Abstract ClassifierFactory template that handles arbitrary types. More...

Namespaces
	DNN

	kNN

	TMVAGlob

Classes
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	compose_binary_t

class	compose_unary_t

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	CrossValidationResult

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
	Base class for all machine learning algorithms. More...

class	Event

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	kNN
	This file contains binary tree and global function template that searches tree for k-nearest neigbors. 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	MethodCuts
	Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular minimum and maximum requirements. More...

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	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	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...

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...

Functions
void	ActionButton (TControlBar *cbar, const TString &title, const TString &macro, 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, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)

void	boostcontrolplots (TString dataset, TDirectory *boostdir)

void	compareanapp (TString finAn="TMVA.root", TString finApp="TMVApp.root", HistType htype=kMVAType, bool useTMVAStyle=kTRUE)

template<typename F , typename G , typename H >
compose_binary_t< F, G, H >	compose_binary (const F &_f, const G &_g, const H &_h)

template<typename F , typename G >
compose_unary_t< F, G >	compose_unary (const F &_f, const G &_g)

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	efficienciesMulticlass (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<typename T >
T	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue)

template<>
int	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue)

template<>
double	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue)

template<>
TString	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue)

template<>
bool	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue)

template<>
std::vector< double >	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue)

Config &	gConfig ()

TString *	get_var_names (TString dataset, Int_t nVars)

Int_t	getBkgColorF ()

Int_t	getBkgColorT ()

Int_t	getIntColorF ()

Int_t	getIntColorT ()

TList *	GetKeyList (const TString &pattern)

Int_t	getSigColorF ()

Int_t	getSigColorT ()

Tools &	gTools ()

Int_t	LargestCommonDivider (Int_t a, Int_t b)

void	likelihoodrefs (TString dataset, TDirectory *lhdir)

void	likelihoodrefs (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)

void	MovieMaker (TString dataset, TString methodType="Method_MLP", TString methodTitle="MLP")

void	MultiClassActionButton (TControlBar *cbar, const TString &title, const TString &macro, const TString &comment, const TString &buttonType, TString requiredKey="")

TList *	MultiClassGetKeyList (const TString &pattern)

void	mvaeffs (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE, TString formula="S/sqrt(S+B)")

void	mvas (TString dataset, TString fin="TMVA.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE)

void	mvasMulticlass (TString dataset, TString fin="TMVAMulticlass.root", HistType htype=kMVAType, Bool_t useTMVAStyle=kTRUE)

void	mvaweights (TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)

void	network (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)

template<typename F >
null_t< F >	null ()

Bool_t	operator< (const GeneticGenes &, const GeneticGenes &)

std::ostream &	operator<< (std::ostream &os, const Rule &rule)

std::ostream &	operator<< (std::ostream &os, const Event &event)

std::ostream &	operator<< (std::ostream &os, const Node &node)

std::ostream &	operator<< (std::ostream &os, const Node *node)

std::ostream &	operator<< (std::ostream &os, const RuleEnsemble &event)

std::ostream &	operator<< (std::ostream &os, const BinaryTree &tree)

std::ostream &	operator<< (std::ostream &os, const PDF &tree)

std::istream &	operator>> (std::istream &istr, BinaryTree &tree)

std::istream &	operator>> (std::istream &istr, PDF &tree)

void	paracoor (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)

void	Plot (TString fileName, TMVA::ECellValue cv, TString cv_long, bool useTMVAStyle=kTRUE)

void	Plot1DimFoams (TList &foam_list, TMVA::ECellValue cell_value, const TString &cell_value_description, TMVA::PDEFoamKernelBase *kernel)

void	plot_efficiencies (TString dataset, TFile file, Int_t type=2, TDirectory BinDir=0)

void	PlotCellTree (TString fileName, TString cv_long, bool useTMVAStyle=kTRUE)

void	plotEfficienciesMulticlass (EEfficiencyPlotType plotType=EEfficiencyPlotType::kRejBvsEffS, TDirectory *BinDir=0)

void	PlotFoams (TString fileName="weights/TMVAClassification_PDEFoam.weights_foams.root", bool useTMVAStyle=kTRUE)

void	PlotNDimFoams (TList &foam_list, TMVA::ECellValue cell_value, const TString &cell_value_description, TMVA::PDEFoamKernelBase *kernel)

void	probas (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)

void	RegGuiActionButton (TControlBar *cbar, const TString &title, const TString &macro, const TString &comment, const TString &buttonType, TString requiredKey="")

