorithm. More...
 
class  GeneticGenes
 Cut optimisation interface class for genetic algorithm. More...
 
class  GeneticPopulation
 Population definition for genetic algorithm. More...
 
class  GeneticRange
 Range definition for genetic algorithm. More...
 
class  GiniIndex
 Implementation of the GiniIndex as separation criterion. More...
 
class  GiniIndexWithLaplace
 Implementation of the GiniIndex With Laplace correction as separation criterion. More...
 
class  HuberLossFunction
 Huber Loss Function. More...
 
class  HuberLossFunctionBDT
 Huber BDT Loss Function. More...
 
class  HyperParameterOptimisation
 
class  HyperParameterOptimisationResult
 
class  IFitterTarget
 Interface for a fitter 'target'. More...
 
class  IMethod
 Interface for all concrete MVA method implementations. More...
 
class  Increment
 
class  Interval
 The TMVA::Interval Class. More...
 
class  IPruneTool
 IPruneTool - a helper interface class to prune a decision tree. More...
 
class  IPythonInteractive
 This class is needed by JsMVA, and it's a helper class for tracking errors during the training in Jupyter notebook. More...
 
class  KDEKernel
 KDE Kernel for "smoothing" the PDFs. More...
 
class  LDA
 
class  LeastSquaresLossFunction
 Least Squares Loss Function. More...
 
class  LeastSquaresLossFunctionBDT
 Least Squares BDT Loss Function. More...
 
class  LogInterval
 The TMVA::Interval Class. More...
 
class  LossFunction
 
class  LossFunctionBDT
 
class  LossFunctionEventInfo
 
class  MCFitter
 Fitter using Monte Carlo sampling of parameters. More...
 
class  MethodANNBase
 Base class for all TMVA methods using artificial neural networks. More...
 
class  MethodBase
 Virtual base Class for all MVA method. More...
 
class  MethodBayesClassifier
 Description of bayesian classifiers. More...
 
class  MethodBDT
 Analysis of Boosted Decision Trees. More...
 
class  MethodBoost
 Class for boosting a TMVA method. More...
 
class  MethodC50
 
class  MethodCategory
 Class for categorizing the phase space. More...
 
class  MethodCFMlpANN
 Interface to Clermond-Ferrand artificial neural network. More...
 
class  MethodCFMlpANN_Utils
 Implementation of Clermond-Ferrand artificial neural network. More...
 
class  MethodCompositeBase
 Virtual base class for combining several TMVA method. More...
 
class  MethodCrossValidation
 
class  MethodCuts
 Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular minimum and maximum requirements. More...
 
class  MethodDL
 
class  MethodDNN
 Deep Neural Network Implementation. More...
 
class  MethodDT
 Analysis of Boosted Decision Trees. More...
 
class  MethodFDA
 Function discriminant analysis (FDA). More...
 
class  MethodFisher
 Fisher and Mahalanobis Discriminants (Linear Discriminant Analysis) More...
 
class  MethodHMatrix
 H-Matrix method, which is implemented as a simple comparison of chi-squared estimators for signal and background, taking into account the linear correlations between the input variables. More...
 
class  MethodInfo
 
class  MethodKNN
 Analysis of k-nearest neighbor. More...
 
class  MethodLD
 Linear Discriminant. More...
 
class  MethodLikelihood
 Likelihood analysis ("non-parametric approach") More...
 
class  MethodMLP
 Multilayer Perceptron class built off of MethodANNBase. More...
 
class  MethodPDEFoam
 The PDEFoam method is an extension of the PDERS method, which divides the multi-dimensional phase space in a finite number of hyper-rectangles (cells) of constant event density. More...
 
class  MethodPDERS
 This is a generalization of the above Likelihood methods to \( N_{var} \) dimensions, where \( N_{var} \) is the number of input variables used in the MVA. More...
 
class  MethodPyAdaBoost
 
class  MethodPyGTB
 
class  MethodPyKeras
 
class  MethodPyRandomForest
 
class  MethodRSNNS
 
class  MethodRSVM
 
class  MethodRuleFit
 J Friedman's RuleFit method. More...
 
class  MethodRXGB
 
class  MethodSVM
 SMO Platt's SVM classifier with Keerthi & Shavade improvements. More...
 
class  MethodTMlpANN
 This is the TMVA TMultiLayerPerceptron interface class. More...
 
class  MinuitFitter
 /Fitter using MINUIT More...
 
class  MinuitWrapper
 Wrapper around MINUIT. More...
 
class  MisClassificationError
 Implementation of the MisClassificationError as separation criterion. More...
 
class  Monitoring
 
class  MsgLogger
 ostringstream derivative to redirect and format output More...
 
class  Node
 Node for the BinarySearch or Decision Trees. More...
 
class  null_t
 
class  OptimizeConfigParameters
 
class  Option
 
class  Option< T * >
 
class  OptionBase
 Class for TMVA-option handling. More...
 
class  OptionMap
 class to storage options for the differents methods More...
 
class  PDEFoam
 Implementation of PDEFoam. More...
 
