Definition at line 117 of file DecisionTreeNode.h.

Public Member Functions
	DecisionTreeNode ()
	constructor of an essentially "empty" node floating in space

	DecisionTreeNode (const DecisionTreeNode &n, DecisionTreeNode *parent=nullptr)
	copy constructor of a node.

	DecisionTreeNode (Node *p, char pos)
	constructor of a daughter node as a daughter of 'p'

virtual	~DecisionTreeNode ()
	destructor

virtual void	AddAttributesToNode (void *node) const
	add attribute to xml

virtual void	AddContentToNode (std::stringstream &s) const
	adding attributes to tree node (well, was used in BinarySearchTree, and somehow I guess someone programmed it such that we need this in this tree too, although we don't..)

void	AddToSumTarget (Float_t t)
	add to sum target, if traininfo defined

void	AddToSumTarget2 (Float_t t2)
	add to sum target 2, if traininfo defined

void	ClearNodeAndAllDaughters ()
	clear the nodes (their S/N, Nevents etc), just keep the structure of the tree

virtual Node *	CreateNode () const

Double_t	GetAlpha () const
	return the critical point alpha, or -1 if traininfo undefined

Double_t	GetAlphaMinSubtree () const
	return the minimum alpha in the tree rooted at this node, or -1 if traininfo undefined

Double_t	GetCC () const
	return CC, or -1 if traininfo undefined

Bool_t	GetCutType (void) const
	return kTRUE: Cuts select signal, kFALSE: Cuts select bkg

Float_t	GetCutValue (void) const
	return the cut value applied at this node

Double_t	GetFisherCoeff (Int_t ivar) const
	get fisher coefficients

virtual DecisionTreeNode *	GetLeft () const

Float_t	GetNBkgEvents (void) const
	return the sum of the backgr weights in the node, or -1 if traininfo undefined

Float_t	GetNBkgEvents_unboosted (void) const
	return the sum of unboosted backgr weights in the node, or -1 if traininfo undefined

Float_t	GetNBkgEvents_unweighted (void) const
	return the sum of unweighted backgr weights in the node, or -1 if traininfo undefined

Double_t	GetNBValidation () const
	return number of background events from the pruning validation sample, or -1 if traininfo undefined

Float_t	GetNEvents (void) const
	return the number of events that entered the node (during training), or -1 if traininfo undefined

Float_t	GetNEvents_unboosted (void) const
	return the number of unboosted events that entered the node (during training), or -1 if traininfo undefined

Float_t	GetNEvents_unweighted (void) const
	return the number of unweighted events that entered the node (during training), or -1 if traininfo undefined

UInt_t	GetNFisherCoeff () const

Double_t	GetNodeR () const
	return the node resubstitution estimate, R(t), for Cost Complexity pruning, or -1 if traininfo undefined

Int_t	GetNodeType (void) const
	return node type: 1 signal node, -1 bkg leave, 0 intermediate Node

Float_t	GetNSigEvents (void) const
	return the sum of the signal weights in the node, or -1 if traininfo undefined

Float_t	GetNSigEvents_unboosted (void) const
	return the sum of unboosted signal weights in the node, or -1 if traininfo undefined

Float_t	GetNSigEvents_unweighted (void) const

Double_t	GetNSValidation () const
	return number of signal events from the pruning validation sample, or -1 if traininfo undefined

Int_t	GetNTerminal () const
	return number of terminal nodes in the subtree rooted here, or -1 if traininfo undefined

virtual DecisionTreeNode *	GetParent () const

Float_t	GetPurity (void) const
	return S/(S+B) (purity) at this node (from training)

Float_t	GetResponse (void) const
	return the response of the node (for regression)

virtual DecisionTreeNode *	GetRight () const

Float_t	GetRMS (void) const
	return the RMS of the response of the node (for regression)

Float_t	GetSampleMax (UInt_t ivar) const
	return the maximum of variable ivar from the training sample that pass/end up in this node, if traininfo defined, otherwise Log Fatal and return 9999

Float_t	GetSampleMin (UInt_t ivar) const
	return the minimum of variable ivar from the training sample that pass/end up in this node, if traininfo defined, otherwise Log Fatal and return -9999

Short_t	GetSelector () const
	return index of variable used for discrimination at this node

Float_t	GetSeparationGain (void) const
	return the gain in separation obtained by this node's selection, or -1 if traininfo undefined

Float_t	GetSeparationIndex (void) const
	return the separation index AT this node, or 0 if traininfo undefined

Double_t	GetSubTreeR () const
	return the resubstitution estimate, R(T_t), of the tree rooted at this node, or -1 if traininfo undefined

Float_t	GetSumTarget () const
	return sum target, or -9999 if traininfo undefined

Float_t	GetSumTarget2 () const
	return sum target 2, or -9999 if traininfo undefined

virtual Bool_t	GoesLeft (const Event &) const
	test event if it descends the tree at this node to the left

virtual Bool_t	GoesRight (const Event &) const
	test event if it descends the tree at this node to the right

void	IncrementNBkgEvents (Float_t b)
	increment the sum of the backgr weights in the node, if traininfo defined

void	IncrementNBkgEvents_unweighted ()
	increment the sum of the backgr weights in the node, if traininfo defined

void	IncrementNEvents (Float_t nev)

void	IncrementNEvents_unweighted ()
	increment the number of events that entered the node (during training), if traininfo defined

void	IncrementNSigEvents (Float_t s)
	increment the sum of the signal weights in the node, if traininfo defined

void	IncrementNSigEvents_unweighted ()
	increment the sum of the signal weights in the node, if traininfo defined

virtual TClass *	IsA () const

Bool_t	IsTerminal () const
	flag indicates whether this node is terminal

virtual void	Print (std::ostream &os) const
	print the node

void	PrintPrune (std::ostream &os) const
	printout of the node (can be read in with ReadDataRecord)

virtual void	PrintRec (std::ostream &os) const
	recursively print the node and its daughters (--> print the 'tree')

void	PrintRecPrune (std::ostream &os) const
	recursive printout of the node and its daughters

virtual void	ReadAttributes (void *node, UInt_t tmva_Version_Code=262657)

virtual void	ReadContent (std::stringstream &s)
	reading attributes from tree node (well, was used in BinarySearchTree, and somehow I guess someone programmed it such that we need this in this tree too, although we don't..)