TList *	RegGuiGetKeyList (const TString &pattern)

void	regression_averagedevs (TString dataset, TString fin, Int_t Nevt=-1, Bool_t useTMVAStyle=kTRUE)

void	rulevis (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)

void	rulevisCorr (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)

void	rulevisCorr (TDirectory rfdir, TDirectory vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type)

void	rulevisHists (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)

void	rulevisHists (TDirectory rfdir, TDirectory vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type)

void	TMVAGui (const char *fName="TMVA.root", TString dataset="")

void	TMVAMultiClassGui (const char *fName="TMVAMulticlass.root", TString dataset="")

void	TMVARegGui (const char *fName="TMVAReg.root", TString dataset="")

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)

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)

Detailed Description

Abstract ClassifierFactory template that handles arbitrary types.

create variable transformations

This template creates ClassifierFactory stores creator functors to template parameter class. ClassifierFactory is a singelton class which is explicitly deleted.

Source: Andrei Alexandrescu, Modern C++ Design

Function Documentation

◆ ActionButton()

void TMVA::ActionButton	(	TControlBar *	cbar,
		const TString &	title,
		const TString &	macro,
		const TString &	comment,
		const TString &	buttonType,
		TString	requiredKey = `""`
	)

◆ annconvergencetest() [1/2]

void TMVA::annconvergencetest	(	TString	dataset,
		TDirectory *	lhdir
	)

◆ annconvergencetest() [2/2]

void TMVA::annconvergencetest	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ BDT() [1/2]

void TMVA::BDT	(	TString	dataset,
		const TString &	fin = `"TMVA.root"`
	)

◆ BDT() [2/2]

void TMVA::BDT	(	TString	dataset,
		Int_t	itree,
		TString	wfile,
		TString	methName = `"BDT"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ BDT_DeleteTBar()

void TMVA::BDT_DeleteTBar ( int i )

◆ BDT_Reg() [1/2]

void TMVA::BDT_Reg	(	TString	dataset,
		const TString &	fin = `"TMVAReg.root"`
	)

◆ BDT_Reg() [2/2]

void TMVA::BDT_Reg	(	TString	dataset,
		Int_t	itree,
		TString	wfile = `""`,
		TString	methName = `"BDT"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ bdtcontrolplots()

void TMVA::bdtcontrolplots	(	TString	dataset,
		TDirectory *
	)

◆ BDTControlPlots()

void TMVA::BDTControlPlots	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ BDTReg_DeleteTBar()

void TMVA::BDTReg_DeleteTBar ( int i )

◆ BoostControlPlots()

void TMVA::BoostControlPlots	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ boostcontrolplots()

void TMVA::boostcontrolplots	(	TString	dataset,
		TDirectory *	boostdir
	)

◆ compareanapp()

void TMVA::compareanapp	(	TString	finAn = `"TMVA.root"`,
		TString	finApp = `"TMVApp.root"`,
		HistType	htype = `kMVAType`,
		bool	useTMVAStyle = `kTRUE`
	)

◆ compose_binary()

template<typename F , typename G , typename H >

compose_binary_t<F,G,H> TMVA::compose_binary	(	const F &	_f,
		const G &	_g,
		const H &	_h
	)

inline

Definition at line 152 of file DataSetFactory.h.

◆ compose_unary()

template<typename F , typename G >

compose_unary_t<F,G> TMVA::compose_unary	(	const F &	_f,
		const G &	_g
	)

inline

Definition at line 178 of file DataSetFactory.h.

