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TMVA

The Multi Variate Analysis package.

The TMVA Multi-Variate-Analysis classes.

See:

Old links, referring to old TMVA versions, but they can still be useful for some of the TMVA methods:

## Classes | |

class | TMVA::AbsoluteDeviationLossFunction |

Absolute Deviation Loss Function. More... | |

class | TMVA::AbsoluteDeviationLossFunctionBDT |

Absolute Deviation BDT Loss Function. More... | |

class | TMVA::BDTEventWrapper |

class | TMVA::BinarySearchTree |

A simple Binary search tree including a volume search method. More... | |

class | TMVA::BinarySearchTreeNode |

Node for the BinarySearch or Decision Trees. More... | |

class | TMVA::BinaryTree |

Base class for BinarySearch and Decision Trees. More... | |

class | TMVA::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 | TMVA::CCTreeWrapper |

class | Classification |

Class to perform two class classification. More... | |

class | ClassificationResult |

Class to save the results of the classifier. More... | |

class | TMVA::ClassifierFactory |

This is the MVA factory. More... | |

class | TMVA::ClassInfo |

Class that contains all the information of a class. More... | |

class | TMVA::Config |

Singleton class for global configuration settings used by TMVA. More... | |

class | TMVA::ConvergenceTest |

Check for convergence. More... | |

class | TMVA::CostComplexityPruneTool |

A class to prune a decision tree using the Cost Complexity method. More... | |

class | CreateMethodPlugins |

Plugins analysis. More... | |

class | TMVA::CrossEntropy |

Implementation of the CrossEntropy as separation criterion. More... | |

class | TMVA::CrossValidation |

Class to perform cross validation, splitting the dataloader into folds. More... | |

class | TMVA::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 | TMVA::DataInputHandler |

Class that contains all the data information. More... | |

class | TMVA::DataLoader |

class | TMVA::DataSet |

Class that contains all the data information. More... | |

class | TMVA::DataSetFactory |

Class that contains all the data information. More... | |

class | TMVA::DataSetInfo |

Class that contains all the data information. More... | |

class | TMVA::DataSetManager |

Class that contains all the data information. More... | |

class | TMVA::DecisionTree |

Implementation of a Decision Tree. More... | |

class | TMVA::Envelope |

Abstract base class for all high level ml algorithms, you can book ml methods like BDT, MLP. More... | |

class | TMVA::Event |

class | TMVA::ExpectedErrorPruneTool |

A helper class to prune a decision tree using the expected error (C4.5) method. More... | |

class | TMVA::Factory |

This is the main MVA steering class. More... | |

class | TMVA::FitterBase |

Base class for TMVA fitters. More... | |

class | TMVA::GeneticAlgorithm |

Base definition for genetic algorithm. More... | |

class | TMVA::GeneticFitter |

Fitter using a Genetic Algorithm. More... | |

class | TMVA::GeneticGenes |

Cut optimisation interface class for genetic algorithm. More... | |

class | TMVA::GeneticPopulation |

Population definition for genetic algorithm. More... | |

class | TMVA::GeneticRange |

Range definition for genetic algorithm. More... | |

class | TMVA::GiniIndex |

Implementation of the GiniIndex as separation criterion. More... | |

class | TMVA::GiniIndexWithLaplace |

Implementation of the GiniIndex With Laplace correction as separation criterion. More... | |

class | TMVA::HuberLossFunction |

Huber Loss Function. More... | |

class | TMVA::HuberLossFunctionBDT |

Huber BDT Loss Function. More... | |

class | TMVA::HyperParameterOptimisation |

class | TMVA::HyperParameterOptimisationResult |

class | TMVA::IFitterTarget |

Interface for a fitter 'target'. More... | |

class | TMVA::IMethod |

Interface for all concrete MVA method implementations. More... | |

class | TMVA::Interval |

The TMVA::Interval Class. More... | |

class | TMVA::IPruneTool |

IPruneTool - a helper interface class to prune a decision tree. More... | |

class | TMVA::IPythonInteractive |

This class is needed by JsMVA, and it's a helper class for tracking errors during the training in Jupyter notebook. More... | |

class | TMVA::KDEKernel |

KDE Kernel for "smoothing" the PDFs. More... | |

class | kNN |

kNN::Event describes point in input variable vector-space, with additional functionality like distance between points More... | |

