Definition at line 63 of file MethodBDT.h.

Public Member Functions
	MethodBDT (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="", TDirectory *theTargetDir=0)

	MethodBDT (DataSetInfo &theData, const TString &theWeightFile, TDirectory *theTargetDir=NULL)

virtual	~MethodBDT (void)
	destructor Note: fEventSample and ValidationSample are already deleted at the end of TRAIN When they are not used anymore for (UInt_t i=0; i<fEventSample.size(); i++) delete fEventSample[i]; for (UInt_t i=0; i<fValidationSample.size(); i++) delete fValidationSample[i]; More...

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
	BDT can handle classification with multiple classes and regression with one regression-target. More...

void	InitEventSample ()
	initialize the event sample (i.e. reset the boost-weights... etc) More...

virtual std::map< TString, Double_t >	OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
	call the Optimzier with the set of paremeters and ranges that are meant to be tuned. More...

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	set the tuning parameters accoding to the argument More...

void	Train (void)
	BDT training. More...

void	Reset (void)
	reset the method, as if it had just been instantiated (forget all training etc.) More...

void	AddWeightsXMLTo (void *parent) const
	write weights to XML More...

void	ReadWeightsFromStream (std::istream &istr)
	read the weights (BDT coefficients) More...

void	ReadWeightsFromXML (void *parent)
	reads the BDT from the xml file More...

void	WriteMonitoringHistosToFile (void) const
	Here we could write some histograms created during the processing to the output file. More...

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)

UInt_t	GetNTrees () const

const std::vector< Float_t > &	GetMulticlassValues ()
	get the multiclass MVA response for the BDT classifier More...

const std::vector< Float_t > &	GetRegressionValues ()
	get the regression value generated by the BDTs More...

Double_t	Boost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
	apply the boosting alogrithim (the algorithm is selecte via the the "option" given in the constructor. More...

const Ranking *	CreateRanking ()
	Compute ranking of input variables. More...

void	DeclareOptions ()
	define the options (their key words) that can be set in the option string know options: nTrees number of trees in the forest to be created BoostType the boosting type for the trees in the forest (AdaBoost e.t.c..) known: AdaBoost AdaBoostR2 (Adaboost for regression) Bagging GradBoost AdaBoostBeta the boosting parameter, beta, for AdaBoost UseRandomisedTrees choose at each node splitting a random set of variables UseNvars use UseNvars variables in randomised trees UsePoission Nvars use UseNvars not as fixed number but as mean of a possion distribution SeparationType the separation criterion applied in the node splitting known: GiniIndex MisClassificationError CrossEntropy SDivSqrtSPlusB MinNodeSize: minimum percentage of training events in a leaf node (leaf criteria, stop splitting) nCuts: the number of steps in the optimisation of the cut for a node (if < 0, then step size is determined by the events) UseFisherCuts: use multivariate splits using the Fisher criterion UseYesNoLeaf decide if the classification is done simply by the node type, or the S/B (from the training) in the leaf node NodePurityLimit the minimum purity to classify a node as a signal node (used in pruning and boosting to determine misclassification error rate) PruneMethod The Pruning method: known: NoPruning // switch off pruning completely ExpectedError CostComplexity PruneStrength a parameter to adjust the amount of pruning. More...

void	ProcessOptions ()
	the option string is decoded, for available options see "DeclareOptions" More...

void	SetMaxDepth (Int_t d)

void	SetMinNodeSize (Double_t sizeInPercent)

void	SetMinNodeSize (TString sizeInPercent)

void	SetNTrees (Int_t d)

void	SetAdaBoostBeta (Double_t b)

void	SetNodePurityLimit (Double_t l)

void	SetShrinkage (Double_t s)

void	SetUseNvars (Int_t n)

void	SetBaggedSampleFraction (Double_t f)

const std::vector< TMVA::DecisionTree * > &	GetForest () const

const std::vector< const TMVA::Event * > &	GetTrainingEvents () const

const std::vector< double > &	GetBoostWeights () const

std::vector< Double_t >	GetVariableImportance ()
	Return the relative variable importance, normalized to all variables together having the importance 1. More...

Double_t	GetVariableImportance (UInt_t ivar)
	Returns the measure for the variable importance of variable "ivar" which is later used in GetVariableImportance() to calculate the relative variable importances. More...

Double_t	TestTreeQuality (DecisionTree *dt)
	test the tree quality.. in terms of Miscalssification More...

void	MakeClassSpecific (std::ostream &, const TString &) const
	make ROOT-independent C++ class for classifier response (classifier-specific implementation) More...

void	MakeClassSpecificHeader (std::ostream &, const TString &) const
	specific class header More...

void	MakeClassInstantiateNode (DecisionTreeNode *n, std::ostream &fout, const TString &className) const
	recursively descends a tree and writes the node instance to the output streem More...

void	GetHelpMessage () const
	Get help message text. More...

Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="", TDirectory *theBaseDir=0)
	standard constructur More...

	MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile, TDirectory *theBaseDir=0)
	constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles More...

virtual	~MethodBase ()
	destructor More...

void	SetupMethod ()
	setup of methods More...

void	ProcessSetup ()
	process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More...

virtual void	CheckSetup ()
	check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More...

void	AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)

void	TrainMethod ()

void	SetTrainTime (Double_t trainTime)

Double_t	GetTrainTime () const

void	SetTestTime (Double_t testTime)

Double_t	GetTestTime () const

virtual void	TestClassification ()
	initialization More...

virtual Double_t	GetKSTrainingVsTest (Char_t SorB, TString opt="X")

virtual void	TestMulticlass ()
	test multiclass classification More...

virtual void	TestRegression (Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
	calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample More...