◆ correlations()

void TMVA::correlations	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	isRegression = `kFALSE`,
		Bool_t	greyScale = `kFALSE`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ correlationscatters()

void TMVA::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`
	)

◆ correlationscattersMultiClass()

void TMVA::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`
	)

◆ correlationsMultiClass()

void TMVA::correlationsMultiClass	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	isRegression = `kFALSE`,
		Bool_t	greyScale = `kFALSE`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ CorrGui()

void TMVA::CorrGui	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		TString	dirName = `"InputVariables_Id"`,
		TString	title = `"TMVA Input Variable"`,
		Bool_t	isRegression = `kFALSE`
	)

◆ CorrGui_DeleteTBar()

void TMVA::CorrGui_DeleteTBar ( )

◆ CorrGuiMultiClass()

void TMVA::CorrGuiMultiClass	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		TString	dirName = `"InputVariables_Id"`,
		TString	title = `"TMVA Input Variable"`,
		Bool_t	isRegression = `kFALSE`
	)

◆ CorrGuiMultiClass_DeleteTBar()

void TMVA::CorrGuiMultiClass_DeleteTBar ( )

◆ CreateVariableTransforms()

void TMVA::CreateVariableTransforms	(	const TString &	trafoDefinition,
		TMVA::DataSetInfo &	dataInfo,
		TMVA::TransformationHandler &	transformationHandler,
		TMVA::MsgLogger &	log
	)

Definition at line 67 of file VariableTransform.cxx.

◆ DataLoaderCopy()

void TMVA::DataLoaderCopy	(	TMVA::DataLoader *	des,
		TMVA::DataLoader *	src
	)

◆ DeleteFunctor()

template<class T >

DeleteFunctor_t<const T> TMVA::DeleteFunctor ( )

Definition at line 92 of file DataSetFactory.h.

◆ deviations()

void TMVA::deviations	(	TString	dataset,
		TString	fin = `"TMVAReg.root"`,
		HistType	htype = `kMVAType`,
		Bool_t	showTarget = `kTRUE`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ draw_activation()

void TMVA::draw_activation	(	TCanvas *	c,
		Double_t	cx,
		Double_t	cy,
		Double_t	radx,
		Double_t	rady,
		Int_t	whichActivation
	)

◆ draw_input_labels()

void TMVA::draw_input_labels	(	TString	dataset,
		Int_t	nInputs,
		Double_t *	cy,
		Double_t	rad,
		Double_t	layerWidth
	)

◆ draw_layer()

void TMVA::draw_layer	(	TString	dataset,
		TCanvas *	c,
		TH2F *	h,
		Int_t	iHist,
		Int_t	nLayers,
		Double_t	maxWeight
	)

◆ draw_layer_labels()

void TMVA::draw_layer_labels ( Int_t nLayers )

◆ draw_network()

void TMVA::draw_network	(	TString	dataset,
		TFile *	f,
		TDirectory *	d,
		const TString &	hName = `"weights_hist"`,
		Bool_t	movieMode = `kFALSE`,
		const TString &	epoch = `""`
	)

◆ draw_synapse()

void TMVA::draw_synapse	(	Double_t	cx1,
		Double_t	cy1,
		Double_t	cx2,
		Double_t	cy2,
		Double_t	rad1,
		Double_t	rad2,
		Double_t	weightNormed
	)

◆ DrawCell()

void TMVA::DrawCell	(	TMVA::PDEFoamCell *	cell,
		TMVA::PDEFoam *	foam,
		Double_t	x,
		Double_t	y,
		Double_t	xscale,
		Double_t	yscale
	)

◆ DrawMLPoutputMovie()

void TMVA::DrawMLPoutputMovie	(	TString	dataset,
		TFile *	file,
		const TString &	methodType,
		const TString &	methodTitle
	)

◆ DrawNetworkMovie()

void TMVA::DrawNetworkMovie	(	TString	dataset,
		TFile *	file,
		const TString &	methodType,
		const TString &	methodTitle
	)

◆ efficiencies()

void TMVA::efficiencies	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Int_t	type = `2`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ efficienciesMulticlass()

void TMVA::efficienciesMulticlass	(	TString	dataset,
		TString	filename_input = `"TMVAMulticlass.root"`,
		EEfficiencyPlotType	plotType = `EEfficiencyPlotType::kRejBvsEffS`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ Endl()

MsgLogger& TMVA::Endl ( MsgLogger & ml )

inline

Definition at line 158 of file MsgLogger.h.