class  PDEFoamCell
 
class  PDEFoamDecisionTree
 This PDEFoam variant acts like a decision tree and stores in every cell the discriminant. More...
 
class  PDEFoamDecisionTreeDensity
 This is a concrete implementation of PDEFoam. More...
 
class  PDEFoamDensityBase
 This is an abstract class, which provides an interface for a PDEFoam density estimator. More...
 
class  PDEFoamDiscriminant
 This PDEFoam variant stores in every cell the discriminant. More...
 
class  PDEFoamDiscriminantDensity
 This is a concrete implementation of PDEFoam. More...
 
class  PDEFoamEvent
 This PDEFoam variant stores in every cell the sum of event weights and the sum of the squared event weights. More...
 
class  PDEFoamEventDensity
 This is a concrete implementation of PDEFoam. More...
 
class  PDEFoamKernelBase
 This class is the abstract kernel interface for PDEFoam. More...
 
class  PDEFoamKernelGauss
 This PDEFoam kernel estimates a cell value for a given event by weighting all cell values with a gauss function. More...
 
class  PDEFoamKernelLinN
 This PDEFoam kernel estimates a cell value for a given event by weighting with cell values of the nearest neighbor cells. More...
 
class  PDEFoamKernelTrivial
 This class is a trivial PDEFoam kernel estimator. More...
 
class  PDEFoamMultiTarget
 This PDEFoam variant is used to estimate multiple targets by creating an event density foam (PDEFoamEvent), which has dimension: More...
 
class  PDEFoamTarget
 This PDEFoam variant stores in every cell the average target fTarget (see the Constructor) as well as the statistical error on the target fTarget. More...
 
class  PDEFoamTargetDensity
 This is a concrete implementation of PDEFoam. More...
 
class  PDEFoamVect
 
class  PDF
 PDF wrapper for histograms; uses user-defined spline interpolation. More...
 
class  PruningInfo
 
class  PyMethodBase
 
class  QuickMVAProbEstimator
 
class  RandomGenerator
 
class  Rank
 
class  Ranking
 Ranking for variables in method (implementation) More...
 
class  Reader
 The Reader class serves to use the MVAs in a specific analysis context. More...
 
class  RegressionVariance
 Calculate the "SeparationGain" for Regression analysis separation criteria used in various training algorithms. More...
 
class  Results
 Class that is the base-class for a vector of result. More...
 
class  ResultsClassification
 Class that is the base-class for a vector of result. More...
 
class  ResultsMulticlass
 Class which takes the results of a multiclass classification. More...
 
class  ResultsRegression
 Class that is the base-class for a vector of result. More...
 
class  RMethodBase
 
class  ROCCalc
 
class  ROCCurve
 
class  RootFinder
 Root finding using Brents algorithm (translated from CERNLIB function RZERO) More...
 
class  Rule
 Implementation of a rule. More...
 
class  RuleCut
 A class describing a 'rule cut'. More...
 
class  RuleEnsemble
 
class  RuleFit
 A class implementing various fits of rule ensembles. More...
 
class  RuleFitAPI
 J Friedman's RuleFit method. More...
 
class  RuleFitParams
 A class doing the actual fitting of a linear model using rules as base functions. More...
 
class  SdivSqrtSplusB
 Implementation of the SdivSqrtSplusB as separation criterion. More...
 
class  SeparationBase
 An interface to calculate the "SeparationGain" for different separation criteria used in various training algorithms. More...
 
class  SimulatedAnnealing
 Base implementation of simulated annealing fitting procedure. More...
 
class  SimulatedAnnealingFitter
 Fitter using a Simulated Annealing Algorithm. More...
 
class  StatDialogBDT
 
class  StatDialogBDTReg
 
class  StatDialogMVAEffs
 
class  SVEvent
 Event class for Support Vector Machine. More...
 
class  SVKernelFunction
 Kernel for Support Vector Machine. More...
 
class  SVKernelMatrix
 Kernel matrix for Support Vector Machine. More...
 
class  SVWorkingSet
 Working class for Support Vector Machine. More...
 
class  TActivation
 Interface for TNeuron activation function classes. More...
 
class  TActivationChooser
 Class for easily choosing activation functions. More...
 
class  TActivationIdentity
 Identity activation function for TNeuron. More...
 
class  TActivationRadial
 Radial basis activation function for ANN. More...
 
class  TActivationReLU
 Rectified Linear Unit activation function for TNeuron. More...
 
class  TActivationSigmoid
 Sigmoid activation function for TNeuron. More...
 
class  TActivationTanh
 Tanh activation function for ANN. More...
 
class  Timer
 Timing information for training and evaluation of MVA methods. More...
 
class  TMVAGaussPair
 
struct  TMVAGUI
 
class  TNeuron
 Neuron class used by TMVA artificial neural network methods. More...
 
class  TNeuronInput
 Interface for TNeuron input calculation classes. More...
 
class  TNeuronInputAbs
 TNeuron input calculator – calculates the sum of the absolute values of the weighted inputs. More...
 
class  TNeuronInputChooser
 Class for easily choosing neuron input functions. More...
 
class  TNeuronInputSqSum
 TNeuron input calculator – calculates the squared weighted sum of inputs. More...
 