Double_t	GetMvaValue (const TMVA::Event const ev, Double_t err=0, Double_t *errUpper=0)

const std::vector< Float_t > &	GetRegressionValues (const TMVA::Event *const ev)

virtual Double_t	GetProba (const Event *ev)

virtual Double_t	GetProba (Double_t mvaVal, Double_t ap_sig)
	compute likelihood ratio More...

virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity: R(x) = Integrate_[-oo..x] { PDF(x') dx' } where PDF(x) is the PDF of the classifier's signal or background distribution More...

virtual void	MakeClass (const TString &classFileName=TString("")) const
	create reader class for method (classification only at present) More...

void	PrintHelpMessage () const
	prints out method-specific help method More...

void	WriteStateToFile () const
	write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created More...

void	ReadStateFromFile ()
	Function to write options and weights to file. More...

void	ReadStateFromStream (std::istream &tf)
	read the header from the weight files of the different MVA methods More...

void	ReadStateFromStream (TFile &rf)
	write reference MVA distributions (and other information) to a ROOT type weight file More...

void	ReadStateFromXMLString (const char *xmlstr)
	for reading from memory More...

virtual void	WriteEvaluationHistosToFile (Types::ETreeType treetype)
	writes all MVA evaluation histograms to file More...

virtual Double_t	GetEfficiency (const TString &, Types::ETreeType, Double_t &err)
	fill background efficiency (resp. More...

virtual Double_t	GetTrainingEfficiency (const TString &)

virtual std::vector< Float_t >	GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual std::vector< Float_t >	GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity)

virtual Double_t	GetSignificance () const
	compute significance of mean difference significance = \|<S> - \|/Sqrt(RMS_S2 + RMS_B2) More...

virtual Double_t	GetROCIntegral (TH1D histS, TH1D histB) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification More...

virtual Double_t	GetROCIntegral (PDF pdfS=0, PDF pdfB=0) const
	calculate the area (integral) under the ROC curve as a overall quality measure of the classification More...

virtual Double_t	GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
	plot significance, S/Sqrt(S^2 + B^2), curve for given number of signal and background events; returns cut for maximum significance also returned via reference is the maximum significance More...

virtual Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...

virtual Double_t	GetSeparation (PDF pdfS=0, PDF pdfB=0) const
	compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...

virtual void	GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const

const TString &	GetJobName () const

const TString &	GetMethodName () const

TString	GetMethodTypeName () const

Types::EMVA	GetMethodType () const

const char *	GetName () const
	Returns name of object. More...

const TString &	GetTestvarName () const

const TString	GetProbaName () const

TString	GetWeightFileName () const
	retrieve weight file name More...

void	SetTestvarName (const TString &v="")

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

UInt_t	GetNTargets () const

const TString &	GetInputVar (Int_t i) const

const TString &	GetInputLabel (Int_t i) const

const TString &	GetInputTitle (Int_t i) const

Double_t	GetMean (Int_t ivar) const

Double_t	GetRMS (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Double_t	GetXmax (Int_t ivar) const

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

void	SetSignalReferenceCut (Double_t cut)

void	SetSignalReferenceCutOrientation (Double_t cutOrientation)

TDirectory *	BaseDir () const
	returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored More...

TDirectory *	MethodBaseDir () const
	returns the ROOT directory where all instances of the corresponding MVA method are stored More...

void	SetMethodDir (TDirectory *methodDir)

void	SetBaseDir (TDirectory *methodDir)

void	SetMethodBaseDir (TDirectory *methodDir)

UInt_t	GetTrainingTMVAVersionCode () const

UInt_t	GetTrainingROOTVersionCode () const

TString	GetTrainingTMVAVersionString () const
	calculates the TMVA version string from the training version code on the fly More...

TString	GetTrainingROOTVersionString () const
	calculates the ROOT version string from the training version code on the fly More...

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)

DataSet *	Data () const

DataSetInfo &	DataInfo () const

UInt_t	GetNEvents () const
	temporary event when testing on a different DataSet than the own one More...

const Event *	GetEvent () const

const Event *	GetEvent (const TMVA::Event *ev) const

const Event *	GetEvent (Long64_t ievt) const

const Event *	GetEvent (Long64_t ievt, Types::ETreeType type) const

const Event *	GetTrainingEvent (Long64_t ievt) const

const Event *	GetTestingEvent (Long64_t ievt) const

const std::vector< TMVA::Event * > &	GetEventCollection (Types::ETreeType type)
	returns the event collection (i.e. More...

virtual Bool_t	IsSignalLike ()
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event would be selected as signal or background More...

virtual Bool_t	IsSignalLike (Double_t mvaVal)
	uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would tbe selected as signal or background More...

Bool_t	HasMVAPdfs () const

virtual void	SetAnalysisType (Types::EAnalysisType type)

Types::EAnalysisType	GetAnalysisType () const

Bool_t	DoRegression () const

Bool_t	DoMulticlass () const

void	DisableWriting (Bool_t setter)

Public Member Functions inherited from TMVA::IMethod
	IMethod ()

virtual	~IMethod ()

Public Member Functions inherited from TMVA::Configurable
	Configurable (const TString &theOption="")

virtual	~Configurable ()
	default destructur More...

virtual void	ParseOptions ()
	options parser More...

void	PrintOptions () const
	prints out the options set in the options string and the defaults More...

const char *	GetConfigName () const

const char *	GetConfigDescription () const

void	SetConfigName (const char *n)

void	SetConfigDescription (const char *d)

template<class T >
OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc="")

template<class T >
OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")

template<class T >
void	AddPreDefVal (const T &)

template<class T >
void	AddPreDefVal (const TString &optname, const T &)

void	CheckForUnusedOptions () const
	checks for unused options in option string More...

const TString &	GetOptions () const

void	SetOptions (const TString &s)

void	WriteOptionsToStream (std::ostream &o, const TString &prefix) const
	write options to output stream (e.g. in writing the MVA weight files More...

void	ReadOptionsFromStream (std::istream &istr)
	read option back from the weight file More...

void	AddOptionsXMLTo (void *parent) const
	write options to XML file More...

void	ReadOptionsFromXML (void *node)

void	SetMsgType (EMsgType t)

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T &ref, const TString &name, const TString &desc)

template<class T >
TMVA::OptionBase *	DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)

Public Member Functions inherited from TObject
	TObject ()

	TObject (const TObject &object)
	TObject copy ctor. More...