◆ fetchValue() [1/7]

TString TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key
	)

Definition at line 307 of file MethodDNN.cxx.

◆ fetchValue() [2/7]

template<typename T >

T TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key,
		T	defaultValue
	)

◆ fetchValue() [3/7]

template<>

int TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key,
		int	defaultValue
	)

Definition at line 327 of file MethodDNN.cxx.

◆ fetchValue() [4/7]

template<>

double TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key,
		double	defaultValue
	)

Definition at line 341 of file MethodDNN.cxx.

◆ fetchValue() [5/7]

template<>

TString TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key,
		TString	defaultValue
	)

Definition at line 354 of file MethodDNN.cxx.

◆ fetchValue() [6/7]

template<>

bool TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key,
		bool	defaultValue
	)

Definition at line 367 of file MethodDNN.cxx.

◆ fetchValue() [7/7]

template<>

std::vector<double> TMVA::fetchValue	(	const std::map< TString, TString > &	keyValueMap,
		TString	key,
		std::vector< double >	defaultValue
	)

Definition at line 384 of file MethodDNN.cxx.

◆ gConfig()

Config& TMVA::gConfig ( )

◆ get_var_names()

TString* TMVA::get_var_names	(	TString	dataset,
		Int_t	nVars
	)

◆ getBkgColorF()

Int_t TMVA::getBkgColorF ( )

inline

Definition at line 41 of file BDT.h.

◆ getBkgColorT()

Int_t TMVA::getBkgColorT ( )

inline

Definition at line 46 of file BDT.h.

◆ getIntColorF()

Int_t TMVA::getIntColorF ( )

inline

Definition at line 42 of file BDT.h.

◆ getIntColorT()

Int_t TMVA::getIntColorT ( )

inline

Definition at line 47 of file BDT.h.

◆ GetKeyList()

TList* TMVA::GetKeyList ( const TString & pattern )

◆ getSigColorF()

Int_t TMVA::getSigColorF ( )

inline

Definition at line 40 of file BDT.h.

◆ getSigColorT()

Int_t TMVA::getSigColorT ( )

inline

Definition at line 45 of file BDT.h.

◆ gTools()

Tools& TMVA::gTools ( )

◆ LargestCommonDivider()

Int_t TMVA::LargestCommonDivider	(	Int_t	a,
		Int_t	b
	)

Definition at line 80 of file DataSetFactory.cxx.

◆ likelihoodrefs() [1/2]

void TMVA::likelihoodrefs	(	TString	dataset,
		TDirectory *	lhdir
	)

◆ likelihoodrefs() [2/2]

void TMVA::likelihoodrefs	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ MovieMaker()

void TMVA::MovieMaker	(	TString	dataset,
		TString	methodType = `"Method_MLP"`,
		TString	methodTitle = `"MLP"`
	)

◆ MultiClassActionButton()

void TMVA::MultiClassActionButton	(	TControlBar *	cbar,
		const TString &	title,
		const TString &	macro,
		const TString &	comment,
		const TString &	buttonType,
		TString	requiredKey = `""`
	)

◆ MultiClassGetKeyList()

TList* TMVA::MultiClassGetKeyList ( const TString & pattern )

◆ mvaeffs()

void TMVA::mvaeffs	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`,
		TString	formula = `"S/sqrt(S+B)"`
	)

◆ mvas()

void TMVA::mvas	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		HistType	htype = `kMVAType`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ mvasMulticlass()

void TMVA::mvasMulticlass	(	TString	dataset,
		TString	fin = `"TMVAMulticlass.root"`,
		HistType	htype = `kMVAType`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ mvaweights()

void TMVA::mvaweights	(	TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ network()

void TMVA::network	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ null()

template<typename F >

null_t<F> TMVA::null ( )

inline

Definition at line 124 of file DataSetFactory.h.