virtual Bool_t	ReadDataRecord (std::istream &is, UInt_t tmva_Version_Code=262657)
	Read the data block.

void	ResetValidationData ()
	temporary stored node values (number of events, etc.) that originate not from the training but from the validation data (used in pruning)

void	SetAlpha (Double_t alpha)
	set the critical point alpha, if traininfo defined

void	SetAlphaMinSubtree (Double_t g)
	set the minimum alpha in the tree rooted at this node, if traininfo defined

void	SetCC (Double_t cc)
	Set CC, if traininfo defined, otherwise Log Fatal.

void	SetCutType (Bool_t t)
	set true: if event variable > cutValue ==> signal , false otherwise

void	SetCutValue (Float_t c)
	set the cut value applied at this node

void	SetFisherCoeff (Int_t ivar, Double_t coeff)
	set fisher coefficients

virtual void	SetLeft (Node *l)

void	SetNBkgEvents (Float_t b)
	set the sum of the backgr weights in the node, if traininfo defined

void	SetNBkgEvents_unboosted (Float_t b)
	set the sum of the unboosted backgr events in the node, if traininfo defined

void	SetNBkgEvents_unweighted (Float_t b)
	set the sum of the unweighted backgr events in the node, if traininfo defined

void	SetNBValidation (Double_t b)
	set number of background events from the pruning validation sample, if traininfo defined

void	SetNEvents (Float_t nev)
	set the number of events that entered the node (during training), if traininfo defined

void	SetNEvents_unboosted (Float_t nev)
	set the number of unboosted events that entered the node (during training), if traininfo defined

void	SetNEvents_unweighted (Float_t nev)
	set the number of unweighted events that entered the node (during training), if traininfo defined

void	SetNFisherCoeff (Int_t nvars)

void	SetNodeR (Double_t r)
	set the node resubstitution estimate, R(t), for Cost Complexity pruning, if traininfo defined

void	SetNodeType (Int_t t)
	set node type: 1 signal node, -1 bkg leave, 0 intermediate Node

void	SetNSigEvents (Float_t s)
	set the sum of the signal weights in the node, if traininfo defined

void	SetNSigEvents_unboosted (Float_t s)
	set the sum of the unboosted signal events in the node, if traininfo defined

void	SetNSigEvents_unweighted (Float_t s)
	set the sum of the unweighted signal events in the node, if traininfo defined

void	SetNSValidation (Double_t s)
	set number of signal events from the pruning validation sample, if traininfo defined

void	SetNTerminal (Int_t n)
	set number of terminal nodes in the subtree rooted here, if traininfo defined

virtual void	SetParent (Node *p)

void	SetPurity (void)
	return the S/(S+B) (purity) for the node REM: even if nodes with purity 0.01 are very PURE background nodes, they still get a small value of the purity.

void	SetResponse (Float_t r)
	set the response of the node (for regression)

virtual void	SetRight (Node *r)

void	SetRMS (Float_t r)
	set the RMS of the response of the node (for regression)

void	SetSampleMax (UInt_t ivar, Float_t xmax)
	set the maximum of variable ivar from the training sample that pass/end up in this node, if traininfo defined

void	SetSampleMin (UInt_t ivar, Float_t xmin)
	set the minimum of variable ivar from the training sample that pass/end up in this node, if traininfo defined

void	SetSelector (Short_t i)
	set index of variable used for discrimination at this node

void	SetSeparationGain (Float_t sep)
	set the separation, or information gained BY this node's selection, if traininfo defined

void	SetSeparationIndex (Float_t sep)
	set the chosen index, measure of "purity" (separation between S and B) AT this node, if traininfo defined

void	SetSubTreeR (Double_t r)
	set the resubstitution estimate, R(T_t), of the tree rooted at this node, if traininfo defined

void	SetSumTarget (Float_t t)
	set sum target, if traininfo defined

void	SetSumTarget2 (Float_t t2)
	set sum target 2, if traininfo defined

void	SetTerminal (Bool_t s=kTRUE)

virtual void	Streamer (TBuffer &)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

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

	Node (const Node &n)
	copy constructor, make sure you don't just copy the pointer to the node, but that the parents/daughters are initialized to 0 (and set by the copy constructors of the derived classes

	Node (Node *p, char pos)
	constructor of a daughter node as a daughter of 'p'

virtual	~Node ()
	node destructor

void *	AddXMLTo (void *parent) const
	add attributes to XML

Int_t	CountMeAndAllDaughters () const
	recursively go through the part of the tree below this node and count all daughters

int	GetCount ()
	returns the global number of instantiated nodes

UInt_t	GetDepth () const

virtual TMVA::BinaryTree *	GetParentTree () const

char	GetPos () const

void	ReadXML (void *node, UInt_t tmva_Version_Code=262657)
	read attributes from XML

void	SetDepth (UInt_t d)

virtual void	SetParentTree (TMVA::BinaryTree *t)

void	SetPos (char s)

void	StreamerNVirtual (TBuffer &ClassDef_StreamerNVirtual_b)