TObject &	operator= (const TObject &rhs)
	TObject assignment operator. More...

virtual	~TObject ()
	TObject destructor. More...

virtual void	AppendPad (Option_t *option="")
	Append graphics object to current pad. More...

virtual void	Browse (TBrowser *b)
	Browse object. May be overridden for another default action. More...

virtual const char *	ClassName () const
	Returns name of class to which the object belongs. More...

virtual void	Clear (Option_t *="")

virtual TObject *	Clone (const char *newname="") const
	Make a clone of an object using the Streamer facility. More...

virtual Int_t	Compare (const TObject *obj) const
	Compare abstract method. More...

virtual void	Copy (TObject &object) const
	Copy this to obj. More...

virtual void	Delete (Option_t *option="")
	Delete this object. More...

virtual Int_t	DistancetoPrimitive (Int_t px, Int_t py)
	Computes distance from point (px,py) to the object. More...

virtual void	Draw (Option_t *option="")
	Default Draw method for all objects. More...

virtual void	DrawClass () const
	Draw class inheritance tree of the class to which this object belongs. More...

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current pad. More...

virtual void	Dump () const
	Dump contents of object on stdout. More...

virtual void	Execute (const char method, const char params, Int_t *error=0)
	Execute method on this object with the given parameter string, e.g. More...

virtual void	Execute (TMethod method, TObjArray params, Int_t *error=0)
	Execute method on this object with parameters stored in the TObjArray. More...

virtual void	ExecuteEvent (Int_t event, Int_t px, Int_t py)
	Execute action corresponding to an event at (px,py). More...

virtual TObject *	FindObject (const char *name) const
	Must be redefined in derived classes. More...

virtual TObject *	FindObject (const TObject *obj) const
	Must be redefined in derived classes. More...

virtual Option_t *	GetDrawOption () const
	Get option used by the graphics system to draw this object. More...

virtual UInt_t	GetUniqueID () const
	Return the unique object id. More...

virtual const char *	GetIconName () const
	Returns mime type name of object. More...

virtual Option_t *	GetOption () const

virtual char *	GetObjectInfo (Int_t px, Int_t py) const
	Returns string containing info about the object at position (px,py). More...

virtual const char *	GetTitle () const
	Returns title of object. More...

virtual Bool_t	HandleTimer (TTimer *timer)
	Execute action in response of a timer timing out. More...

virtual ULong_t	Hash () const
	Return hash value for this object. More...

virtual Bool_t	InheritsFrom (const char *classname) const
	Returns kTRUE if object inherits from class "classname". More...

virtual Bool_t	InheritsFrom (const TClass *cl) const
	Returns kTRUE if object inherits from TClass cl. More...

virtual void	Inspect () const
	Dump contents of this object in a graphics canvas. More...

virtual Bool_t	IsFolder () const
	Returns kTRUE in case object contains browsable objects (like containers or lists of other objects). More...

virtual Bool_t	IsEqual (const TObject *obj) const
	Default equal comparison (objects are equal if they have the same address in memory). More...

virtual Bool_t	IsSortable () const

Bool_t	IsOnHeap () const

Bool_t	IsZombie () const

virtual Bool_t	Notify ()
	This method must be overridden to handle object notification. More...

virtual void	ls (Option_t *option="") const
	The ls function lists the contents of a class on stdout. More...

virtual void	Paint (Option_t *option="")
	This method must be overridden if a class wants to paint itself. More...

virtual void	Pop ()
	Pop on object drawn in a pad to the top of the display list. More...

virtual void	Print (Option_t *option="") const
	This method must be overridden when a class wants to print itself. More...

virtual Int_t	Read (const char *name)
	Read contents of object with specified name from the current directory. More...

virtual void	RecursiveRemove (TObject *obj)
	Recursively remove this object from a list. More...

virtual void	SaveAs (const char filename="", Option_t option="") const
	Save this object in the file specified by filename. More...

virtual void	SavePrimitive (std::ostream &out, Option_t *option="")
	Save a primitive as a C++ statement(s) on output stream "out". More...

virtual void	SetDrawOption (Option_t *option="")
	Set drawing option for object. More...

virtual void	SetUniqueID (UInt_t uid)
	Set the unique object id. More...

virtual void	UseCurrentStyle ()
	Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked. More...

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
	Write this object to the current directory. More...

virtual Int_t	Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
	Write this object to the current directory. More...

void *	operator new (size_t sz)

void *	operator new[] (size_t sz)

void *	operator new (size_t sz, void *vp)

void *	operator new[] (size_t sz, void *vp)

void	operator delete (void *ptr)
	Operator delete. More...

void	operator delete[] (void *ptr)
	Operator delete []. More...

void	SetBit (UInt_t f, Bool_t set)
	Set or unset the user status bits as specified in f. More...

void	SetBit (UInt_t f)

void	ResetBit (UInt_t f)

Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

void	InvertBit (UInt_t f)

virtual void	Info (const char method, const char msgfmt,...) const
	Issue info message. More...

virtual void	Warning (const char method, const char msgfmt,...) const
	Issue warning message. More...

virtual void	Error (const char method, const char msgfmt,...) const
	Issue error message. More...

virtual void	SysError (const char method, const char msgfmt,...) const
	Issue system error message. More...

virtual void	Fatal (const char method, const char msgfmt,...) const
	Issue fatal error message. More...

void	AbstractMethod (const char *method) const
	Use this method to implement an "abstract" method that you don't want to leave purely abstract. More...

void	MayNotUse (const char *method) const
	Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary). More...

void	Obsolete (const char method, const char asOfVers, const char *removedFromVers) const
	Use this method to declare a method obsolete. More...

Protected Member Functions
void	DeclareCompatibilityOptions ()
	options that are used ONLY for the READER to ensure backward compatibility More...

Protected Member Functions inherited from TMVA::MethodBase
void	NoErrorCalc (Double_t const err, Double_t const errUpper)

virtual void	ReadWeightsFromStream (TFile &)

void	SetWeightFileName (TString)
	set the weight file name (depreciated) More...

const TString &	GetWeightFileDir () const

void	SetWeightFileDir (TString fileDir)
	set directory of weight file More...

Bool_t	IsNormalised () const

void	SetNormalised (Bool_t norm)

Bool_t	Verbose () const

Bool_t	Help () const

const TString &	GetInternalVarName (Int_t ivar) const

const TString &	GetOriginalVarName (Int_t ivar) const

Bool_t	HasTrainingTree () const

void	Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
	calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE More...

