TMVA Namespace Reference

create variable transformations More...

Namespaces
namespace	DNN
namespace	Experimental
namespace	Internal
namespace	kNN
namespace	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	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 to perform cross validation, splitting the dataloader into folds. More...
class	CrossValidationFoldResult
class	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	CvSplit
class	CvSplitKFolds
class	CvSplitKFoldsExpr
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
	Abstract base class for all high level ml algorithms, you can book ml methods like BDT, MLP. More...
class	Event
class	Executor
	Base Excutor class. More...
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	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	MethodCrossValidation
class	MethodCuts
	Multivariate optimisation of signal efficiency for given background efficiency, applying rectangular minimum and maximum requirements. More...
class	MethodDL
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	MethodPyTorch
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	TrainingHistory
	Tracking data from training. 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...
struct	TTrainingSettings
	All of the options that can be specified in the training string. 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, TDirectory *boostdir)
void	BoostControlPlots (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
void	compareanapp (TString finAn="TMVA.root", TString finApp="TMVApp.root", HistType htype=kMVAType, bool useTMVAStyle=kTRUE)
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	efficienciesMulticlass1vs1 (TString dataset, TString fin)
void	efficienciesMulticlass1vsRest (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<>
bool	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue)
template<>
double	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue)
template<>
int	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue)
template<>
std::vector< double >	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue)
template<typename T >
T	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue)
template<>
TString	fetchValue (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue)
TString	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key)
template<>
bool	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, bool defaultValue)
template<>
double	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, double defaultValue)
template<>
int	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, int defaultValue)
template<>
std::vector< double >	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, std::vector< double > defaultValue)
template<typename T >
T	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, T defaultValue)
template<>
TString	fetchValueTmp (const std::map< TString, TString > &keyValueMap, TString key, TString defaultValue)
Config &	gConfig ()
TString *	get_var_names (TString dataset, Int_t nVars)
Int_t	getBkgColorF ()
Int_t	getBkgColorT ()
std::vector< TString >	getclassnames (TString dataset, TString fin)
Int_t	getIntColorF ()
Int_t	getIntColorT ()
TList *	GetKeyList (const TString &pattern)
roccurvelist_t	getRocCurves (TDirectory *binDir, TString methodPrefix, TString graphNameRef)
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", Float_t nSignal=1000, Float_t nBackground=1000, 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 BinaryTree &tree)
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 PDF &tree)
std::ostream &	operator<< (std::ostream &os, const Rule &rule)
std::ostream &	operator<< (std::ostream &os, const RuleEnsemble &event)
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	plot_training_history (TString dataset, TFile *file, TDirectory *BinDir=0)
void	PlotCellTree (TString fileName, TString cv_long, bool useTMVAStyle=kTRUE)
void	plotEfficienciesMulticlass (roccurvelist_t rocCurves, classcanvasmap_t classCanvasMap)
void	plotEfficienciesMulticlass1vs1 (TString dataset, TString fin, TString baseClassname)
void	plotEfficienciesMulticlass1vsRest (TString dataset, EEfficiencyPlotType plotType=EEfficiencyPlotType::kRejBvsEffS, TString filename_input="TMVAMulticlass.root")
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)
TString	Python_Executable ()
	Function to find current Python executable used by ROOT If Python2 is installed return "python" Instead if "Python3" return "python3".
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 (TDirectory *rfdir, TDirectory *vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type)
void	rulevisCorr (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)
void	rulevisHists (TDirectory *rfdir, TDirectory *vardir, TDirectory *corrdir, TMVAGlob::TypeOfPlot type)
void	rulevisHists (TString fin="TMVA.root", TMVAGlob::TypeOfPlot type=TMVAGlob::kNorm, bool useTMVAStyle=kTRUE)
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	training_history (TString dataset, TString fin="TMVA.root", Bool_t useTMVAStyle=kTRUE)
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

create variable transformations

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,
		TDirectory *	boostdir
	)

BoostControlPlots()

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

compareanapp()