Static Public Member Functions
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

static UInt_t	GetTmvaVersionCode ()

static bool	IsTraining ()

static void	SetIsTraining (bool on)

static void	SetTmvaVersionCode (UInt_t code)

Static Public Member Functions inherited from TMVA::Node
static TClass *	Class ()

static const char *	Class_Name ()

static constexpr Version_t	Class_Version ()

static const char *	DeclFileName ()

Static Protected Member Functions
static MsgLogger &	Log ()

Protected Attributes
Bool_t	fCutType
	true: if event variable > cutValue ==> signal , false otherwise

Float_t	fCutValue
	cut value applied on this node to discriminate bkg against sig

std::vector< Double_t >	fFisherCoeff
	the fisher coeff (offset at the last element)

Bool_t	fIsTerminalNode
	! flag to set node as terminal (i.e., without deleting its descendants)

Int_t	fNodeType
	Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal.

Float_t	fPurity
	the node purity

Float_t	fResponse
	response value in case of regression

Float_t	fRMS
	response RMS of the regression node

Short_t	fSelector
	index of variable used in node selection (decision tree)

DTNodeTrainingInfo *	fTrainInfo

Protected Attributes inherited from TMVA::Node
UInt_t	fDepth
	depth of the node within the tree (seen from root node)

Node *	fLeft
	pointers to the two "daughter" nodes

Node *	fParent
	the previous (parent) node

BinaryTree *	fParentTree
	pointer to the parent tree to which the Node belongs

char	fPos
	position, i.e. it is a left (l) or right (r) daughter

Node *	fRight
	pointers to the two "daughter" nodes

Static Protected Attributes
static bool	fgIsTraining = false
	static variable to flag training phase in which we need fTrainInfo

static UInt_t	fgTmva_Version_Code = 0
	set only when read from weightfile

#include <TMVA/DecisionTreeNode.h>

Inheritance diagram for TMVA::DecisionTreeNode:

[legend]

Constructor & Destructor Documentation

◆ DecisionTreeNode() [1/3]

TMVA::DecisionTreeNode::DecisionTreeNode ( )

constructor of an essentially "empty" node floating in space

Definition at line 67 of file DecisionTreeNode.cxx.

◆ DecisionTreeNode() [2/3]

TMVA::DecisionTreeNode::DecisionTreeNode	(	TMVA::Node *	p,
		char	pos
	)

constructor of a daughter node as a daughter of 'p'

Definition at line 91 of file DecisionTreeNode.cxx.

◆ DecisionTreeNode() [3/3]

TMVA::DecisionTreeNode::DecisionTreeNode	(	const DecisionTreeNode &	n,
		DecisionTreeNode *	parent = `nullptr`
	)

copy constructor of a node.

It will result in an explicit copy of the node and recursively all it's daughters

Definition at line 116 of file DecisionTreeNode.cxx.

◆ ~DecisionTreeNode()

TMVA::DecisionTreeNode::~DecisionTreeNode ( )

virtual

destructor

Definition at line 148 of file DecisionTreeNode.cxx.

Member Function Documentation

◆ AddAttributesToNode()

void TMVA::DecisionTreeNode::AddAttributesToNode ( void * node ) const

virtual

add attribute to xml

Implements TMVA::Node.

Definition at line 495 of file DecisionTreeNode.cxx.

◆ AddContentToNode()

void TMVA::DecisionTreeNode::AddContentToNode ( std::stringstream & s ) const

virtual

adding attributes to tree node (well, was used in BinarySearchTree, and somehow I guess someone programmed it such that we need this in this tree too, although we don't..)

Implements TMVA::Node.

Definition at line 529 of file DecisionTreeNode.cxx.

◆ AddToSumTarget()

void TMVA::DecisionTreeNode::AddToSumTarget ( Float_t t )

inline

add to sum target, if traininfo defined

Definition at line 335 of file DecisionTreeNode.h.

◆ AddToSumTarget2()

void TMVA::DecisionTreeNode::AddToSumTarget2 ( Float_t t2 )

inline

add to sum target 2, if traininfo defined

Definition at line 337 of file DecisionTreeNode.h.

◆ Class()

static TClass * TMVA::DecisionTreeNode::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

static const char * TMVA::DecisionTreeNode::Class_Name ( )

static

Returns: Name of this class

◆ Class_Version()

static constexpr Version_t TMVA::DecisionTreeNode::Class_Version ( )

inlinestaticconstexpr

Returns: Version of this class

Definition at line 397 of file DecisionTreeNode.h.

◆ ClearNodeAndAllDaughters()

void TMVA::DecisionTreeNode::ClearNodeAndAllDaughters ( )

clear the nodes (their S/N, Nevents etc), just keep the structure of the tree

Definition at line 346 of file DecisionTreeNode.cxx.

◆ CreateNode()

virtual Node * TMVA::DecisionTreeNode::CreateNode ( ) const

inlinevirtual

Implements TMVA::Node.

Definition at line 132 of file DecisionTreeNode.h.

◆ DeclFileName()

static const char * TMVA::DecisionTreeNode::DeclFileName ( )

inlinestatic

Returns: Name of the file containing the class declaration

Definition at line 397 of file DecisionTreeNode.h.

◆ GetAlpha()

Double_t TMVA::DecisionTreeNode::GetAlpha ( ) const

inline

return the critical point alpha, or -1 if traininfo undefined

Definition at line 308 of file DecisionTreeNode.h.

