The Multi Variate Analysis package.

The TMVA Multi-Variate-Analysis classes.

See:

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...

Classes