◆ operator<()

Bool_t TMVA::operator<	(	const GeneticGenes &	,
		const GeneticGenes &
	)

◆ operator<<() [1/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const Rule &	rule
	)

◆ operator<<() [2/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const Event &	event
	)

◆ operator<<() [3/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const Node &	node
	)

◆ operator<<() [4/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const Node *	node
	)

◆ operator<<() [5/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const RuleEnsemble &	event
	)

◆ operator<<() [6/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const BinaryTree &	tree
	)

◆ operator<<() [7/7]

std::ostream& TMVA::operator<<	(	std::ostream &	os,
		const PDF &	tree
	)

◆ operator>>() [1/2]

std::istream& TMVA::operator>>	(	std::istream &	istr,
		BinaryTree &	tree
	)

◆ operator>>() [2/2]

std::istream& TMVA::operator>>	(	std::istream &	istr,
		PDF &	tree
	)

◆ paracoor()

void TMVA::paracoor	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ Plot()

void TMVA::Plot	(	TString	fileName,
		TMVA::ECellValue	cv,
		TString	cv_long,
		bool	useTMVAStyle = `kTRUE`
	)

◆ Plot1DimFoams()

void TMVA::Plot1DimFoams	(	TList &	foam_list,
		TMVA::ECellValue	cell_value,
		const TString &	cell_value_description,
		TMVA::PDEFoamKernelBase *	kernel
	)

◆ plot_efficiencies()

void TMVA::plot_efficiencies	(	TString	dataset,
		TFile *	file,
		Int_t	type = `2`,
		TDirectory *	BinDir = `0`
	)

◆ PlotCellTree()

void TMVA::PlotCellTree	(	TString	fileName,
		TString	cv_long,
		bool	useTMVAStyle = `kTRUE`
	)

◆ plotEfficienciesMulticlass()

void TMVA::plotEfficienciesMulticlass	(	EEfficiencyPlotType	plotType = `EEfficiencyPlotType::kRejBvsEffS`,
		TDirectory *	BinDir = `0`
	)

◆ PlotFoams()

void TMVA::PlotFoams	(	TString	fileName = `"weights/TMVAClassification_PDEFoam.weights_foams.root"`,
		bool	useTMVAStyle = `kTRUE`
	)

◆ PlotNDimFoams()

void TMVA::PlotNDimFoams	(	TList &	foam_list,
		TMVA::ECellValue	cell_value,
		const TString &	cell_value_description,
		TMVA::PDEFoamKernelBase *	kernel
	)

◆ probas()

void TMVA::probas	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ RegGuiActionButton()

void TMVA::RegGuiActionButton	(	TControlBar *	cbar,
		const TString &	title,
		const TString &	macro,
		const TString &	comment,
		const TString &	buttonType,
		TString	requiredKey = `""`
	)

◆ RegGuiGetKeyList()

TList* TMVA::RegGuiGetKeyList ( const TString & pattern )

◆ regression_averagedevs()

void TMVA::regression_averagedevs	(	TString	dataset,
		TString	fin,
		Int_t	Nevt = `-1`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ rulevis()

void TMVA::rulevis	(	TString	fin = `"TMVA.root"`,
		TMVAGlob::TypeOfPlot	type = `TMVAGlob::kNorm`,
		bool	useTMVAStyle = `kTRUE`
	)

◆ rulevisCorr() [1/2]

void TMVA::rulevisCorr	(	TString	fin = `"TMVA.root"`,
		TMVAGlob::TypeOfPlot	type = `TMVAGlob::kNorm`,
		bool	useTMVAStyle = `kTRUE`
	)

◆ rulevisCorr() [2/2]

void TMVA::rulevisCorr	(	TDirectory *	rfdir,
		TDirectory *	vardir,
		TDirectory *	corrdir,
		TMVAGlob::TypeOfPlot	type
	)