class  TNeuronInputSum
 TNeuron input calculator – calculates the weighted sum of inputs. More...
 
class  Tools
 Global auxiliary applications and data treatment routines. More...
 
class  TransformationHandler
 Class that contains all the data information. More...
 
class  TreeInfo
 
class  TSpline1
 Linear interpolation of TGraph. More...
 
class  TSpline2
 Quadratic interpolation of TGraph. More...
 
class  TSynapse
 Synapse class used by TMVA artificial neural network methods. More...
 
struct  TTrainingSettings
 All of the options that can be specified in the training string. More...
 
class  Types
 Singleton class for Global types used by TMVA. More...
 
class  VariableDecorrTransform
 Linear interpolation class. More...
 
class  VariableGaussTransform
 Gaussian Transformation of input variables. More...
 
class  VariableIdentityTransform
 Linear interpolation class. More...
 
class  VariableImportance
 
class  VariableImportanceResult
 
class  VariableInfo
 Class for type info of MVA input variable. More...
 
class  VariableNormalizeTransform
 Linear interpolation class. More...
 
class  VariablePCATransform
 Linear interpolation class. More...
 
class  VariableRearrangeTransform
 Rearrangement of input variables. More...
 
class  VariableTransformBase
 Linear interpolation class. More...
 
class  VarTransformHandler
 
class  Volume
 Volume for BinarySearchTree. More...
 

Functions

void ActionButton (TControlBar *cbar, const TString &title, const TString &macro, const TString &comment, const TString &buttonType, TString requiredKey="")
 
void annconvergencetest (TString dataset, TDirectory *lhdir)
 
void annconvergencetest (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void BDT (TString dataset, const TString &fin="TMVA.root")
 
void BDT (TString dataset, Int_t itree, TString wfile, TString methName="BDT", Bool_t useTMVAStyle=kTRUE)
 
void BDT_DeleteTBar (int i)
 
void BDT_Reg (TString dataset, const TString &fin="TMVAReg.root")
 
void BDT_Reg (TString dataset, Int_t itree, TString wfile="", TString methName="BDT", Bool_t useTMVAStyle=kTRUE)
 
void bdtcontrolplots (TString dataset, TDirectory *)
 
void BDTControlPlots (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void BDTReg_DeleteTBar (int i)
 
void BoostControlPlots (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void boostcontrolplots (TString dataset, TDirectory *boostdir)
 
void compareanapp (TString finAn="TMVA.root", TString finApp="TMVApp.root", HistType htype=kMVAType, bool useTMVAStyle=kTRUE)
 
template<typename F , typename G , typename H >
compose_binary_t< F, G, Hcompose_binary (const F &_f, const G &_g, const H &_h)
 
template<typename F , typename G >
compose_unary_t< F, Gcompose_unary (const F &_f, const G &_g)
 
void correlations (TString dataset, TString fin="TMVA.root", Bool_t isRegression=kFALSE, Bool_t greyScale=kFALSE, Bool_t useTMVAStyle=kTRUE)
 
void correlationscatters (TString dataset, TString fin, TString var="var3", TString dirName_="InputVariables_Id", TString title="TMVA Input Variable", Bool_t isRegression=kFALSE, Bool_t useTMVAStyle=kTRUE)
 
void correlationscattersMultiClass (TString dataset, TString fin="TMVA.root", TString var="var3", TString dirName_="InputVariables_Id", TString title="TMVA Input Variable", Bool_t isRegression=kFALSE, Bool_t useTMVAStyle=kTRUE)
 
void correlationsMultiClass (TString dataset, TString fin="TMVA.root", Bool_t isRegression=kFALSE, Bool_t greyScale=kFALSE, Bool_t useTMVAStyle=kTRUE)
 
void CorrGui (TString dataset, TString fin="TMVA.root", TString dirName="InputVariables_Id", TString title="TMVA Input Variable", Bool_t isRegression=kFALSE)
 
void CorrGui_DeleteTBar ()
 
void CorrGuiMultiClass (TString dataset, TString fin="TMVA.root", TString dirName="InputVariables_Id", TString title="TMVA Input Variable", Bool_t isRegression=kFALSE)
 
void CorrGuiMultiClass_DeleteTBar ()
 
void CreateVariableTransforms (const TString &trafoDefinition, TMVA::DataSetInfo &dataInfo, TMVA::TransformationHandler &transformationHandler, TMVA::MsgLogger &log)
 
void DataLoaderCopy (TMVA::DataLoader *des, TMVA::DataLoader *src)
 
template<class T >
DeleteFunctor_t< const T > DeleteFunctor ()
 
void deviations (TString dataset, TString fin="TMVAReg.root", HistType htype=kMVAType, Bool_t showTarget=kTRUE, Bool_t useTMVAStyle=kTRUE)
 
void draw_activation (TCanvas *c, Double_t cx, Double_t cy, Double_t radx, Double_t rady, Int_t whichActivation)
 
void draw_input_labels (TString dataset, Int_t nInputs, Double_t *cy, Double_t rad, Double_t layerWidth)
 