◆ GetAlphaMinSubtree()

Double_t TMVA::DecisionTreeNode::GetAlphaMinSubtree ( ) const

inline

return the minimum alpha in the tree rooted at this node, or -1 if traininfo undefined

Definition at line 313 of file DecisionTreeNode.h.

◆ GetCC()

Double_t TMVA::DecisionTreeNode::GetCC ( ) const

inline

return CC, or -1 if traininfo undefined

Definition at line 356 of file DecisionTreeNode.h.

◆ GetCutType()

Bool_t TMVA::DecisionTreeNode::GetCutType ( void ) const

inline

return kTRUE: Cuts select signal, kFALSE: Cuts select bkg

Definition at line 160 of file DecisionTreeNode.h.

◆ GetCutValue()

Float_t TMVA::DecisionTreeNode::GetCutValue ( void ) const

inline

return the cut value applied at this node

Definition at line 155 of file DecisionTreeNode.h.

◆ GetFisherCoeff()

Double_t TMVA::DecisionTreeNode::GetFisherCoeff ( Int_t ivar ) const

inline

get fisher coefficients

Definition at line 139 of file DecisionTreeNode.h.

◆ GetLeft()

virtual DecisionTreeNode * TMVA::DecisionTreeNode::GetLeft ( ) const

inlinevirtual

Reimplemented from TMVA::Node.

Definition at line 283 of file DecisionTreeNode.h.

◆ GetNBkgEvents()

Float_t TMVA::DecisionTreeNode::GetNBkgEvents ( void ) const

inline

return the sum of the backgr weights in the node, or -1 if traininfo undefined

Definition at line 233 of file DecisionTreeNode.h.

◆ GetNBkgEvents_unboosted()

Float_t TMVA::DecisionTreeNode::GetNBkgEvents_unboosted ( void ) const

inline

return the sum of unboosted backgr weights in the node, or -1 if traininfo undefined

Definition at line 251 of file DecisionTreeNode.h.

◆ GetNBkgEvents_unweighted()

Float_t TMVA::DecisionTreeNode::GetNBkgEvents_unweighted ( void ) const

inline

return the sum of unweighted backgr weights in the node, or -1 if traininfo undefined

Definition at line 242 of file DecisionTreeNode.h.

◆ GetNBValidation()

Double_t TMVA::DecisionTreeNode::GetNBValidation ( ) const

inline

return number of background events from the pruning validation sample, or -1 if traininfo undefined

Definition at line 325 of file DecisionTreeNode.h.

◆ GetNEvents()

Float_t TMVA::DecisionTreeNode::GetNEvents ( void ) const

inline

return the number of events that entered the node (during training), or -1 if traininfo undefined

Definition at line 236 of file DecisionTreeNode.h.

◆ GetNEvents_unboosted()

Float_t TMVA::DecisionTreeNode::GetNEvents_unboosted ( void ) const

inline

return the number of unboosted events that entered the node (during training), or -1 if traininfo undefined

Definition at line 254 of file DecisionTreeNode.h.

◆ GetNEvents_unweighted()

Float_t TMVA::DecisionTreeNode::GetNEvents_unweighted ( void ) const

inline

return the number of unweighted events that entered the node (during training), or -1 if traininfo undefined

Definition at line 245 of file DecisionTreeNode.h.

◆ GetNFisherCoeff()

UInt_t TMVA::DecisionTreeNode::GetNFisherCoeff ( ) const

inline

Definition at line 135 of file DecisionTreeNode.h.

◆ GetNodeR()

Double_t TMVA::DecisionTreeNode::GetNodeR ( ) const

inline

return the node resubstitution estimate, R(t), for Cost Complexity pruning, or -1 if traininfo undefined

Definition at line 295 of file DecisionTreeNode.h.

◆ GetNodeType()

Int_t TMVA::DecisionTreeNode::GetNodeType ( void ) const

inline

return node type: 1 signal node, -1 bkg leave, 0 intermediate Node

Definition at line 165 of file DecisionTreeNode.h.

◆ GetNSigEvents()

Float_t TMVA::DecisionTreeNode::GetNSigEvents ( void ) const

inline

return the sum of the signal weights in the node, or -1 if traininfo undefined

Definition at line 230 of file DecisionTreeNode.h.

◆ GetNSigEvents_unboosted()

Float_t TMVA::DecisionTreeNode::GetNSigEvents_unboosted ( void ) const

inline

return the sum of unboosted signal weights in the node, or -1 if traininfo undefined

Definition at line 248 of file DecisionTreeNode.h.

◆ GetNSigEvents_unweighted()

Float_t TMVA::DecisionTreeNode::GetNSigEvents_unweighted ( void ) const

inline

Definition at line 239 of file DecisionTreeNode.h.

◆ GetNSValidation()

Double_t TMVA::DecisionTreeNode::GetNSValidation ( ) const

inline

return number of signal events from the pruning validation sample, or -1 if traininfo undefined

Definition at line 327 of file DecisionTreeNode.h.

◆ GetNTerminal()

Int_t TMVA::DecisionTreeNode::GetNTerminal ( ) const

inline

return number of terminal nodes in the subtree rooted here, or -1 if traininfo undefined

Definition at line 318 of file DecisionTreeNode.h.

◆ GetParent()

virtual DecisionTreeNode * TMVA::DecisionTreeNode::GetParent ( ) const

inlinevirtual

Reimplemented from TMVA::Node.

Definition at line 285 of file DecisionTreeNode.h.

