Index of TMVA

This directory contains the TMVA Multi-Variate-Analysis classes.

• The full description of the Multi Variate Analysis package
• The TMVA Users Guide

Class Index

• TMVA
• TMVA::BinarySearchTree Binary search tree including volume search method
• TMVA::BinarySearchTreeNode Node for the BinarySearchTree
• TMVA::BinaryTree Base class for BinarySearch and Decision Trees
• TMVA::Config Singleton class for global configuration settings
• TMVA::Config::IONames
• TMVA::Config::VariablePlotting
• TMVA::Configurable Virtual base class for all TMVA method
• TMVA::CrossEntropy Implementation of the CrossEntropy as separation criterion
• TMVA::DataSet
• TMVA::DecisionTree Implementation of a Decision Tree
• TMVA::DecisionTreeNode Node for the Decision Tree
• TMVA::Event
• TMVA::Factory The factory creates all MVA methods, and performs their training and testing
• TMVA::FitterBase Baseclass for fitters
• TMVA::GeneticAlgorithm Genetic algorithm controller
• TMVA::GeneticFitter Fitter using a Genetic Algorithm
• TMVA::GeneticGenes Genes definition for genetic algorithm
• TMVA::GeneticPopulation Population definition for genetic algorithm
• TMVA::GeneticRange Range definition for genetic algorithm
• TMVA::GiniIndex Implementation of the GiniIndex as separation criterion
• TMVA::IFitterTarget base class for a fitter "target"
• TMVA::IMethod Method Interface
• TMVA::IMetric calculates the "distance" between two points
• TMVA::Interval Interval definition, continous and discrete
• TMVA::KDEKernel Kernel density estimator for PDF smoothing
• TMVA::MCFitter Fitter using Monte Carlo sampling of parameters
• TMVA::MethodANNBase Base class for TMVA ANNs
• TMVA::MethodBDT Analysis of Boosted Decision Trees
• TMVA::MethodBase Virtual base class for all TMVA method
• TMVA::MethodBayesClassifier Friedman's BayesClassifier method
• TMVA::MethodCFMlpANN Interface for Clermond-Ferrand artificial neural network
• TMVA::MethodCFMlpANN_Utils Implementation of Clermond-Ferrand artificial neural network
• TMVA::MethodCommittee Analysis of Boosted MVA methods
• TMVA::MethodCuts Multivariate optimisation of signal efficiency
• TMVA::MethodFDA Function Discriminant Analysis
• TMVA::MethodFisher Analysis of Fisher discriminant (Fisher or Mahalanobis approach)
• TMVA::MethodHMatrix H-Matrix method, a simple comparison of chi-squared estimators for signal and background
• TMVA::MethodKNN k Nearest Neighbour classifier
• TMVA::MethodLikelihood Likelihood analysis A ("non-parametric approach")
• TMVA::MethodMLP Multi-layer perceptron implemented specifically for TMVA
• TMVA::MethodPDERS Multi-dimensional probability density estimator range search (PDERS) method
• TMVA::MethodRuleFit Friedman's RuleFit method
• TMVA::MethodSVM Support Vector Machine
• TMVA::MethodSeedDistance Function Discriminant Analysis
• TMVA::MethodTMlpANN Implementation of interface for TMultiLayerPerceptron
• TMVA::MethodVariable Wrapper class for a single variable "MVA"
• TMVA::MetricEuler calculates the "distance" between two points
• TMVA::MetricManhattan calculates the "distance" between two points
• TMVA::MinuitFitter Fitter using a Genetic Algorithm
• TMVA::MinuitWrapper Wrapper around TMinuit
• TMVA::MisClassificationError Implementation of the MisClassificationError as separation criterion
• TMVA::MsgLogger Ostringstream derivative to redirect and format logging output
• TMVA::Node Node for the BinarySearch or Decision Trees
• TMVA::PDF PDF wrapper for histograms
• TMVA::Ranking Method-specific ranking for input variables
• TMVA::Reader Interpret the trained MVAs in an analysis context
• TMVA::RootFinder Root finding using Brents algorithm
• TMVA::RuleFit Calculations for Friedman's RuleFit method
• TMVA::RuleFitAPI Friedman's RuleFit method
• TMVA::SdivSqrtSplusB Implementation of the SdivSqrtSplusB as separation criterion
• TMVA::SeedDistance Seed and metric storage
• TMVA::SeparationBase Interface to different separation critiera used in training algorithms
• TMVA::SimulatedAnnealing Base class for Simulated Annealing fitting
• TMVA::SimulatedAnnealingFitter Fitter using a Simulated Annealing Algorithm
• TMVA::TActivation Interface for TNeuron activation function classes
• TMVA::TActivationChooser Class for choosing activation functions
• TMVA::TActivationIdentity Identity activation function for TNeuron
• TMVA::TActivationRadial Radial basis activation function for TNeuron
• TMVA::TActivationSigmoid Sigmoid activation function for TNeuron
• TMVA::TActivationTanh Tanh sigmoid activation function for TNeuron
• TMVA::TNeuron Neuron class used by MethodANNBase derivative ANNs
• TMVA::TNeuronInput Interface for TNeuron input calculation classes
• TMVA::TNeuronInputAbs Calculates the sum of the absolute values of the weighted inputs
• TMVA::TNeuronInputChooser Class for choosing neuron input functions
• TMVA::TNeuronInputSqSum Calculates square of weighted sum of neuron inputs
• TMVA::TNeuronInputSum Calculates weighted sum of neuron inputs
• TMVA::TSpline1 Linear interpolation class
• TMVA::TSpline2 Quadratic interpolation class (using quadrax)
• TMVA::TSynapse Synapse class used by MethodANNBase and derivatives
• TMVA::Timer Timing information for training and evaluation of MVA methods
• TMVA::Types
• TMVA::VariableDecorrTransform Variable transformation: decorrelation
• TMVA::VariableIdentityTransform Variable transformation: identity
• TMVA::VariablePCATransform Variable transformation: Principal Value Composition
• TMVA::VariableTransformBase Base class for variable transformations
• TMVA::kNN::Event