void draw_layer (TString dataset, TCanvas *c, TH2F *h, Int_t iHist, Int_t nLayers, Double_t maxWeight)
 
void draw_layer_labels (Int_t nLayers)
 
void draw_network (TString dataset, TFile *f, TDirectory *d, const TString &hName="weights_hist", Bool_t movieMode=kFALSE, const TString &epoch="")
 
void draw_synapse (Double_t cx1, Double_t cy1, Double_t cx2, Double_t cy2, Double_t rad1, Double_t rad2, Double_t weightNormed)
 
void DrawCell (TMVA::PDEFoamCell *cell, TMVA::PDEFoam *foam, Double_t x, Double_t y, Double_t xscale, Double_t yscale)
 
void DrawMLPoutputMovie (TString dataset, TFile *file, const TString &methodType, const TString &methodTitle)
 
void DrawNetworkMovie (TString dataset, TFile *file, const TString &methodType, const TString &methodTitle)
 
void efficiencies (TString dataset, TString fin="TMVA.root", Int_t type=2, Bool_t useTMVAStyle=kTRUE)
 
void efficienciesMulticlass1vs1 (TString dataset, TString fin)
 
void efficienciesMulticlass1vsRest (TString dataset, TString filename_input="TMVAMulticlass.root", EEfficiencyPlotType plotType=EEfficiencyPlotType::kRejBvsEffS, Bool_t useTMVAStyle=kTRUE)
 
MsgLoggerEndl (MsgLogger &ml)
 
TString fetchValue (const std::map< TString, TString > &keyValueMap, TString key)
 
template<typename T >
fetchValue (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue)
 
template<>
int fetchValue (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue)
 
template<>
double fetchValue (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue)
 
template<>
TString fetchValue (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue)
 
template<>
bool fetchValue (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue)
 
template<>
std::vector< double > fetchValue (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue)
 
TString fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key)
 
template<typename T >
fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue)
 
template<>
int fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue)
 
template<>
double fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue)
 
template<>
TString fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue)
 
template<>
bool fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue)
 
template<>
std::vector< double > fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue)
 
ConfiggConfig ()
 
TStringget_var_names (TString dataset, Int_t nVars)
 
Int_t getBkgColorF ()
 
Int_t getBkgColorT ()
 
std::vector< TStringgetclassnames (TString dataset, TString fin)
 
Int_t getIntColorF ()
 
Int_t getIntColorT ()
 
TListGetKeyList (const TString &pattern)
 
roccurvelist_t getRocCurves (TDirectory *binDir, TString methodPrefix, TString graphNameRef)
 
Int_t getSigColorF ()
 
Int_t getSigColorT ()
 
ToolsgTools ()
 
Int_t LargestCommonDivider (Int_t a, Int_t b)
 
void likelihoodrefs (TString dataset, TDirectory *lhdir)
 
void likelihoodrefs (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void MovieMaker (TString dataset, TString methodType="Method_MLP", TString methodTitle="MLP")
 
void MultiClassActionButton (TControlBar *cbar, const TString &title, const TString &macro, const TString &comment, const TString &buttonType, TString requiredKey="")
 
TListMultiClassGetKeyList (const TString &pattern)
 
void mvaeffs (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE, TString formula="S/sqrt(S+B)")
 
void mvas (TString dataset, TString fin="TMVA.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE)
 
void mvasMulticlass (TString dataset, TString fin="TMVAMulticlass.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE)
 
void mvaweights (TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void network (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
template<typename F >
null_t< Fnull ()
 
Bool_t operator< (const GeneticGenes &, const GeneticGenes &)
 
std::ostream & operator<< (std::ostream &os, const Rule &rule)
 
std::ostream & operator<< (std::ostream &os, const RuleEnsemble &event)
 
std::ostream & operator<< (std::ostream &os, const Node &node)
 
std::ostream & operator<< (std::ostream &os, const Event &event)
 
std::ostream & operator<< (std::ostream &os, const Node *node)
 
std::ostream & operator<< (std::ostream &os, const BinaryTree &tree)
 
std::ostream & operator<< (std::ostream &os, const PDF &tree)
 
std::istream & operator>> (std::istream &istr, BinaryTree &tree)
 
std::istream & operator>> (std::istream &istr, PDF &tree)
 
void paracoor (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void Plot (TString fileName, TMVA::ECellValue cv, TString cv_long, bool useTMVAStyle=kTRUE)
 
void Plot1DimFoams (TList &foam_list, TMVA::ECellValue cell_value, const TString &cell_value_description, TMVA::PDEFoamKernelBase *kernel)
 
void plot_efficiencies (TString dataset, TFile *file, Int_t type=2, TDirectory *BinDir=0)
 
void PlotCellTree (TString fileName, TString cv_long, bool useTMVAStyle=kTRUE)
 
void plotEfficienciesMulticlass (roccurvelist_t rocCurves, classcanvasmap_t classCanvasMap)
 
void plotEfficienciesMulticlass1vs1 (TString dataset, TString fin, TString baseClassname)
 
void plotEfficienciesMulticlass1vsRest (TString dataset, EEfficiencyPlotType plotType=EEfficiencyPlotType::kRejBvsEffS, TString filename_input="TMVAMulticlass.root")
 
void PlotFoams (TString fileName="weights/TMVAClassification_PDEFoam.weights_foams.root", bool useTMVAStyle=kTRUE)
 
void PlotNDimFoams (TList &foam_list, TMVA::ECellValue cell_value, const TString &cell_value_description, TMVA::PDEFoamKernelBase *kernel)
 
void probas (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
 
void RegGuiActionButton (TControlBar *cbar, const TString &title, const TString &macro, const TString &comment, const TString &buttonType, TString requiredKey="")
 