◆ GetPurity()

Float_t TMVA::DecisionTreeNode::GetPurity ( void ) const

inline

return S/(S+B) (purity) at this node (from training)

Definition at line 168 of file DecisionTreeNode.h.

◆ GetResponse()

Float_t TMVA::DecisionTreeNode::GetResponse ( void ) const

inline

return the response of the node (for regression)

Definition at line 176 of file DecisionTreeNode.h.

◆ GetRight()

virtual DecisionTreeNode * TMVA::DecisionTreeNode::GetRight ( ) const

inlinevirtual

Reimplemented from TMVA::Node.

Definition at line 284 of file DecisionTreeNode.h.

◆ GetRMS()

Float_t TMVA::DecisionTreeNode::GetRMS ( void ) const

inline

return the RMS of the response of the node (for regression)

Definition at line 182 of file DecisionTreeNode.h.

◆ GetSampleMax()

Float_t TMVA::DecisionTreeNode::GetSampleMax ( UInt_t ivar ) const

return the maximum of variable ivar from the training sample that pass/end up in this node, if traininfo defined, otherwise Log Fatal and return 9999

Definition at line 427 of file DecisionTreeNode.cxx.

◆ GetSampleMin()

Float_t TMVA::DecisionTreeNode::GetSampleMin ( UInt_t ivar ) const

return the minimum of variable ivar from the training sample that pass/end up in this node, if traininfo defined, otherwise Log Fatal and return -9999

Definition at line 415 of file DecisionTreeNode.cxx.

◆ GetSelector()

Short_t TMVA::DecisionTreeNode::GetSelector ( ) const

inline

return index of variable used for discrimination at this node

Definition at line 150 of file DecisionTreeNode.h.

◆ GetSeparationGain()

Float_t TMVA::DecisionTreeNode::GetSeparationGain ( void ) const

inline

return the gain in separation obtained by this node's selection, or -1 if traininfo undefined

Definition at line 266 of file DecisionTreeNode.h.

◆ GetSeparationIndex()

Float_t TMVA::DecisionTreeNode::GetSeparationIndex ( void ) const

inline

return the separation index AT this node, or 0 if traininfo undefined

Definition at line 260 of file DecisionTreeNode.h.

◆ GetSubTreeR()

Double_t TMVA::DecisionTreeNode::GetSubTreeR ( ) const

inline

return the resubstitution estimate, R(T_t), of the tree rooted at this node, or -1 if traininfo undefined

Definition at line 300 of file DecisionTreeNode.h.

◆ GetSumTarget()

Float_t TMVA::DecisionTreeNode::GetSumTarget ( ) const

inline

return sum target, or -9999 if traininfo undefined

Definition at line 340 of file DecisionTreeNode.h.

◆ GetSumTarget2()

Float_t TMVA::DecisionTreeNode::GetSumTarget2 ( ) const

inline

return sum target 2, or -9999 if traininfo undefined

Definition at line 342 of file DecisionTreeNode.h.

◆ GetTmvaVersionCode()

UInt_t TMVA::DecisionTreeNode::GetTmvaVersionCode ( )

static

Definition at line 561 of file DecisionTreeNode.cxx.

◆ GoesLeft()

Bool_t TMVA::DecisionTreeNode::GoesLeft ( const Event & e ) const

virtual

test event if it descends the tree at this node to the left

Implements TMVA::Node.

Definition at line 179 of file DecisionTreeNode.cxx.

◆ GoesRight()

Bool_t TMVA::DecisionTreeNode::GoesRight ( const Event & e ) const

virtual

test event if it descends the tree at this node to the right

Implements TMVA::Node.

Definition at line 155 of file DecisionTreeNode.cxx.

◆ IncrementNBkgEvents()

void TMVA::DecisionTreeNode::IncrementNBkgEvents ( Float_t b )

inline

increment the sum of the backgr weights in the node, if traininfo defined

Definition at line 215 of file DecisionTreeNode.h.

◆ IncrementNBkgEvents_unweighted()

void TMVA::DecisionTreeNode::IncrementNBkgEvents_unweighted ( )

inline

increment the sum of the backgr weights in the node, if traininfo defined

Definition at line 224 of file DecisionTreeNode.h.

◆ IncrementNEvents()

void TMVA::DecisionTreeNode::IncrementNEvents ( Float_t nev )

inline

Definition at line 218 of file DecisionTreeNode.h.

◆ IncrementNEvents_unweighted()

void TMVA::DecisionTreeNode::IncrementNEvents_unweighted ( )

inline

increment the number of events that entered the node (during training), if traininfo defined

Definition at line 227 of file DecisionTreeNode.h.

◆ IncrementNSigEvents()

void TMVA::DecisionTreeNode::IncrementNSigEvents ( Float_t s )

inline

increment the sum of the signal weights in the node, if traininfo defined

Definition at line 212 of file DecisionTreeNode.h.

◆ IncrementNSigEvents_unweighted()

void TMVA::DecisionTreeNode::IncrementNSigEvents_unweighted ( )

inline

increment the sum of the signal weights in the node, if traininfo defined

Definition at line 221 of file DecisionTreeNode.h.

◆ IsA()

virtual TClass * TMVA::DecisionTreeNode::IsA ( ) const

inlinevirtual

Returns: TClass describing current object

Reimplemented from TMVA::Node.

Definition at line 397 of file DecisionTreeNode.h.

◆ IsTerminal()

Bool_t TMVA::DecisionTreeNode::IsTerminal ( ) const

inline

flag indicates whether this node is terminal

Definition at line 349 of file DecisionTreeNode.h.