Bool_t	TxtWeightsOnly () const

Bool_t	IsConstructedFromWeightFile () const

Bool_t	IgnoreEventsWithNegWeightsInTraining () const

Protected Member Functions inherited from TMVA::Configurable
Bool_t	LooseOptionCheckingEnabled () const

void	EnableLooseOptions (Bool_t b=kTRUE)

void	WriteOptionsReferenceToFile ()
	write complete options to output stream More...

void	ResetSetFlag ()
	resets the IsSet falg for all declare options to be called before options are read from stream More...

const TString &	GetReferenceFile () const

MsgLogger &	Log () const

Protected Member Functions inherited from TObject
void	MakeZombie ()

virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected). More...

Private Member Functions
Double_t	GetMvaValue (Double_t err, Double_t errUpper, UInt_t useNTrees)
	Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees. More...

Double_t	PrivateGetMvaValue (const TMVA::Event ev, Double_t err=0, Double_t *errUpper=0, UInt_t useNTrees=0)
	Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees. More...

void	BoostMonitor (Int_t iTree)
	fills the ROCIntegral vs Itree from the testSample for the monitoring plots during the training . More...

void	Init (void)
	common initialisation with defaults for the BDT-Method More...

void	PreProcessNegativeEventWeights ()
	o.k. More...

Double_t	AdaBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	the AdaBoost implementation. More...

Double_t	AdaCost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	the AdaCost boosting algorithm takes a simple cost Matrix (currently fixed for all events... More...

Double_t	Bagging ()
	call it boot-strapping, re-sampling or whatever you like, in the end it is nothing else but applying "random" poisson weights to each event. More...

Double_t	RegBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	a special boosting only for Regression ... More...

Double_t	AdaBoostR2 (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	adaption of the AdaBoost to regression problems (see H.Drucker 1997) More...

Double_t	GradBoost (std::vector< const TMVA::Event * > &, DecisionTree *dt, UInt_t cls=0)
	Calculate the desired response value for each region. More...

Double_t	GradBoostRegression (std::vector< const TMVA::Event * > &, DecisionTree *dt)
	Implementation of M_TreeBoost using a Huber loss function as desribed by Friedman 1999. More...

void	InitGradBoost (std::vector< const TMVA::Event * > &)
	initialize targets for first tree More...

void	UpdateTargets (std::vector< const TMVA::Event * > &, UInt_t cls=0)
	Calculate residua for all events;. More...

void	UpdateTargetsRegression (std::vector< const TMVA::Event * > &, Bool_t first=kFALSE)
	Calculate current residuals for all events and update targets for next iteration. More...

Double_t	GetGradBoostMVA (const TMVA::Event *e, UInt_t nTrees)
	returns MVA value: -1 for background, 1 for signal More...

void	GetBaggedSubSample (std::vector< const TMVA::Event * > &)
	fills fEventSample with fBaggedSampleFraction*NEvents random training events More...

Double_t	GetWeightedQuantile (std::vector< std::pair< Double_t, Double_t > > vec, const Double_t quantile, const Double_t SumOfWeights=0.0)
	calculates the quantile of the distribution of the first pair entries weighted with the values in the second pair entries More...

void	DeterminePreselectionCuts (const std::vector< const TMVA::Event * > &eventSample)
	find useful preselection cuts that will be applied before and Decision Tree training. More...

Double_t	ApplyPreselectionCuts (const Event *ev)
	aply the preselection cuts before even bothing about any Decision Trees in the GetMVA . More...

Private Attributes
std::vector< const TMVA::Event * >	fEventSample

std::vector< const TMVA::Event * >	fValidationSample

std::vector< const TMVA::Event * >	fSubSample

std::vector< const TMVA::Event * > *	fTrainSample

Int_t	fNTrees

std::vector< DecisionTree * >	fForest

std::vector< double >	fBoostWeights

Double_t	fSigToBkgFraction

TString	fBoostType

Double_t	fAdaBoostBeta

TString	fAdaBoostR2Loss

Double_t	fTransitionPoint

Double_t	fShrinkage

Bool_t	fBaggedBoost

Bool_t	fBaggedGradBoost

Double_t	fSumOfWeights

std::map< const TMVA::Event *, std::pair< Double_t, Double_t > >	fWeightedResiduals

std::map< const TMVA::Event *, std::vector< double > >	fResiduals

SeparationBase *	fSepType

TString	fSepTypeS

Int_t	fMinNodeEvents

Float_t	fMinNodeSize

TString	fMinNodeSizeS

Int_t	fNCuts

Bool_t	fUseFisherCuts

Double_t	fMinLinCorrForFisher

Bool_t	fUseExclusiveVars

Bool_t	fUseYesNoLeaf

Double_t	fNodePurityLimit

UInt_t	fNNodesMax

UInt_t	fMaxDepth

DecisionTree::EPruneMethod	fPruneMethod

TString	fPruneMethodS

Double_t	fPruneStrength

Double_t	fFValidationEvents

Bool_t	fAutomatic

Bool_t	fRandomisedTrees

UInt_t	fUseNvars

Bool_t	fUsePoissonNvars

UInt_t	fUseNTrainEvents

Double_t	fBaggedSampleFraction

TString	fNegWeightTreatment

Bool_t	fNoNegWeightsInTraining

Bool_t	fInverseBoostNegWeights

Bool_t	fPairNegWeightsGlobal

Bool_t	fTrainWithNegWeights

Bool_t	fDoBoostMonitor

TTree *	fMonitorNtuple

Int_t	fITree

Double_t	fBoostWeight

Double_t	fErrorFraction

Double_t	fCss

Double_t	fCts_sb

Double_t	fCtb_ss

Double_t	fCbb

Bool_t	fDoPreselection

std::vector< Double_t >	fVariableImportance

std::vector< Double_t >	fLowSigCut

std::vector< Double_t >	fLowBkgCut

std::vector< Double_t >	fHighSigCut

std::vector< Double_t >	fHighBkgCut

std::vector< Bool_t >	fIsLowSigCut

std::vector< Bool_t >	fIsLowBkgCut

std::vector< Bool_t >	fIsHighSigCut

std::vector< Bool_t >	fIsHighBkgCut

Bool_t	fHistoricBool

Static Private Attributes
static const Int_t	fgDebugLevel

Additional Inherited Members
Public Types inherited from TMVA::MethodBase
enum	EWeightFileType { kROOT =0, kTEXT }