TListRegGuiGetKeyList (const TString &pattern)
 
void regression_averagedevs (TString dataset, TString fin, Int_t Nevt=-1, Bool_t useTMVAStyle=kTRUE)
 
void rulevis (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)
 
void rulevisCorr (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)
 
void rulevisCorr (TDirectory *rfdir, TDirectory *vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type)
 
void rulevisHists (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)
 
void rulevisHists (TDirectory *rfdir, TDirectory *vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type)
 
void TMVAGui (const char *fName="TMVA.root", TString dataset="")
 
void TMVAMultiClassGui (const char *fName="TMVAMulticlass.root", TString dataset="")
 
void TMVARegGui (const char *fName="TMVAReg.root", TString dataset="")
 
void variables (TString dataset, TString fin="TMVA.root", TString dirName="InputVariables_Id", TString title="TMVA Input Variables", Bool_t isRegression=kFALSE, Bool_t useTMVAStyle=kTRUE)
 
void variablesMultiClass (TString dataset, TString fin="TMVA.root", TString dirName="InputVariables_Id", TString title="TMVA Input Variables", Bool_t isRegression=kFALSE, Bool_t useTMVAStyle=kTRUE)
 

Detailed Description

create variable transformations

Function Documentation

◆ ActionButton()

void TMVA::ActionButton ( TControlBar cbar,
const TString title,
const TString macro,
const TString comment,
const TString buttonType,
TString  requiredKey = "" 
)

◆ annconvergencetest() [1/2]

void TMVA::annconvergencetest ( TString  dataset,
TDirectory lhdir 
)

◆ annconvergencetest() [2/2]

void TMVA::annconvergencetest ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ BDT() [1/2]

void TMVA::BDT ( TString  dataset,
const TString fin = "TMVA.root" 
)

◆ BDT() [2/2]

void TMVA::BDT ( TString  dataset,
Int_t  itree,
TString  wfile,
TString  methName = "BDT",
Bool_t  useTMVAStyle = kTRUE 
)

◆ BDT_DeleteTBar()

void TMVA::BDT_DeleteTBar ( int  i)

◆ BDT_Reg() [1/2]

void TMVA::BDT_Reg ( TString  dataset,
const TString fin = "TMVAReg.root" 
)

◆ BDT_Reg() [2/2]

void TMVA::BDT_Reg ( TString  dataset,
Int_t  itree,
TString  wfile = "",
TString  methName = "BDT",
Bool_t  useTMVAStyle = kTRUE 
)

◆ bdtcontrolplots()

void TMVA::bdtcontrolplots ( TString  dataset,
TDirectory  
)

◆ BDTControlPlots()

void TMVA::BDTControlPlots ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ BDTReg_DeleteTBar()

void TMVA::BDTReg_DeleteTBar ( int  i)

◆ BoostControlPlots()

void TMVA::BoostControlPlots ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ boostcontrolplots()

void TMVA::boostcontrolplots ( TString  dataset,
TDirectory boostdir 
)

◆ compareanapp()

void TMVA::compareanapp ( TString  finAn = "TMVA.root",
TString  finApp = "TMVApp.root",
HistType  htype = kMVAType,
bool  useTMVAStyle = kTRUE 
)

◆ compose_binary()

template<typename F , typename G , typename H >
compose_binary_t<F,G,H> TMVA::compose_binary ( const F _f,
const G _g,
const H _h 
)
inline

Definition at line 137 of file DataSetFactory.h.

◆ compose_unary()

template<typename F , typename G >
compose_unary_t<F,G> TMVA::compose_unary ( const F _f,
const G _g 
)
inline

Definition at line 163 of file DataSetFactory.h.

◆ correlations()

void TMVA::correlations ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  isRegression = kFALSE,
Bool_t  greyScale = kFALSE,
Bool_t  useTMVAStyle = kTRUE 
)

◆ correlationscatters()

void TMVA::correlationscatters ( TString  dataset,
TString  fin,
TString  var = "var3",
TString  dirName_ = "InputVariables_Id",
TString  title = "TMVA Input Variable",
Bool_t  isRegression = kFALSE,
Bool_t  useTMVAStyle = kTRUE 
)

◆ correlationscattersMultiClass()

void TMVA::correlationscattersMultiClass ( TString  dataset,
TString  fin = "TMVA.root",
TString  var = "var3",
TString  dirName_ = "InputVariables_Id",
TString  title = "TMVA Input Variable",
Bool_t  isRegression = kFALSE,
Bool_t  useTMVAStyle = kTRUE 
)

