|
| 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 | ClassifierFactory |
| | This is the MVA factory. More...
|
| |
| class | ClassInfo |
| | Class that contains all the information of a class. More...
|
| |
| 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 Executor 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 | MethodPyTorch |
| |
| 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 | TrainingHistory |
| | Tracking data from training. 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...
|
| |
|
| void | ActionButton (TControlBar *cbar, const TString &title, const TString ¯o, 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, TDirectory *boostdir) |
| |
| void | BoostControlPlots (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
| |
| void | compareanapp (TString finAn="TMVA.root", TString finApp="TMVApp.root", HistType htype=kMVAType, bool useTMVAStyle=kTRUE) |
| |
| 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) |
| |
| MsgLogger & | Endl (MsgLogger &ml) |
| |
| TString | fetchValue (const std::map< TString, TString > &keyValueMap, TString key) |
| |
| template<> |
| bool | fetchValue (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue) |
| |
| template<> |
| double | fetchValue (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue) |
| |
| template<> |
| int | fetchValue (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue) |
| |
| template<> |
| std::vector< double > | fetchValue (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue) |
| |
| template<typename T > |
| T | fetchValue (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue) |
| |
| template<> |
| TString | fetchValue (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue) |
| |
| TString | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key) |
| |
| template<> |
| bool | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue) |
| |
| template<> |
| double | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue) |
| |
| template<> |
| int | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue) |
| |
| template<> |
| std::vector< double > | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue) |
| |
| template<typename T > |
| T | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue) |
| |
| template<> |
| TString | fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue) |
| |
| Config & | gConfig () |
| |
| TString * | get_var_names (TString dataset, Int_t nVars) |
| |
| Int_t | getBkgColorF () |
| |
| Int_t | getBkgColorT () |
| |
| std::vector< TString > | getclassnames (TString dataset, TString fin) |
| |
| Int_t | getIntColorF () |
| |
| Int_t | getIntColorT () |
| |
| TList * | GetKeyList (const TString &pattern) |
| |
| roccurvelist_t | getRocCurves (TDirectory *binDir, TString methodPrefix, TString graphNameRef) |
| |
| Int_t | getSigColorF () |
| |
| Int_t | getSigColorT () |
| |
| Tools & | gTools () |
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| Int_t | LargestCommonDivider (Int_t a, Int_t b) |
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| void | likelihoodrefs (TString dataset, TDirectory *lhdir) |
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| void | likelihoodrefs (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
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| void | MovieMaker (TString dataset, TString methodType="Method_MLP", TString methodTitle="MLP") |
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| void | MultiClassActionButton (TControlBar *cbar, const TString &title, const TString ¯o, const TString &comment, const TString &buttonType, TString requiredKey="") |
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| TList * | MultiClassGetKeyList (const TString &pattern) |
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| void | mvaeffs (TString dataset, TString fin="TMVA.root", Float_t nSignal=1000, Float_t nBackground=1000, Bool_t useTMVAStyle=kTRUE, TString formula="S/sqrt(S+B)") |
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| void | mvas (TString dataset, TString fin="TMVA.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE) |
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| void | mvasMulticlass (TString dataset, TString fin="TMVAMulticlass.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE) |
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| void | mvaweights (TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
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| void | network (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
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| template<typename F > |
| null_t< F > | null () |
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| Bool_t | operator< (const GeneticGenes &, const GeneticGenes &) |
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| std::ostream & | operator<< (std::ostream &os, const BinaryTree &tree) |
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| std::ostream & | operator<< (std::ostream &os, const Event &event) |
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| std::ostream & | operator<< (std::ostream &os, const Node &node) |
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| std::ostream & | operator<< (std::ostream &os, const Node *node) |
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| std::ostream & | operator<< (std::ostream &os, const PDF &tree) |
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| std::ostream & | operator<< (std::ostream &os, const Rule &rule) |
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| std::ostream & | operator<< (std::ostream &os, const RuleEnsemble &event) |
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| std::istream & | operator>> (std::istream &istr, BinaryTree &tree) |
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| std::istream & | operator>> (std::istream &istr, PDF &tree) |
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| void | paracoor (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
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| void | Plot (TString fileName, TMVA::ECellValue cv, TString cv_long, bool useTMVAStyle=kTRUE) |
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| void | Plot1DimFoams (TList &foam_list, TMVA::ECellValue cell_value, const TString &cell_value_description, TMVA::PDEFoamKernelBase *kernel) |
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| void | plot_efficiencies (TString dataset, TFile *file, Int_t type=2, TDirectory *BinDir=nullptr) |
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| void | plot_training_history (TString dataset, TFile *file, TDirectory *BinDir=nullptr) |
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| void | PlotCellTree (TString fileName, TString cv_long, bool useTMVAStyle=kTRUE) |
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| void | plotEfficienciesMulticlass (roccurvelist_t rocCurves, classcanvasmap_t classCanvasMap) |
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| void | plotEfficienciesMulticlass1vs1 (TString dataset, TString fin, TString baseClassname) |
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| void | plotEfficienciesMulticlass1vsRest (TString dataset, EEfficiencyPlotType plotType=EEfficiencyPlotType::kRejBvsEffS, TString filename_input="TMVAMulticlass.root") |
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| void | PlotFoams (TString fileName="weights/TMVAClassification_PDEFoam.weights_foams.root", bool useTMVAStyle=kTRUE) |
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| void | PlotNDimFoams (TList &foam_list, TMVA::ECellValue cell_value, const TString &cell_value_description, TMVA::PDEFoamKernelBase *kernel) |
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| void | probas (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
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| TString | Python_Executable () |
| | Function to find current Python executable used by ROOT If "Python3" is installed, return "python3".
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| void | RegGuiActionButton (TControlBar *cbar, const TString &title, const TString ¯o, const TString &comment, const TString &buttonType, TString requiredKey="") |
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| TList * | RegGuiGetKeyList (const TString &pattern) |
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| void | regression_averagedevs (TString dataset, TString fin, Int_t Nevt=-1, Bool_t useTMVAStyle=kTRUE) |
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| void | rulevis (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE) |
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| void | rulevisCorr (TDirectory *rfdir, TDirectory *vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type) |
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| void | rulevisCorr (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE) |
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| void | rulevisHists (TDirectory *rfdir, TDirectory *vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type) |
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| void | rulevisHists (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE) |
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| void | TMVAGui (const char *fName="TMVA.root", TString dataset="") |
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| void | TMVAMultiClassGui (const char *fName="TMVAMulticlass.root", TString dataset="") |
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| void | TMVARegGui (const char *fName="TMVAReg.root", TString dataset="") |
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| void | training_history (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE) |
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| 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) |
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| 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) |
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create variable transformations