◆ IsTraining()

Bool_t TMVA::DecisionTreeNode::IsTraining ( )

static

Definition at line 557 of file DecisionTreeNode.cxx.

◆ Log()

TMVA::MsgLogger & TMVA::DecisionTreeNode::Log ( )

staticprotected

Definition at line 543 of file DecisionTreeNode.cxx.

◆ Print()

void TMVA::DecisionTreeNode::Print ( std::ostream & os ) const

virtual

print the node

Implements TMVA::Node.

Definition at line 209 of file DecisionTreeNode.cxx.

◆ PrintPrune()

void TMVA::DecisionTreeNode::PrintPrune ( std::ostream & os ) const

printout of the node (can be read in with ReadDataRecord)

Definition at line 381 of file DecisionTreeNode.cxx.

◆ PrintRec()

void TMVA::DecisionTreeNode::PrintRec ( std::ostream & os ) const

virtual

recursively print the node and its daughters (--> print the 'tree')

Implements TMVA::Node.

Definition at line 241 of file DecisionTreeNode.cxx.

◆ PrintRecPrune()

void TMVA::DecisionTreeNode::PrintRecPrune ( std::ostream & os ) const

recursive printout of the node and its daughters

Definition at line 393 of file DecisionTreeNode.cxx.

◆ ReadAttributes()

void TMVA::DecisionTreeNode::ReadAttributes	(	void *	node,
		UInt_t	tmva_Version_Code = `262657`
	)

virtual

Implements TMVA::Node.

Definition at line 458 of file DecisionTreeNode.cxx.

◆ ReadContent()

void TMVA::DecisionTreeNode::ReadContent ( std::stringstream & s )

virtual

reading attributes from tree node (well, was used in BinarySearchTree, and somehow I guess someone programmed it such that we need this in this tree too, although we don't..)

Implements TMVA::Node.

Definition at line 538 of file DecisionTreeNode.cxx.

◆ ReadDataRecord()

Bool_t TMVA::DecisionTreeNode::ReadDataRecord	(	std::istream &	is,
		UInt_t	tmva_Version_Code = `262657`
	)

virtual

Read the data block.

Implements TMVA::Node.

Definition at line 272 of file DecisionTreeNode.cxx.

◆ ResetValidationData()

void TMVA::DecisionTreeNode::ResetValidationData ( )

temporary stored node values (number of events, etc.) that originate not from the training but from the validation data (used in pruning)

Definition at line 366 of file DecisionTreeNode.cxx.

◆ SetAlpha()

void TMVA::DecisionTreeNode::SetAlpha ( Double_t alpha )

inline

set the critical point alpha, if traininfo defined

Definition at line 306 of file DecisionTreeNode.h.

◆ SetAlphaMinSubtree()

void TMVA::DecisionTreeNode::SetAlphaMinSubtree ( Double_t g )

inline

set the minimum alpha in the tree rooted at this node, if traininfo defined

Definition at line 311 of file DecisionTreeNode.h.

◆ SetCC()

void TMVA::DecisionTreeNode::SetCC ( Double_t cc )

Set CC, if traininfo defined, otherwise Log Fatal.

Definition at line 404 of file DecisionTreeNode.cxx.

◆ SetCutType()

void TMVA::DecisionTreeNode::SetCutType ( Bool_t t )

inline

set true: if event variable > cutValue ==> signal , false otherwise

Definition at line 158 of file DecisionTreeNode.h.

◆ SetCutValue()

void TMVA::DecisionTreeNode::SetCutValue ( Float_t c )

inline

set the cut value applied at this node

Definition at line 153 of file DecisionTreeNode.h.

◆ SetFisherCoeff()

void TMVA::DecisionTreeNode::SetFisherCoeff	(	Int_t	ivar,
		Double_t	coeff
	)

set fisher coefficients

Definition at line 518 of file DecisionTreeNode.cxx.

◆ SetIsTraining()

void TMVA::DecisionTreeNode::SetIsTraining ( bool on )

static

Definition at line 549 of file DecisionTreeNode.cxx.

◆ SetLeft()

virtual void TMVA::DecisionTreeNode::SetLeft ( Node * l )

inlinevirtual

Reimplemented from TMVA::Node.

Definition at line 288 of file DecisionTreeNode.h.

◆ SetNBkgEvents()

void TMVA::DecisionTreeNode::SetNBkgEvents ( Float_t b )

inline

set the sum of the backgr weights in the node, if traininfo defined

Definition at line 188 of file DecisionTreeNode.h.

◆ SetNBkgEvents_unboosted()

void TMVA::DecisionTreeNode::SetNBkgEvents_unboosted ( Float_t b )

inline

set the sum of the unboosted backgr events in the node, if traininfo defined

Definition at line 206 of file DecisionTreeNode.h.

◆ SetNBkgEvents_unweighted()

void TMVA::DecisionTreeNode::SetNBkgEvents_unweighted ( Float_t b )

inline

set the sum of the unweighted backgr events in the node, if traininfo defined

Definition at line 197 of file DecisionTreeNode.h.

◆ SetNBValidation()

void TMVA::DecisionTreeNode::SetNBValidation ( Double_t b )

inline

set number of background events from the pruning validation sample, if traininfo defined

Definition at line 321 of file DecisionTreeNode.h.

◆ SetNEvents()

void TMVA::DecisionTreeNode::SetNEvents ( Float_t nev )

inline

set the number of events that entered the node (during training), if traininfo defined

Definition at line 191 of file DecisionTreeNode.h.

◆ SetNEvents_unboosted()

void TMVA::DecisionTreeNode::SetNEvents_unboosted ( Float_t nev )

inline

set the number of unboosted events that entered the node (during training), if traininfo defined

Definition at line 209 of file DecisionTreeNode.h.