Public Types inherited from TObject
enum	EStatusBits { kCanDelete = BIT(0), kMustCleanup = BIT(3), kObjInCanvas = BIT(3), kIsReferenced = BIT(4), kHasUUID = BIT(5), kCannotPick = BIT(6), kNoContextMenu = BIT(8), kInvalidObject = BIT(13) }

enum	{ kIsOnHeap = 0x01000000, kNotDeleted = 0x02000000, kZombie = 0x04000000, kBitMask = 0x00ffffff }

enum	{ kSingleKey = BIT(0), kOverwrite = BIT(1), kWriteDelete = BIT(2) }

Static Public Member Functions inherited from TObject
static Long_t	GetDtorOnly ()
	Return destructor only flag. More...

static void	SetDtorOnly (void *obj)
	Set destructor only flag. More...

static Bool_t	GetObjectStat ()
	Get status of object stat flag. More...

static void	SetObjectStat (Bool_t stat)
	Turn on/off tracking of objects in the TObjectTable. More...

Public Attributes inherited from TMVA::MethodBase
const Event *	fTmpEvent

Bool_t	fSetupCompleted

Static Protected Member Functions inherited from TMVA::MethodBase
static MethodBase *	GetThisBase ()
	return a pointer the base class of this method More...

Protected Attributes inherited from TMVA::MethodBase
Ranking *	fRanking

std::vector< TString > *	fInputVars

Int_t	fNbins

Int_t	fNbinsMVAoutput

Int_t	fNbinsH

Types::EAnalysisType	fAnalysisType

std::vector< Float_t > *	fRegressionReturnVal

std::vector< Float_t > *	fMulticlassReturnVal

UInt_t	fSignalClass

UInt_t	fBackgroundClass

#include <TMVA/MethodBDT.h>

Inheritance diagram for TMVA::MethodBDT:

Collaboration diagram for TMVA::MethodBDT:

Constructor & Destructor Documentation

TMVA::MethodBDT::MethodBDT	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption = `""`,
		TDirectory *	theTargetDir = `0`
	)

TMVA::MethodBDT::MethodBDT	(	DataSetInfo &	theData,
		const TString &	theWeightFile,
		TDirectory *	theTargetDir = `NULL`
	)

Definition at line 207 of file MethodBDT.cxx.

TMVA::MethodBDT::~MethodBDT ( void )

virtual

destructor Note: fEventSample and ValidationSample are already deleted at the end of TRAIN When they are not used anymore for (UInt_t i=0; i<fEventSample.size(); i++) delete fEventSample[i]; for (UInt_t i=0; i<fValidationSample.size(); i++) delete fValidationSample[i];

Definition at line 708 of file MethodBDT.cxx.

Member Function Documentation

Double_t TMVA::MethodBDT::AdaBoost	(	std::vector< const TMVA::Event * > &	eventSample,
		DecisionTree *	dt
	)

private

the AdaBoost implementation.

a new training sample is generated by weighting events that are misclassified by the decision tree. The weight applied is w = (1-err)/err or more general: w = ((1-err)/err)^beta where err is the fraction of misclassified events in the tree ( <0.5 assuming demanding the that previous selection was better than random guessing) and "beta" being a free parameter (standard: beta = 1) that modifies the boosting.

Definition at line 1703 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::AdaBoostR2	(	std::vector< const TMVA::Event * > &	eventSample,
		DecisionTree *	dt
	)

private

adaption of the AdaBoost to regression problems (see H.Drucker 1997)

Definition at line 2056 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::AdaCost	(	std::vector< const TMVA::Event * > &	eventSample,
		DecisionTree *	dt
	)

private

the AdaCost boosting algorithm takes a simple cost Matrix (currently fixed for all events...

later could be modified to use individual cost matrices for each events as in the original paper...

              true_signal true_bkg 
----------------------------------
sel_signal |   Css         Ctb_ss    Cxx.. in the range [0,1]
sel_bkg    |   Cts_sb      Cbb

and takes this into account when calculating the misclass. cost (former: error fraction):

err = sum_events ( weight* y_true*y_sel * beta(event)

Definition at line 1884 of file MethodBDT.cxx.

void TMVA::MethodBDT::AddWeightsXMLTo ( void * parent ) const

virtual

write weights to XML

Implements TMVA::MethodBase.

Definition at line 2173 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::ApplyPreselectionCuts ( const Event * ev )

private

aply the preselection cuts before even bothing about any Decision Trees in the GetMVA .

. –> -1 for background +1 for Signal

Definition at line 2943 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::Bagging ( )

private

call it boot-strapping, re-sampling or whatever you like, in the end it is nothing else but applying "random" poisson weights to each event.

Definition at line 2002 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::Boost	(	std::vector< const TMVA::Event * > &	eventSample,
		DecisionTree *	dt,
		UInt_t	cls = `0`
	)

apply the boosting alogrithim (the algorithm is selecte via the the "option" given in the constructor.

The return value is the boosting weight

Definition at line 1575 of file MethodBDT.cxx.

void TMVA::MethodBDT::BoostMonitor ( Int_t iTree )

private

fills the ROCIntegral vs Itree from the testSample for the monitoring plots during the training .

. but using the testing events

Definition at line 1609 of file MethodBDT.cxx.

const TMVA::Ranking * TMVA::MethodBDT::CreateRanking ( )

virtual

Compute ranking of input variables.

Implements TMVA::MethodBase.

Definition at line 2533 of file MethodBDT.cxx.

void TMVA::MethodBDT::DeclareCompatibilityOptions ( )

protectedvirtual

options that are used ONLY for the READER to ensure backward compatibility

Reimplemented from TMVA::MethodBase.