class | TMVA::LDA |

class | TMVA::LeastSquaresLossFunction |

Least Squares Loss Function. More... | |

class | TMVA::LeastSquaresLossFunctionBDT |

Least Squares BDT Loss Function. More... | |

class | TMVA::LogInterval |

The TMVA::Interval Class. More... | |

class | TMVA::MCFitter |

Fitter using Monte Carlo sampling of parameters. More... | |

class | TMVA::MethodANNBase |

Base class for all TMVA methods using artificial neural networks. More... | |

class | TMVA::MethodBase |

Virtual base Class for all MVA method. More... | |

class | TMVA::MethodBayesClassifier |

Description of bayesian classifiers. More... | |

class | TMVA::MethodBDT |

Analysis of Boosted Decision Trees. More... | |

class | TMVA::MethodBoost |

Class for boosting a TMVA method. More... | |

class | TMVA::MethodCategory |

Class for categorizing the phase space. More... | |

class | TMVA::MethodCFMlpANN |

Interface to Clermond-Ferrand artificial neural network. More... | |

class | TMVA::MethodCFMlpANN_Utils |

Implementation of Clermond-Ferrand artificial neural network. More... | |

class | TMVA::MethodCompositeBase |

Virtual base class for combining several TMVA method. More... | |

class | TMVA::MethodCrossValidation |

class | TMVA::MethodCuts |

Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular minimum and maximum requirements. More... | |

class | TMVA::MethodDNN |

Deep Neural Network Implementation. More... | |

class | TMVA::MethodDT |

Analysis of Boosted Decision Trees. More... | |

class | TMVA::MethodFDA |

Function discriminant analysis (FDA). More... | |

class | TMVA::MethodFisher |

Fisher and Mahalanobis Discriminants (Linear Discriminant Analysis) More... | |

class | TMVA::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 | TMVA::MethodKNN |

Analysis of k-nearest neighbor. More... | |

class | TMVA::MethodLD |

Linear Discriminant. More... | |

class | TMVA::MethodLikelihood |

Likelihood analysis ("non-parametric approach") More... | |

class | TMVA::MethodMLP |

Multilayer Perceptron class built off of MethodANNBase. More... | |

class | TMVA::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 | TMVA::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 | TMVA::MethodRuleFit |

J Friedman's RuleFit method. More... | |

class | TMVA::MethodSVM |

SMO Platt's SVM classifier with Keerthi & Shavade improvements. More... | |

class | TMVA::MethodTMlpANN |

This is the TMVA TMultiLayerPerceptron interface class. More... | |

class | TMVA::MinuitFitter |

/Fitter using MINUIT More... | |

class | TMVA::MinuitWrapper |

Wrapper around MINUIT. More... | |

class | TMVA::MisClassificationError |

Implementation of the MisClassificationError as separation criterion. More... | |

class | TMVA::MsgLogger |

ostringstream derivative to redirect and format output More... | |

class | TMVA::kNN::Node< T > |

This file contains binary tree and global function template that searches tree for k-nearest neigbors. More... | |

class | TMVA::Node |

Node for the BinarySearch or Decision Trees. More... | |

class | TMVA::OptimizeConfigParameters |

class | TMVA::OptionBase |

Class for TMVA-option handling. More... | |

class | TMVA::OptionMap |

class to storage options for the differents methods More... | |

class | TMVA::PDEFoam |

Implementation of PDEFoam. More... | |

class | TMVA::PDEFoamCell |

class | TMVA::PDEFoamDecisionTree |

This PDEFoam variant acts like a decision tree and stores in every cell the discriminant. More... | |

class | TMVA::PDEFoamDecisionTreeDensity |

This is a concrete implementation of PDEFoam. More... | |

class | TMVA::PDEFoamDensityBase |

This is an abstract class, which provides an interface for a PDEFoam density estimator. More... | |

class | TMVA::PDEFoamDiscriminant |

This PDEFoam variant stores in every cell the discriminant. More... | |

class | TMVA::PDEFoamDiscriminantDensity |

This is a concrete implementation of PDEFoam. More... | |

class | TMVA::PDEFoamEvent |

This PDEFoam variant stores in every cell the sum of event weights and the sum of the squared event weights. More... | |

class | TMVA::PDEFoamEventDensity |

This is a concrete implementation of PDEFoam. More... | |

class | TMVA::PDEFoamKernelBase |

This class is the abstract kernel interface for PDEFoam. More... | |

class | TMVA::PDEFoamKernelGauss |

This PDEFoam kernel estimates a cell value for a given event by weighting all cell values with a gauss function. More... | |

class | TMVA::PDEFoamKernelLinN |

This PDEFoam kernel estimates a cell value for a given event by weighting with cell values of the nearest neighbor cells. More... | |

class | TMVA::PDEFoamKernelTrivial |

This class is a trivial PDEFoam kernel estimator. More... | |

class | TMVA::PDEFoamMultiTarget |

This PDEFoam variant is used to estimate multiple targets by creating an event density foam (PDEFoamEvent), which has dimension: More... | |

class | TMVA::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 | TMVA::PDEFoamTargetDensity |