◆ SetNEvents_unweighted()

void TMVA::DecisionTreeNode::SetNEvents_unweighted ( Float_t nev )

inline

set the number of unweighted events that entered the node (during training), if traininfo defined

Definition at line 200 of file DecisionTreeNode.h.

◆ SetNFisherCoeff()

void TMVA::DecisionTreeNode::SetNFisherCoeff ( Int_t nvars )

inline

Definition at line 134 of file DecisionTreeNode.h.

◆ SetNodeR()

void TMVA::DecisionTreeNode::SetNodeR ( Double_t r )

inline

set the node resubstitution estimate, R(t), for Cost Complexity pruning, if traininfo defined

Definition at line 293 of file DecisionTreeNode.h.

◆ SetNodeType()

void TMVA::DecisionTreeNode::SetNodeType ( Int_t t )

inline

set node type: 1 signal node, -1 bkg leave, 0 intermediate Node

Definition at line 163 of file DecisionTreeNode.h.

◆ SetNSigEvents()

void TMVA::DecisionTreeNode::SetNSigEvents ( Float_t s )

inline

set the sum of the signal weights in the node, if traininfo defined

Definition at line 185 of file DecisionTreeNode.h.

◆ SetNSigEvents_unboosted()

void TMVA::DecisionTreeNode::SetNSigEvents_unboosted ( Float_t s )

inline

set the sum of the unboosted signal events in the node, if traininfo defined

Definition at line 203 of file DecisionTreeNode.h.

◆ SetNSigEvents_unweighted()

void TMVA::DecisionTreeNode::SetNSigEvents_unweighted ( Float_t s )

inline

set the sum of the unweighted signal events in the node, if traininfo defined

Definition at line 194 of file DecisionTreeNode.h.

◆ SetNSValidation()

void TMVA::DecisionTreeNode::SetNSValidation ( Double_t s )

inline

set number of signal events from the pruning validation sample, if traininfo defined

Definition at line 323 of file DecisionTreeNode.h.

◆ SetNTerminal()

void TMVA::DecisionTreeNode::SetNTerminal ( Int_t n )

inline

set number of terminal nodes in the subtree rooted here, if traininfo defined

Definition at line 316 of file DecisionTreeNode.h.

◆ SetParent()

virtual void TMVA::DecisionTreeNode::SetParent ( Node * p )

inlinevirtual

Reimplemented from TMVA::Node.

Definition at line 290 of file DecisionTreeNode.h.

◆ SetPurity()

void TMVA::DecisionTreeNode::SetPurity ( void )

return the S/(S+B) (purity) for the node REM: even if nodes with purity 0.01 are very PURE background nodes, they still get a small value of the purity.

Definition at line 191 of file DecisionTreeNode.cxx.

◆ SetResponse()

void TMVA::DecisionTreeNode::SetResponse ( Float_t r )

inline

set the response of the node (for regression)

Definition at line 173 of file DecisionTreeNode.h.

◆ SetRight()

virtual void TMVA::DecisionTreeNode::SetRight ( Node * r )

inlinevirtual

Reimplemented from TMVA::Node.

Definition at line 289 of file DecisionTreeNode.h.

◆ SetRMS()

void TMVA::DecisionTreeNode::SetRMS ( Float_t r )

inline

set the RMS of the response of the node (for regression)

Definition at line 179 of file DecisionTreeNode.h.

◆ SetSampleMax()

void TMVA::DecisionTreeNode::SetSampleMax	(	UInt_t	ivar,
		Float_t	xmax
	)

set the maximum of variable ivar from the training sample that pass/end up in this node, if traininfo defined

Definition at line 449 of file DecisionTreeNode.cxx.

◆ SetSampleMin()

void TMVA::DecisionTreeNode::SetSampleMin	(	UInt_t	ivar,
		Float_t	xmin
	)

set the minimum of variable ivar from the training sample that pass/end up in this node, if traininfo defined

Definition at line 438 of file DecisionTreeNode.cxx.

◆ SetSelector()

void TMVA::DecisionTreeNode::SetSelector ( Short_t i )

inline

set index of variable used for discrimination at this node

Definition at line 148 of file DecisionTreeNode.h.

◆ SetSeparationGain()

void TMVA::DecisionTreeNode::SetSeparationGain ( Float_t sep )

inline

set the separation, or information gained BY this node's selection, if traininfo defined

Definition at line 263 of file DecisionTreeNode.h.

◆ SetSeparationIndex()

void TMVA::DecisionTreeNode::SetSeparationIndex ( Float_t sep )

inline

set the chosen index, measure of "purity" (separation between S and B) AT this node, if traininfo defined

Definition at line 257 of file DecisionTreeNode.h.

◆ SetSubTreeR()

void TMVA::DecisionTreeNode::SetSubTreeR ( Double_t r )

inline

set the resubstitution estimate, R(T_t), of the tree rooted at this node, if traininfo defined

Definition at line 298 of file DecisionTreeNode.h.

◆ SetSumTarget()

void TMVA::DecisionTreeNode::SetSumTarget ( Float_t t )

inline

set sum target, if traininfo defined

Definition at line 330 of file DecisionTreeNode.h.

◆ SetSumTarget2()

void TMVA::DecisionTreeNode::SetSumTarget2 ( Float_t t2 )

inline

set sum target 2, if traininfo defined

Definition at line 332 of file DecisionTreeNode.h.