Definition at line 423 of file MethodBDT.cxx.

void TMVA::MethodBDT::DeclareOptions ( )

virtual

define the options (their key words) that can be set in the option string know options: nTrees number of trees in the forest to be created BoostType the boosting type for the trees in the forest (AdaBoost e.t.c..) known: AdaBoost AdaBoostR2 (Adaboost for regression) Bagging GradBoost AdaBoostBeta the boosting parameter, beta, for AdaBoost UseRandomisedTrees choose at each node splitting a random set of variables UseNvars use UseNvars variables in randomised trees UsePoission Nvars use UseNvars not as fixed number but as mean of a possion distribution SeparationType the separation criterion applied in the node splitting known: GiniIndex MisClassificationError CrossEntropy SDivSqrtSPlusB MinNodeSize: minimum percentage of training events in a leaf node (leaf criteria, stop splitting) nCuts: the number of steps in the optimisation of the cut for a node (if < 0, then step size is determined by the events) UseFisherCuts: use multivariate splits using the Fisher criterion UseYesNoLeaf decide if the classification is done simply by the node type, or the S/B (from the training) in the leaf node NodePurityLimit the minimum purity to classify a node as a signal node (used in pruning and boosting to determine misclassification error rate) PruneMethod The Pruning method: known: NoPruning // switch off pruning completely ExpectedError CostComplexity PruneStrength a parameter to adjust the amount of pruning.

Should be large enough such that overtraining is avoided. PruningValFraction number of events to use for optimizing pruning (only if PruneStrength < 0, i.e. automatic pruning) NegWeightTreatment IgnoreNegWeightsInTraining Ignore negative weight events in the training. DecreaseBoostWeight Boost ev. with neg. weight with 1/boostweight instead of boostweight PairNegWeightsGlobal Pair ev. with neg. and pos. weights in traning sample and "annihilate" them MaxDepth maximum depth of the decision tree allowed before further splitting is stopped

Implements TMVA::MethodBase.

Definition at line 311 of file MethodBDT.cxx.

void TMVA::MethodBDT::DeterminePreselectionCuts ( const std::vector< const TMVA::Event * > & eventSample )

private

find useful preselection cuts that will be applied before and Decision Tree training.

. (and of course also applied in the GetMVA .. –> -1 for background +1 for Signal /*

Definition at line 2843 of file MethodBDT.cxx.

void TMVA::MethodBDT::GetBaggedSubSample ( std::vector< const TMVA::Event * > & eventSample )

private

fills fEventSample with fBaggedSampleFraction*NEvents random training events

Definition at line 2013 of file MethodBDT.cxx.

const std::vector< double > & TMVA::MethodBDT::GetBoostWeights ( ) const

inline

Definition at line 306 of file MethodBDT.h.

const std::vector< TMVA::DecisionTree * > & TMVA::MethodBDT::GetForest ( ) const

inline

Definition at line 304 of file MethodBDT.h.

Double_t TMVA::MethodBDT::GetGradBoostMVA	(	const TMVA::Event *	e,
		UInt_t	nTrees
	)

private

returns MVA value: -1 for background, 1 for signal

Definition at line 1352 of file MethodBDT.cxx.

void TMVA::MethodBDT::GetHelpMessage ( ) const

virtual

Get help message text.

typical length of text line: "|--------------------------------------------------------------|"

Implements TMVA::IMethod.

Definition at line 2553 of file MethodBDT.cxx.

const std::vector< Float_t > & TMVA::MethodBDT::GetMulticlassValues ( )

virtual

get the multiclass MVA response for the BDT classifier

Reimplemented from TMVA::MethodBase.

Definition at line 2357 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::GetMvaValue	(	Double_t *	err = `0`,
		Double_t *	errUpper = `0`
	)

virtual

Implements TMVA::MethodBase.

Definition at line 2306 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::GetMvaValue	(	Double_t *	err,
		Double_t *	errUpper,
		UInt_t	useNTrees
	)

private

Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees.

Definition at line 2315 of file MethodBDT.cxx.

UInt_t TMVA::MethodBDT::GetNTrees ( ) const

inline

Definition at line 113 of file MethodBDT.h.

const std::vector< Float_t > & TMVA::MethodBDT::GetRegressionValues ( )

virtual

get the regression value generated by the BDTs

Reimplemented from TMVA::MethodBase.

Definition at line 2392 of file MethodBDT.cxx.

const std::vector< const TMVA::Event * > & TMVA::MethodBDT::GetTrainingEvents ( ) const

inline

Definition at line 305 of file MethodBDT.h.

vector< Double_t > TMVA::MethodBDT::GetVariableImportance ( )

Return the relative variable importance, normalized to all variables together having the importance 1.

The importance in evaluated as the total separation-gain that this variable had in the decision trees (weighted by the number of events)

Definition at line 2493 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::GetVariableImportance ( UInt_t ivar )

Returns the measure for the variable importance of variable "ivar" which is later used in GetVariableImportance() to calculate the relative variable importances.

Definition at line 2521 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::GetWeightedQuantile	(	std::vector< std::pair< Double_t, Double_t > >	vec,
		const Double_t	quantile,
		const Double_t	SumOfWeights = `0.0`
	)

private

calculates the quantile of the distribution of the first pair entries weighted with the values in the second pair entries

Definition at line 1430 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::GradBoost	(	std::vector< const TMVA::Event * > &	eventSample,
		DecisionTree *	dt,
		UInt_t	cls = `0`
	)

private

Calculate the desired response value for each region.

Definition at line 1445 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::GradBoostRegression	(	std::vector< const TMVA::Event * > &	eventSample,
		DecisionTree *	dt
	)

private

Implementation of M_TreeBoost using a Huber loss function as desribed by Friedman 1999.

Definition at line 1476 of file MethodBDT.cxx.

Bool_t TMVA::MethodBDT::HasAnalysisType	(	Types::EAnalysisType	type,
		UInt_t	numberClasses,
		UInt_t	numberTargets
	)

virtual

BDT can handle classification with multiple classes and regression with one regression-target.

Implements TMVA::IMethod.

Definition at line 266 of file MethodBDT.cxx.

void TMVA::MethodBDT::Init ( void )

privatevirtual

common initialisation with defaults for the BDT-Method

Implements TMVA::MethodBase.

Definition at line 642 of file MethodBDT.cxx.

void TMVA::MethodBDT::InitEventSample ( void )

initialize the event sample (i.e. reset the boost-weights... etc)

Definition at line 716 of file MethodBDT.cxx.

void TMVA::MethodBDT::InitGradBoost ( std::vector< const TMVA::Event * > & eventSample )

private

initialize targets for first tree

Definition at line 1506 of file MethodBDT.cxx.

void TMVA::MethodBDT::MakeClassInstantiateNode	(	DecisionTreeNode *	n,
		std::ostream &	fout,
		const TString &	className
	)		const

recursively descends a tree and writes the node instance to the output streem

Definition at line 2797 of file MethodBDT.cxx.

void TMVA::MethodBDT::MakeClassSpecific	(	std::ostream &	fout,
		const TString &	className
	)		const

virtual

make ROOT-independent C++ class for classifier response (classifier-specific implementation)

Reimplemented from TMVA::MethodBase.

