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ROOT 6.10/09 Reference Guide |
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ROOT 6.10/09 Reference Guide |
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 | 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 | TMVA::CrossEntropy |
| Implementation of the CrossEntropy as separation criterion. More... | |
| class | TMVA::CrossValidation |
| class | TMVA::CrossValidationResult |
| 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 |
| Base class for all machine learning algorithms. 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 | TMVA::kNN |
| This file contains binary tree and global function template that searches tree for k-nearest neigbors. 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::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::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::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::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... | |
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