◆ SetTerminal()

void TMVA::DecisionTreeNode::SetTerminal ( Bool_t s = kTRUE )

inline

Definition at line 350 of file DecisionTreeNode.h.

◆ SetTmvaVersionCode()

void TMVA::DecisionTreeNode::SetTmvaVersionCode ( UInt_t code )

static

Definition at line 553 of file DecisionTreeNode.cxx.

◆ Streamer()

virtual void TMVA::DecisionTreeNode::Streamer ( TBuffer & )

virtual

Reimplemented from TMVA::Node.

◆ StreamerNVirtual()

void TMVA::DecisionTreeNode::StreamerNVirtual ( TBuffer & ClassDef_StreamerNVirtual_b )

inline

Definition at line 397 of file DecisionTreeNode.h.

Member Data Documentation

◆ fCutType

Bool_t TMVA::DecisionTreeNode::fCutType

protected

true: if event variable > cutValue ==> signal , false otherwise

Definition at line 383 of file DecisionTreeNode.h.

◆ fCutValue

Float_t TMVA::DecisionTreeNode::fCutValue

protected

cut value applied on this node to discriminate bkg against sig

Definition at line 382 of file DecisionTreeNode.h.

◆ fFisherCoeff

std::vector<Double_t> TMVA::DecisionTreeNode::fFisherCoeff

protected

the fisher coeff (offset at the last element)

Definition at line 380 of file DecisionTreeNode.h.

◆ fgIsTraining

Bool_t TMVA::DecisionTreeNode::fgIsTraining = false

staticprotected

static variable to flag training phase in which we need fTrainInfo

Definition at line 377 of file DecisionTreeNode.h.

◆ fgTmva_Version_Code

UInt_t TMVA::DecisionTreeNode::fgTmva_Version_Code = 0

staticprotected

set only when read from weightfile

Definition at line 378 of file DecisionTreeNode.h.

◆ fIsTerminalNode

Bool_t TMVA::DecisionTreeNode::fIsTerminalNode

protected

! flag to set node as terminal (i.e., without deleting its descendants)

Definition at line 391 of file DecisionTreeNode.h.

◆ fNodeType

Int_t TMVA::DecisionTreeNode::fNodeType

protected

Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal.

Definition at line 388 of file DecisionTreeNode.h.

◆ fPurity

Float_t TMVA::DecisionTreeNode::fPurity

protected

the node purity

Definition at line 389 of file DecisionTreeNode.h.

◆ fResponse

Float_t TMVA::DecisionTreeNode::fResponse

protected

response value in case of regression

Definition at line 386 of file DecisionTreeNode.h.

◆ fRMS

Float_t TMVA::DecisionTreeNode::fRMS

protected

response RMS of the regression node

Definition at line 387 of file DecisionTreeNode.h.

◆ fSelector

Short_t TMVA::DecisionTreeNode::fSelector

protected

index of variable used in node selection (decision tree)

Definition at line 384 of file DecisionTreeNode.h.

◆ fTrainInfo

DTNodeTrainingInfo* TMVA::DecisionTreeNode::fTrainInfo

mutableprotected

Definition at line 393 of file DecisionTreeNode.h.

Libraries for TMVA::DecisionTreeNode:

[legend]

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

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

Public Member Functions

Static Public Member Functions

Static Protected Member Functions

Protected Attributes

Static Protected Attributes

Constructor & Destructor Documentation

◆ DecisionTreeNode() [1/3]

◆ DecisionTreeNode() [2/3]

◆ DecisionTreeNode() [3/3]

◆ ~DecisionTreeNode()

Member Function Documentation

◆ AddAttributesToNode()

◆ AddContentToNode()

◆ AddToSumTarget()

◆ AddToSumTarget2()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ ClearNodeAndAllDaughters()

◆ CreateNode()

◆ DeclFileName()

◆ GetAlpha()

◆ GetAlphaMinSubtree()

◆ GetCC()

◆ GetCutType()

◆ GetCutValue()

◆ GetFisherCoeff()

◆ GetLeft()

◆ GetNBkgEvents()

◆ GetNBkgEvents_unboosted()

◆ GetNBkgEvents_unweighted()

◆ GetNBValidation()

◆ GetNEvents()

◆ GetNEvents_unboosted()

◆ GetNEvents_unweighted()

◆ GetNFisherCoeff()

◆ GetNodeR()

◆ GetNodeType()

◆ GetNSigEvents()

◆ GetNSigEvents_unboosted()

◆ GetNSigEvents_unweighted()

◆ GetNSValidation()

◆ GetNTerminal()

◆ GetParent()

◆ GetPurity()

◆ GetResponse()

◆ GetRight()

◆ GetRMS()

◆ GetSampleMax()

◆ GetSampleMin()

◆ GetSelector()

◆ GetSeparationGain()

◆ GetSeparationIndex()

◆ GetSubTreeR()

◆ GetSumTarget()

◆ GetSumTarget2()

◆ GetTmvaVersionCode()

◆ GoesLeft()

◆ GoesRight()

◆ IncrementNBkgEvents()

◆ IncrementNBkgEvents_unweighted()

◆ IncrementNEvents()

◆ IncrementNEvents_unweighted()

◆ IncrementNSigEvents()

◆ IncrementNSigEvents_unweighted()

◆ IsA()

◆ IsTerminal()

◆ IsTraining()

◆ Log()

◆ Print()

◆ PrintPrune()

◆ PrintRec()

◆ PrintRecPrune()

◆ ReadAttributes()

◆ ReadContent()

◆ ReadDataRecord()

◆ ResetValidationData()

◆ SetAlpha()

◆ SetAlphaMinSubtree()

◆ SetCC()