◆ correlationsMultiClass()

void TMVA::correlationsMultiClass ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  isRegression = kFALSE,
Bool_t  greyScale = kFALSE,
Bool_t  useTMVAStyle = kTRUE 
)

◆ CorrGui()

void TMVA::CorrGui ( TString  dataset,
TString  fin = "TMVA.root",
TString  dirName = "InputVariables_Id",
TString  title = "TMVA Input Variable",
Bool_t  isRegression = kFALSE 
)

◆ CorrGui_DeleteTBar()

void TMVA::CorrGui_DeleteTBar ( )

◆ CorrGuiMultiClass()

void TMVA::CorrGuiMultiClass ( TString  dataset,
TString  fin = "TMVA.root",
TString  dirName = "InputVariables_Id",
TString  title = "TMVA Input Variable",
Bool_t  isRegression = kFALSE 
)

◆ CorrGuiMultiClass_DeleteTBar()

void TMVA::CorrGuiMultiClass_DeleteTBar ( )

◆ CreateVariableTransforms()

void TMVA::CreateVariableTransforms ( const TString trafoDefinition,
TMVA::DataSetInfo dataInfo,
TMVA::TransformationHandler transformationHandler,
TMVA::MsgLogger log 
)

Definition at line 67 of file VariableTransform.cxx.

◆ DataLoaderCopy()

void TMVA::DataLoaderCopy ( TMVA::DataLoader des,
TMVA::DataLoader src 
)

◆ DeleteFunctor()

template<class T >
DeleteFunctor_t<const T> TMVA::DeleteFunctor ( )

Definition at line 77 of file DataSetFactory.h.

◆ deviations()

void TMVA::deviations ( TString  dataset,
TString  fin = "TMVAReg.root",
HistType  htype = kMVAType,
Bool_t  showTarget = kTRUE,
Bool_t  useTMVAStyle = kTRUE 
)

◆ draw_activation()

void TMVA::draw_activation ( TCanvas c,
Double_t  cx,
Double_t  cy,
Double_t  radx,
Double_t  rady,
Int_t  whichActivation 
)

◆ draw_input_labels()

void TMVA::draw_input_labels ( TString  dataset,
Int_t  nInputs,
Double_t cy,
Double_t  rad,
Double_t  layerWidth 
)

◆ draw_layer()

void TMVA::draw_layer ( TString  dataset,
TCanvas c,
TH2F h,
Int_t  iHist,
Int_t  nLayers,
Double_t  maxWeight 
)

◆ draw_layer_labels()

void TMVA::draw_layer_labels ( Int_t  nLayers)

◆ draw_network()

void TMVA::draw_network ( TString  dataset,
TFile f,
TDirectory d,
const TString hName = "weights_hist",
Bool_t  movieMode = kFALSE,
const TString epoch = "" 
)

◆ draw_synapse()

void TMVA::draw_synapse ( Double_t  cx1,
Double_t  cy1,
Double_t  cx2,
Double_t  cy2,
Double_t  rad1,
Double_t  rad2,
Double_t  weightNormed 
)

◆ DrawCell()

void TMVA::DrawCell ( TMVA::PDEFoamCell cell,
TMVA::PDEFoam foam,
Double_t  x,
Double_t  y,
Double_t  xscale,
Double_t  yscale 
)

◆ DrawMLPoutputMovie()

void TMVA::DrawMLPoutputMovie ( TString  dataset,
TFile file,
const TString methodType,
const TString methodTitle 
)

◆ DrawNetworkMovie()

void TMVA::DrawNetworkMovie ( TString  dataset,
TFile file,
const TString methodType,
const TString methodTitle 
)

◆ efficiencies()

void TMVA::efficiencies ( TString  dataset,
TString  fin = "TMVA.root",
Int_t  type = 2,
Bool_t  useTMVAStyle = kTRUE 
)

◆ efficienciesMulticlass1vs1()

void TMVA::efficienciesMulticlass1vs1 ( TString  dataset,
TString  fin 
)

◆ efficienciesMulticlass1vsRest()

void TMVA::efficienciesMulticlass1vsRest ( TString  dataset,
TString  filename_input = "TMVAMulticlass.root",
EEfficiencyPlotType  plotType = EEfficiencyPlotType::kRejBvsEffS,
Bool_t  useTMVAStyle = kTRUE 
)

◆ Endl()

MsgLogger& TMVA::Endl ( MsgLogger ml)
inline

Definition at line 158 of file MsgLogger.h.

◆ fetchValue() [1/7]

TString TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key 
)

Definition at line 308 of file MethodDNN.cxx.

◆ fetchValue() [2/7]

template<typename T >
T TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key,
defaultValue 
)

◆ fetchValue() [3/7]

template<>
int TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key,
int  defaultValue 
)

Definition at line 328 of file MethodDNN.cxx.

◆ fetchValue() [4/7]

template<>
double TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key,
double  defaultValue 
)

Definition at line 342 of file MethodDNN.cxx.

◆ fetchValue() [5/7]

template<>
TString TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key,
TString  defaultValue 
)

Definition at line 355 of file MethodDNN.cxx.