◆ rulevisHists() [1/2]

void TMVA::rulevisHists	(	TString	fin = `"TMVA.root"`,
		TMVAGlob::TypeOfPlot	type = `TMVAGlob::kNorm`,
		bool	useTMVAStyle = `kTRUE`
	)

◆ rulevisHists() [2/2]

void TMVA::rulevisHists	(	TDirectory *	rfdir,
		TDirectory *	vardir,
		TDirectory *	corrdir,
		TMVAGlob::TypeOfPlot	type
	)

◆ TMVAGui()

void TMVA::TMVAGui	(	const char *	fName = `"TMVA.root"`,
		TString	dataset = `""`
	)

◆ TMVAMultiClassGui()

void TMVA::TMVAMultiClassGui	(	const char *	fName = `"TMVAMulticlass.root"`,
		TString	dataset = `""`
	)

◆ TMVARegGui()

void TMVA::TMVARegGui	(	const char *	fName = `"TMVAReg.root"`,
		TString	dataset = `""`
	)

◆ variables()

void TMVA::variables	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		TString	dirName = `"InputVariables_Id"`,
		TString	title = `"TMVA Input Variables"`,
		Bool_t	isRegression = `kFALSE`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

◆ variablesMultiClass()

void TMVA::variablesMultiClass	(	TString	dataset,
		TString	fin = `"TMVA.root"`,
		TString	dirName = `"InputVariables_Id"`,
		TString	title = `"TMVA Input Variables"`,
		Bool_t	isRegression = `kFALSE`,
		Bool_t	useTMVAStyle = `kTRUE`
	)

Namespaces

Classes

Functions

Detailed Description

Function Documentation

◆ ActionButton()

◆ annconvergencetest() [1/2]

◆ annconvergencetest() [2/2]

◆ BDT() [1/2]

◆ BDT() [2/2]

◆ BDT_DeleteTBar()

◆ BDT_Reg() [1/2]

◆ BDT_Reg() [2/2]

◆ bdtcontrolplots()

◆ BDTControlPlots()

◆ BDTReg_DeleteTBar()

◆ BoostControlPlots()

◆ boostcontrolplots()

◆ compareanapp()

◆ compose_binary()

◆ compose_unary()

◆ correlations()

◆ correlationscatters()

◆ correlationscattersMultiClass()

◆ correlationsMultiClass()

◆ CorrGui()

◆ CorrGui_DeleteTBar()

◆ CorrGuiMultiClass()

◆ CorrGuiMultiClass_DeleteTBar()

◆ CreateVariableTransforms()

◆ DataLoaderCopy()

◆ DeleteFunctor()

◆ deviations()

◆ draw_activation()

◆ draw_input_labels()

◆ draw_layer()

◆ draw_layer_labels()

◆ draw_network()

◆ draw_synapse()

◆ DrawCell()

◆ DrawMLPoutputMovie()

◆ DrawNetworkMovie()

◆ efficiencies()

◆ efficienciesMulticlass()

◆ Endl()

◆ fetchValue() [1/7]

◆ fetchValue() [2/7]

◆ fetchValue() [3/7]

◆ fetchValue() [4/7]

◆ fetchValue() [5/7]

◆ fetchValue() [6/7]

◆ fetchValue() [7/7]

◆ gConfig()

◆ get_var_names()

◆ getBkgColorF()

◆ getBkgColorT()

◆ getIntColorF()

◆ getIntColorT()

◆ GetKeyList()

◆ getSigColorF()

◆ getSigColorT()

◆ gTools()

◆ LargestCommonDivider()

◆ likelihoodrefs() [1/2]

◆ likelihoodrefs() [2/2]

◆ MovieMaker()

◆ MultiClassActionButton()

◆ MultiClassGetKeyList()

◆ mvaeffs()

◆ mvas()

◆ mvasMulticlass()

◆ mvaweights()

◆ network()

◆ null()

◆ operator<()

◆ operator<<() [1/7]

◆ operator<<() [2/7]

◆ operator<<() [3/7]

◆ operator<<() [4/7]

◆ operator<<() [5/7]