Definition at line 2610 of file MethodBDT.cxx.

void TMVA::MethodBDT::MakeClassSpecificHeader	(	std::ostream &	fout,
		const TString &	className
	)		const

virtual

specific class header

Reimplemented from TMVA::MethodBase.

Definition at line 2686 of file MethodBDT.cxx.

std::map< TString, Double_t > TMVA::MethodBDT::OptimizeTuningParameters	(	TString	fomType = `"ROCIntegral"`,
		TString	fitType = `"FitGA"`
	)

virtual

call the Optimzier with the set of paremeters and ranges that are meant to be tuned.

Reimplemented from TMVA::MethodBase.

Definition at line 1020 of file MethodBDT.cxx.

void TMVA::MethodBDT::PreProcessNegativeEventWeights ( )

private

o.k.

you know there are events with negative event weights. This routine will remove them by pairing them with the closest event(s) of the same event class with positive weights A first attempt is "brute force", I dont' try to be clever using search trees etc, just quick and dirty to see if the result is any good

Definition at line 882 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::PrivateGetMvaValue	(	const TMVA::Event *	ev,
		Double_t *	err = `0`,
		Double_t *	errUpper = `0`,
		UInt_t	useNTrees = `0`
	)

private

Return the MVA value (range [-1;1]) that classifies the event according to the majority vote from the total number of decision trees.

Definition at line 2330 of file MethodBDT.cxx.

void TMVA::MethodBDT::ProcessOptions ( )

virtual

the option string is decoded, for available options see "DeclareOptions"

Implements TMVA::MethodBase.

Definition at line 442 of file MethodBDT.cxx.

void TMVA::MethodBDT::ReadWeightsFromStream ( std::istream & istr )

virtual

read the weights (BDT coefficients)

Implements TMVA::MethodBase.

Definition at line 2271 of file MethodBDT.cxx.

void TMVA::MethodBDT::ReadWeightsFromXML ( void * parent )

virtual

reads the BDT from the xml file

Implements TMVA::MethodBase.

Definition at line 2204 of file MethodBDT.cxx.

Double_t TMVA::MethodBDT::RegBoost	(	std::vector< const TMVA::Event * > &	,
		DecisionTree *	dt
	)

private

a special boosting only for Regression ...

maybe I'll implement it later...

Definition at line 2048 of file MethodBDT.cxx.

void TMVA::MethodBDT::Reset ( void )

virtual

reset the method, as if it had just been instantiated (forget all training etc.)

Reimplemented from TMVA::MethodBase.

Definition at line 680 of file MethodBDT.cxx.

void TMVA::MethodBDT::SetAdaBoostBeta ( Double_t b )

inline

Definition at line 139 of file MethodBDT.h.

void TMVA::MethodBDT::SetBaggedSampleFraction ( Double_t f )

inline

Definition at line 143 of file MethodBDT.h.

void TMVA::MethodBDT::SetMaxDepth ( Int_t d )

inline

Definition at line 134 of file MethodBDT.h.

void TMVA::MethodBDT::SetMinNodeSize ( Double_t sizeInPercent )

Definition at line 614 of file MethodBDT.cxx.

void TMVA::MethodBDT::SetMinNodeSize ( TString sizeInPercent )

Definition at line 627 of file MethodBDT.cxx.

void TMVA::MethodBDT::SetNodePurityLimit ( Double_t l )

inline

Definition at line 140 of file MethodBDT.h.

void TMVA::MethodBDT::SetNTrees ( Int_t d )

inline

Definition at line 138 of file MethodBDT.h.

void TMVA::MethodBDT::SetShrinkage ( Double_t s )

inline

Definition at line 141 of file MethodBDT.h.

void TMVA::MethodBDT::SetTuneParameters ( std::map< TString, Double_t > tuneParameters )

virtual

set the tuning parameters accoding to the argument

Reimplemented from TMVA::MethodBase.

Definition at line 1071 of file MethodBDT.cxx.

void TMVA::MethodBDT::SetUseNvars ( Int_t n )

inline

Definition at line 142 of file MethodBDT.h.

Double_t TMVA::MethodBDT::TestTreeQuality ( DecisionTree * dt )

test the tree quality.. in terms of Miscalssification

Definition at line 1554 of file MethodBDT.cxx.

void TMVA::MethodBDT::Train ( void )

virtual

BDT training.

Implements TMVA::MethodBase.

Definition at line 1093 of file MethodBDT.cxx.

void TMVA::MethodBDT::UpdateTargets	(	std::vector< const TMVA::Event * > &	eventSample,
		UInt_t	cls = `0`
	)

private

Calculate residua for all events;.

Definition at line 1366 of file MethodBDT.cxx.

void TMVA::MethodBDT::UpdateTargetsRegression	(	std::vector< const TMVA::Event * > &	eventSample,
		Bool_t	first = `kFALSE`
	)

private

Calculate current residuals for all events and update targets for next iteration.

Definition at line 1399 of file MethodBDT.cxx.

void TMVA::MethodBDT::WriteMonitoringHistosToFile ( void ) const

virtual

Here we could write some histograms created during the processing to the output file.

Reimplemented from TMVA::MethodBase.

Definition at line 2478 of file MethodBDT.cxx.

Member Data Documentation

Double_t TMVA::MethodBDT::fAdaBoostBeta

private

Definition at line 217 of file MethodBDT.h.

Referenced by SetAdaBoostBeta().

TString TMVA::MethodBDT::fAdaBoostR2Loss

private

Definition at line 218 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fAutomatic

private

Definition at line 247 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fBaggedBoost

private

Definition at line 221 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fBaggedGradBoost

private

Definition at line 222 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fBaggedSampleFraction

private

Definition at line 253 of file MethodBDT.h.

Referenced by SetBaggedSampleFraction().