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

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 59 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 78 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
	)

efficienciesMulticlass1vs1()

void TMVA::efficienciesMulticlass1vs1	(	TString	dataset,
		TString	fin
	)

efficienciesMulticlass1vsRest()

void TMVA::efficienciesMulticlass1vsRest	(	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 148 of file MsgLogger.h.

fetchValue() [1/7]

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

Definition at line 320 of file MethodDNN.cxx.

fetchValue() [2/7]

template<>

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

Definition at line 380 of file MethodDNN.cxx.

fetchValue() [3/7]

template<>

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

Definition at line 354 of file MethodDNN.cxx.

fetchValue() [4/7]

template<>

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

Definition at line 340 of file MethodDNN.cxx.

fetchValue() [5/7]

template<>

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

Definition at line 397 of file MethodDNN.cxx.

fetchValue() [6/7]

template<typename T >

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

fetchValue() [7/7]

template<>

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

Definition at line 367 of file MethodDNN.cxx.

fetchValueTmp() [1/7]

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

Definition at line 75 of file MethodDL.cxx.

fetchValueTmp() [2/7]

template<>

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

Definition at line 124 of file MethodDL.cxx.

fetchValueTmp() [3/7]

template<>

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

Definition at line 102 of file MethodDL.cxx.

fetchValueTmp() [4/7]

template<>

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

Definition at line 91 of file MethodDL.cxx.

fetchValueTmp() [5/7]

template<>

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

Definition at line 141 of file MethodDL.cxx.

fetchValueTmp() [6/7]

template<typename T >

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

fetchValueTmp() [7/7]

template<>

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

Definition at line 113 of file MethodDL.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 36 of file BDT.h.

getBkgColorT()

Int_t TMVA::getBkgColorT ( )

inline

Definition at line 41 of file BDT.h.

getclassnames()

std::vector< TString > TMVA::getclassnames	(	TString	dataset,
		TString	fin
	)

getIntColorF()

Int_t TMVA::getIntColorF ( )

inline

Definition at line 37 of file BDT.h.

getIntColorT()

Int_t TMVA::getIntColorT ( )

inline

Definition at line 42 of file BDT.h.

GetKeyList()

TList * TMVA::GetKeyList

(

const TString &

pattern

)

getRocCurves()

roccurvelist_t TMVA::getRocCurves	(	TDirectory *	binDir,
		TString	methodPrefix,
		TString	graphNameRef
	)

getSigColorF()

Int_t TMVA::getSigColorF ( )

inline

Definition at line 35 of file BDT.h.

getSigColorT()

Int_t TMVA::getSigColorT ( )

inline

Definition at line 40 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",
		Float_t	nSignal = 1000,
		Float_t	nBackground = 1000,
		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 110 of file DataSetFactory.h.

operator<()

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

operator<<() [1/7]

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

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 PDF &	tree
	)

operator<<() [6/7]

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

operator<<() [7/7]

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

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
	)

plot_training_history()

void TMVA::plot_training_history	(	TString	dataset,
		TFile *	file,
		TDirectory *	BinDir = 0
	)

PlotCellTree()

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

plotEfficienciesMulticlass()

void TMVA::plotEfficienciesMulticlass	(	roccurvelist_t	rocCurves,
		classcanvasmap_t	classCanvasMap
	)

plotEfficienciesMulticlass1vs1()

void TMVA::plotEfficienciesMulticlass1vs1	(	TString	dataset,
		TString	fin,
		TString	baseClassname
	)

plotEfficienciesMulticlass1vsRest()

void TMVA::plotEfficienciesMulticlass1vsRest	(	TString	dataset,
		EEfficiencyPlotType	plotType = EEfficiencyPlotType::kRejBvsEffS,
		TString	filename_input = "TMVAMulticlass.root"
	)

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
	)

Python_Executable()

TString TMVA::Python_Executable

(

)

Function to find current Python executable used by ROOT If Python2 is installed return "python" Instead if "Python3" return "python3".

get current Python executable used by ROOT

Definition at line 45 of file PyMethodBase.cxx.

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

rulevisCorr() [2/2]

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

rulevisHists() [1/2]

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

rulevisHists() [2/2]

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

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 = ""
	)

training_history()

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

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
	)