◆ fetchValue() [6/7]

template<>
bool TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key,
bool  defaultValue 
)

Definition at line 368 of file MethodDNN.cxx.

◆ fetchValue() [7/7]

template<>
std::vector<double> TMVA::fetchValue ( const std::map< TString, TString > &  keyValueMap,
TString  key,
std::vector< double >  defaultValue 
)

Definition at line 385 of file MethodDNN.cxx.

◆ fetchValueTmp() [1/7]

TString TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key 
)

Definition at line 69 of file MethodDL.cxx.

◆ fetchValueTmp() [2/7]

template<typename T >
T TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key,
defaultValue 
)

◆ fetchValueTmp() [3/7]

template<>
int TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key,
int  defaultValue 
)

Definition at line 85 of file MethodDL.cxx.

◆ fetchValueTmp() [4/7]

template<>
double TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key,
double  defaultValue 
)

Definition at line 96 of file MethodDL.cxx.

◆ fetchValueTmp() [5/7]

template<>
TString TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key,
TString  defaultValue 
)

Definition at line 107 of file MethodDL.cxx.

◆ fetchValueTmp() [6/7]

template<>
bool TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key,
bool  defaultValue 
)

Definition at line 118 of file MethodDL.cxx.

◆ fetchValueTmp() [7/7]

template<>
std::vector<double> TMVA::fetchValueTmp ( const std::map< TString, TString > &  keyValueMap,
TString  key,
std::vector< double >  defaultValue 
)

Definition at line 135 of file MethodDL.cxx.

◆ gConfig()

Config& TMVA::gConfig ( )

◆ get_var_names()

TString* TMVA::get_var_names ( TString  dataset,
Int_t  nVars 
)

◆ getBkgColorF()

Int_t TMVA::getBkgColorF ( )
inline

Definition at line 41 of file BDT.h.

◆ getBkgColorT()

Int_t TMVA::getBkgColorT ( )
inline

Definition at line 46 of file BDT.h.

◆ getclassnames()

std::vector<TString> TMVA::getclassnames ( TString  dataset,
TString  fin 
)

◆ getIntColorF()

Int_t TMVA::getIntColorF ( )
inline

Definition at line 42 of file BDT.h.

◆ getIntColorT()

Int_t TMVA::getIntColorT ( )
inline

Definition at line 47 of file BDT.h.

◆ GetKeyList()

TList* TMVA::GetKeyList ( const TString pattern)

◆ getRocCurves()

roccurvelist_t TMVA::getRocCurves ( TDirectory binDir,
TString  methodPrefix,
TString  graphNameRef 
)

◆ getSigColorF()

Int_t TMVA::getSigColorF ( )
inline

Definition at line 40 of file BDT.h.

◆ getSigColorT()

Int_t TMVA::getSigColorT ( )
inline

Definition at line 45 of file BDT.h.

◆ gTools()

Tools& TMVA::gTools ( )

◆ LargestCommonDivider()

Int_t TMVA::LargestCommonDivider ( Int_t  a,
Int_t  b 
)

Definition at line 82 of file DataSetFactory.cxx.

◆ likelihoodrefs() [1/2]

void TMVA::likelihoodrefs ( TString  dataset,
TDirectory lhdir 
)

◆ likelihoodrefs() [2/2]

void TMVA::likelihoodrefs ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ MovieMaker()

void TMVA::MovieMaker ( TString  dataset,
TString  methodType = "Method_MLP",
TString  methodTitle = "MLP" 
)

◆ MultiClassActionButton()

void TMVA::MultiClassActionButton ( TControlBar cbar,
const TString title,
const TString macro,
const TString comment,
const TString buttonType,
TString  requiredKey = "" 
)

◆ MultiClassGetKeyList()

TList* TMVA::MultiClassGetKeyList ( const TString pattern)

◆ mvaeffs()

void TMVA::mvaeffs ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE,
TString  formula = "S/sqrt(S+B)" 
)

◆ mvas()

void TMVA::mvas ( TString  dataset,
TString  fin = "TMVA.root",
HistType  htype = kMVAType,
Bool_t  useTMVAStyle = kTRUE 
)

◆ mvasMulticlass()

void TMVA::mvasMulticlass ( TString  dataset,
TString  fin = "TMVAMulticlass.root",
HistType  htype = kMVAType,
Bool_t  useTMVAStyle = kTRUE 
)

◆ mvaweights()

void TMVA::mvaweights ( TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ network()

void TMVA::network ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ null()

template<typename F >
null_t<F> TMVA::null ( )
inline

Definition at line 109 of file DataSetFactory.h.