TString TMVA::MethodBDT::fBoostType

private

Definition at line 216 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fBoostWeight

private

Definition at line 265 of file MethodBDT.h.

std::vector<double> TMVA::MethodBDT::fBoostWeights

private

Definition at line 214 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCbb

private

Definition at line 271 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCss

private

Definition at line 268 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCtb_ss

private

Definition at line 270 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fCts_sb

private

Definition at line 269 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fDoBoostMonitor

private

Definition at line 259 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fDoPreselection

private

Definition at line 273 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fErrorFraction

private

Definition at line 266 of file MethodBDT.h.

std::vector<const TMVA::Event*> TMVA::MethodBDT::fEventSample

private

Definition at line 207 of file MethodBDT.h.

std::vector<DecisionTree*> TMVA::MethodBDT::fForest

private

Definition at line 213 of file MethodBDT.h.

Referenced by GetForest(), and GetNTrees().

Double_t TMVA::MethodBDT::fFValidationEvents

private

Definition at line 246 of file MethodBDT.h.

const Int_t TMVA::MethodBDT::fgDebugLevel

staticprivate

Definition at line 295 of file MethodBDT.h.

std::vector<Double_t> TMVA::MethodBDT::fHighBkgCut

private

Definition at line 284 of file MethodBDT.h.

std::vector<Double_t> TMVA::MethodBDT::fHighSigCut

private

Definition at line 283 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fHistoricBool

private

Definition at line 291 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fInverseBoostNegWeights

private

Definition at line 256 of file MethodBDT.h.

std::vector<Bool_t> TMVA::MethodBDT::fIsHighBkgCut

private

Definition at line 289 of file MethodBDT.h.

std::vector<Bool_t> TMVA::MethodBDT::fIsHighSigCut

private

Definition at line 288 of file MethodBDT.h.

std::vector<Bool_t> TMVA::MethodBDT::fIsLowBkgCut

private

Definition at line 287 of file MethodBDT.h.

std::vector<Bool_t> TMVA::MethodBDT::fIsLowSigCut

private

Definition at line 286 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fITree

private

Definition at line 264 of file MethodBDT.h.

std::vector<Double_t> TMVA::MethodBDT::fLowBkgCut

private

Definition at line 282 of file MethodBDT.h.

std::vector<Double_t> TMVA::MethodBDT::fLowSigCut

private

Definition at line 281 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fMaxDepth

private

Definition at line 241 of file MethodBDT.h.

Referenced by SetMaxDepth().

Double_t TMVA::MethodBDT::fMinLinCorrForFisher

private

Definition at line 236 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fMinNodeEvents

private

Definition at line 230 of file MethodBDT.h.

Float_t TMVA::MethodBDT::fMinNodeSize

private

Definition at line 231 of file MethodBDT.h.

TString TMVA::MethodBDT::fMinNodeSizeS

private

Definition at line 232 of file MethodBDT.h.

TTree* TMVA::MethodBDT::fMonitorNtuple

private

Definition at line 263 of file MethodBDT.h.

Referenced by MethodBDT().

Int_t TMVA::MethodBDT::fNCuts

private

Definition at line 234 of file MethodBDT.h.

TString TMVA::MethodBDT::fNegWeightTreatment

private

Definition at line 254 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fNNodesMax

private

Definition at line 240 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fNodePurityLimit

private

Definition at line 239 of file MethodBDT.h.

Referenced by SetNodePurityLimit().

Bool_t TMVA::MethodBDT::fNoNegWeightsInTraining

private

Definition at line 255 of file MethodBDT.h.

Int_t TMVA::MethodBDT::fNTrees

private

Definition at line 212 of file MethodBDT.h.

Referenced by SetNTrees().

Bool_t TMVA::MethodBDT::fPairNegWeightsGlobal

private

Definition at line 257 of file MethodBDT.h.

DecisionTree::EPruneMethod TMVA::MethodBDT::fPruneMethod

private

Definition at line 243 of file MethodBDT.h.

TString TMVA::MethodBDT::fPruneMethodS

private

Definition at line 244 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fPruneStrength

private

Definition at line 245 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fRandomisedTrees

private

Definition at line 248 of file MethodBDT.h.

std::map< const TMVA::Event*,std::vector<double> > TMVA::MethodBDT::fResiduals

private

Definition at line 225 of file MethodBDT.h.

SeparationBase* TMVA::MethodBDT::fSepType

private

Definition at line 228 of file MethodBDT.h.

Referenced by MethodBDT().

TString TMVA::MethodBDT::fSepTypeS

private

Definition at line 229 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fShrinkage

private

Definition at line 220 of file MethodBDT.h.

Referenced by SetShrinkage().

Double_t TMVA::MethodBDT::fSigToBkgFraction

private

Definition at line 215 of file MethodBDT.h.

std::vector<const TMVA::Event*> TMVA::MethodBDT::fSubSample

private

Definition at line 209 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fSumOfWeights

private

Definition at line 223 of file MethodBDT.h.

std::vector<const TMVA::Event*>* TMVA::MethodBDT::fTrainSample

private

Definition at line 210 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fTrainWithNegWeights

private

Definition at line 258 of file MethodBDT.h.

Double_t TMVA::MethodBDT::fTransitionPoint

private

Definition at line 219 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUseExclusiveVars

private

Definition at line 237 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUseFisherCuts

private

Definition at line 235 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fUseNTrainEvents

private

Definition at line 251 of file MethodBDT.h.

UInt_t TMVA::MethodBDT::fUseNvars

private

Definition at line 249 of file MethodBDT.h.

Referenced by SetUseNvars().

Bool_t TMVA::MethodBDT::fUsePoissonNvars

private

Definition at line 250 of file MethodBDT.h.

Bool_t TMVA::MethodBDT::fUseYesNoLeaf

private

Definition at line 238 of file MethodBDT.h.

std::vector<const TMVA::Event*> TMVA::MethodBDT::fValidationSample

private

Definition at line 208 of file MethodBDT.h.

std::vector<Double_t> TMVA::MethodBDT::fVariableImportance

private

Definition at line 275 of file MethodBDT.h.

std::map< const TMVA::Event*, std::pair<Double_t, Double_t> > TMVA::MethodBDT::fWeightedResiduals

private

Definition at line 224 of file MethodBDT.h.

The documentation for this class was generated from the following files:

tmva/tmva/inc/TMVA/MethodBDT.h
tmva/tmva/src/MethodBDT.cxx

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