This is a concrete implementation of PDEFoam. More... | |

class | TMVA::PDEFoamVect |

class | TMVA::PDF |

PDF wrapper for histograms; uses user-defined spline interpolation. More... | |

class | TMVA::QuickMVAProbEstimator |

class | TMVA::Ranking |

Ranking for variables in method (implementation) More... | |

class | TMVA::Reader |

The Reader class serves to use the MVAs in a specific analysis context. More... | |

class | TMVA::RegressionVariance |

Calculate the "SeparationGain" for Regression analysis separation criteria used in various training algorithms. More... | |

class | TMVA::Results |

Class that is the base-class for a vector of result. More... | |

class | TMVA::ResultsClassification |

Class that is the base-class for a vector of result. More... | |

class | TMVA::ResultsMulticlass |

Class which takes the results of a multiclass classification. More... | |

class | TMVA::ResultsRegression |

Class that is the base-class for a vector of result. More... | |

class | TMVA::ROCCalc |

class | TMVA::ROCCurve |

class | TMVA::RootFinder |

Root finding using Brents algorithm (translated from CERNLIB function RZERO) More... | |

class | TMVA::Rule |

Implementation of a rule. More... | |

class | TMVA::RuleCut |

A class describing a 'rule cut'. More... | |

class | TMVA::RuleEnsemble |

class | TMVA::RuleFit |

A class implementing various fits of rule ensembles. More... | |

class | TMVA::RuleFitAPI |

J Friedman's RuleFit method. More... | |

class | TMVA::RuleFitParams |

A class doing the actual fitting of a linear model using rules as base functions. More... | |

class | TMVA::SdivSqrtSplusB |

Implementation of the SdivSqrtSplusB as separation criterion. More... | |

class | TMVA::SeparationBase |

An interface to calculate the "SeparationGain" for different separation criteria used in various training algorithms. More... | |

class | TMVA::SimulatedAnnealing |

Base implementation of simulated annealing fitting procedure. More... | |

class | TMVA::SimulatedAnnealingFitter |

Fitter using a Simulated Annealing Algorithm. More... | |

class | TMVA::SVEvent |

Event class for Support Vector Machine. More... | |

class | TMVA::SVKernelFunction |

Kernel for Support Vector Machine. More... | |

class | TMVA::SVKernelMatrix |

Kernel matrix for Support Vector Machine. More... | |

class | TMVA::SVWorkingSet |

Working class for Support Vector Machine. More... | |

class | TMVA::TActivation |

Interface for TNeuron activation function classes. More... | |

class | TMVA::TActivationChooser |

Class for easily choosing activation functions. More... | |

class | TMVA::TActivationIdentity |

Identity activation function for TNeuron. More... | |

class | TMVA::TActivationRadial |

Radial basis activation function for ANN. More... | |

class | TMVA::TActivationReLU |

Rectified Linear Unit activation function for TNeuron. More... | |

class | TMVA::TActivationSigmoid |

Sigmoid activation function for TNeuron. More... | |

class | TMVA::TActivationTanh |

Tanh activation function for ANN. More... | |

class | TMVA::Timer |

Timing information for training and evaluation of MVA methods. More... | |

class | TMVA |

Base Class for all classes that need option parsing. More... | |

class | TMVA::TNeuron |

Neuron class used by TMVA artificial neural network methods. More... | |

class | TMVA::TNeuronInput |

Interface for TNeuron input calculation classes. More... | |

class | TMVA::TNeuronInputAbs |

TNeuron input calculator – calculates the sum of the absolute values of the weighted inputs. More... | |

class | TMVA::TNeuronInputChooser |

Class for easily choosing neuron input functions. More... | |

class | TMVA::TNeuronInputSqSum |

TNeuron input calculator – calculates the squared weighted sum of inputs. More... | |

class | TMVA::TNeuronInputSum |

TNeuron input calculator – calculates the weighted sum of inputs. More... | |

class | TMVA::Tools |

Global auxiliary applications and data treatment routines. More... | |

class | TMVA::TrainingHistory |

Tracking data from training. More... | |

class | TMVA::TransformationHandler |

Class that contains all the data information. More... | |

class | TMVA::TSpline1 |

Linear interpolation of TGraph. More... | |

class | TMVA::TSpline2 |

Quadratic interpolation of TGraph. More... | |

class | TMVA::TSynapse |

Synapse class used by TMVA artificial neural network methods. More... | |

class | TMVA::Types |

Singleton class for Global types used by TMVA. More... | |

class | TMVA::VariableDecorrTransform |

Linear interpolation class. More... | |

class | TMVA::VariableGaussTransform |

Gaussian Transformation of input variables. More... | |

class | TMVA::VariableIdentityTransform |

Linear interpolation class. More... | |

class | TMVA::VariableImportance |

class | TMVA::VariableImportanceResult |

class | TMVA::VariableInfo |

Class for type info of MVA input variable. More... | |

class | TMVA::VariableNormalizeTransform |

Linear interpolation class. More... | |

class | TMVA::VariablePCATransform |

Linear interpolation class. More... | |

class | TMVA::VariableRearrangeTransform |

Rearrangement of input variables. More... | |

class | TMVA::VariableTransformBase |

Linear interpolation class. More... | |

class | TMVA::Volume |

Volume for BinarySearchTree. More... | |