◆ operator<()

Bool_t TMVA::operator< ( const GeneticGenes ,
const GeneticGenes  
)

◆ operator<<() [1/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const Rule rule 
)

◆ operator<<() [2/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const RuleEnsemble event 
)

◆ operator<<() [3/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const Node node 
)

◆ operator<<() [4/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const Event event 
)

◆ operator<<() [5/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const Node node 
)

◆ operator<<() [6/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const BinaryTree tree 
)

◆ operator<<() [7/7]

std::ostream& TMVA::operator<< ( std::ostream &  os,
const PDF tree 
)

◆ operator>>() [1/2]

std::istream& TMVA::operator>> ( std::istream &  istr,
BinaryTree tree 
)

◆ operator>>() [2/2]

std::istream& TMVA::operator>> ( std::istream &  istr,
PDF tree 
)

◆ paracoor()

void TMVA::paracoor ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ Plot()

void TMVA::Plot ( TString  fileName,
TMVA::ECellValue  cv,
TString  cv_long,
bool  useTMVAStyle = kTRUE 
)

◆ Plot1DimFoams()

void TMVA::Plot1DimFoams ( TList foam_list,
TMVA::ECellValue  cell_value,
const TString cell_value_description,
TMVA::PDEFoamKernelBase kernel 
)

◆ plot_efficiencies()

void TMVA::plot_efficiencies ( TString  dataset,
TFile file,
Int_t  type = 2,
TDirectory BinDir = 0 
)

◆ PlotCellTree()

void TMVA::PlotCellTree ( TString  fileName,
TString  cv_long,
bool  useTMVAStyle = kTRUE 
)

◆ plotEfficienciesMulticlass()

void TMVA::plotEfficienciesMulticlass ( roccurvelist_t  rocCurves,
classcanvasmap_t  classCanvasMap 
)

◆ plotEfficienciesMulticlass1vs1()

void TMVA::plotEfficienciesMulticlass1vs1 ( TString  dataset,
TString  fin,
TString  baseClassname 
)

◆ plotEfficienciesMulticlass1vsRest()

void TMVA::plotEfficienciesMulticlass1vsRest ( TString  dataset,
EEfficiencyPlotType  plotType = EEfficiencyPlotType::kRejBvsEffS,
TString  filename_input = "TMVAMulticlass.root" 
)

◆ PlotFoams()

void TMVA::PlotFoams ( TString  fileName = "weights/TMVAClassification_PDEFoam.weights_foams.root",
bool  useTMVAStyle = kTRUE 
)

◆ PlotNDimFoams()

void TMVA::PlotNDimFoams ( TList foam_list,
TMVA::ECellValue  cell_value,
const TString cell_value_description,
TMVA::PDEFoamKernelBase kernel 
)

◆ probas()

void TMVA::probas ( TString  dataset,
TString  fin = "TMVA.root",
Bool_t  useTMVAStyle = kTRUE 
)

◆ RegGuiActionButton()

void TMVA::RegGuiActionButton ( TControlBar cbar,
const TString title,
const TString macro,
const TString comment,
const TString buttonType,
TString  requiredKey = "" 
)

◆ RegGuiGetKeyList()

TList* TMVA::RegGuiGetKeyList ( const TString pattern)

◆ regression_averagedevs()

void TMVA::regression_averagedevs ( TString  dataset,
TString  fin,
Int_t  Nevt = -1,
Bool_t  useTMVAStyle = kTRUE 
)

◆ rulevis()

void TMVA::rulevis ( TString  fin = "TMVA.root",
TMVAGlob::TypeOfPlot  type = TMVAGlob::kNorm,
bool  useTMVAStyle = kTRUE 
)

◆ rulevisCorr() [1/2]

void TMVA::rulevisCorr ( TString  fin = "TMVA.root",
TMVAGlob::TypeOfPlot  type = TMVAGlob::kNorm,
bool  useTMVAStyle = kTRUE 
)

◆ rulevisCorr() [2/2]

void TMVA::rulevisCorr ( TDirectory rfdir,
TDirectory vardir,
TDirectory corrdir,
TMVAGlob::TypeOfPlot  type 
)

◆ rulevisHists() [1/2]

void TMVA::rulevisHists ( TString  fin = "TMVA.root",
TMVAGlob::TypeOfPlot  type = TMVAGlob::kNorm,
bool  useTMVAStyle = kTRUE 
)

◆ rulevisHists() [2/2]

void TMVA::rulevisHists ( TDirectory rfdir,
TDirectory vardir,
TDirectory corrdir,
TMVAGlob::TypeOfPlot  type 
)

◆ TMVAGui()

void TMVA::TMVAGui ( const char *  fName = "TMVA.root",
TString  dataset = "" 
)

◆ TMVAMultiClassGui()

void TMVA::TMVAMultiClassGui ( const char *  fName = "TMVAMulticlass.root",
TString  dataset = "" 
)

◆ TMVARegGui()

void TMVA::TMVARegGui ( const char *  fName = "TMVAReg.root",
TString  dataset = "" 
)

◆ variables()

void TMVA::variables ( TString  dataset,
TString  fin = "TMVA.root",
TString  dirName = "InputVariables_Id",
TString  title = "TMVA Input Variables",
Bool_t  isRegression = kFALSE,
Bool_t  useTMVAStyle = kTRUE 
)

◆ variablesMultiClass()

void TMVA::variablesMultiClass ( TString  dataset,
TString  fin = "TMVA.root",
TString  dirName = "InputVariables_Id",
TString  title = "TMVA Input Variables",
Bool_t  isRegression = kFALSE,
Bool_t  useTMVAStyle = kTRUE 
)