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
void	AddAttributesToNode (void *node) const override
	add attribute to xml
void	AddContentToNode (std::stringstream &s) const override
	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 *	AddXMLTo (void *parent) const
	add attributes to XML
void	ClearNodeAndAllDaughters ()
	clear the nodes (their S/N, Nevents etc), just keep the structure of the tree
Int_t	CountMeAndAllDaughters () const
	recursively go through the part of the tree below this node and count all daughters
Node *	CreateNode () const override
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
int	GetCount ()
	returns the global number of instantiated nodes
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
UInt_t	GetDepth () const
Double_t	GetFisherCoeff (Int_t ivar) const
	get fisher coefficients
DecisionTreeNode *	GetLeft () const override
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
DecisionTreeNode *	GetParent () const override
virtual TMVA::BinaryTree *	GetParentTree () const
char	GetPos () 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)
DecisionTreeNode *	GetRight () const override
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
Bool_t	GoesLeft (const Event &) const override
	test event if it descends the tree at this node to the left
Bool_t	GoesRight (const Event &) const override
	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
TClass *	IsA () const override
Bool_t	IsTerminal () const
	flag indicates whether this node is terminal
void	Print (std::ostream &os) const override
	print the node
void	PrintPrune (std::ostream &os) const
	printout of the node (can be read in with ReadDataRecord)
void	PrintRec (std::ostream &os) const override
	recursively print the node and its daughters (--> print the 'tree')
void	PrintRecPrune (std::ostream &os) const
	recursive printout of the node and its daughters
void	ReadAttributes (void *node, UInt_t tmva_Version_Code=262657) override
void	ReadContent (std::stringstream &s) override
	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..)
Bool_t	ReadDataRecord (std::istream &is, UInt_t tmva_Version_Code=262657) override
	Read the data block.
void	ReadXML (void *node, UInt_t tmva_Version_Code=262657)
	read attributes from XML
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	SetDepth (UInt_t d)
void	SetFisherCoeff (Int_t ivar, Double_t coeff)
	set fisher coefficients
void	SetLeft (Node *l) override
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
void	SetParent (Node *p) override
virtual void	SetParentTree (TMVA::BinaryTree *t)
void	SetPos (char s)
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)
void	SetRight (Node *r) override
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)
void	Streamer (TBuffer &) override
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 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
UInt_t	fDepth
	depth of the node within the tree (seen from root node)
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)
Node *	fLeft
	pointers to the two "daughter" nodes
Int_t	fNodeType
	Type of node: -1 == Bkg-leaf, 1 == Signal-leaf, 0 = internal.
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
Float_t	fPurity
	the node purity
Float_t	fResponse
	response value in case of regression
Node *	fRight
	pointers to the two "daughter" nodes
Float_t	fRMS
	response RMS of the regression node
Short_t	fSelector
	index of variable used in node selection (decision tree)
DTNodeTrainingInfo *	fTrainInfo

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

Static Private Attributes
static Int_t	fgCount = 0
	counter of all nodes present.. for debug.. to spot memory leaks...

#include <TMVA/DecisionTreeNode.h>

Inheritance diagram for TMVA::DecisionTreeNode:

Constructor & Destructor Documentation

◆ DecisionTreeNode() [1/3]

TMVA::DecisionTreeNode::DecisionTreeNode ( )

constructor of an essentially "empty" node floating in space

Definition at line 66 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 90 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 115 of file DecisionTreeNode.cxx.

◆ ~DecisionTreeNode()

TMVA::DecisionTreeNode::~DecisionTreeNode ( )

virtual

destructor

Definition at line 147 of file DecisionTreeNode.cxx.

Member Function Documentation

◆ AddAttributesToNode()

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

overridevirtual

add attribute to xml

Implements TMVA::Node.

Definition at line 494 of file DecisionTreeNode.cxx.

◆ AddContentToNode()

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

overridevirtual

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 528 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.

◆ AddXMLTo()

void * TMVA::Node::AddXMLTo ( void * parent ) const

inherited

add attributes to XML

Definition at line 146 of file Node.cxx.

◆ Class()

TClass * TMVA::DecisionTreeNode::Class ( )

static

Returns: TClass describing this class

◆ Class_Name()

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

static

Returns: Name of this class

◆ Class_Version()

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 345 of file DecisionTreeNode.cxx.

◆ CountMeAndAllDaughters()

Int_t TMVA::Node::CountMeAndAllDaughters ( ) const

inherited

recursively go through the part of the tree below this node and count all daughters

Definition at line 113 of file Node.cxx.

◆ CreateNode()

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

inlineoverridevirtual

Implements TMVA::Node.

Definition at line 132 of file DecisionTreeNode.h.

◆ DeclFileName()

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.

◆ GetCount()

int TMVA::Node::GetCount ( )

inherited

returns the global number of instantiated nodes

Definition at line 105 of file Node.cxx.

◆ 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.

◆ GetDepth()

UInt_t TMVA::Node::GetDepth ( ) const

inlineinherited

Definition at line 116 of file Node.h.

◆ GetFisherCoeff()

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

inline

get fisher coefficients

Definition at line 139 of file DecisionTreeNode.h.

◆ GetLeft()

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

inlineoverridevirtual

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()

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

inlineoverridevirtual

Reimplemented from TMVA::Node.

Definition at line 285 of file DecisionTreeNode.h.

◆ GetParentTree()

virtual TMVA::BinaryTree * TMVA::Node::GetParentTree ( ) const

inlinevirtualinherited

Definition at line 125 of file Node.h.

◆ GetPos()

char TMVA::Node::GetPos ( ) const

inlineinherited

Definition at line 122 of file Node.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()

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

inlineoverridevirtual

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 426 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 414 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 560 of file DecisionTreeNode.cxx.

◆ GoesLeft()

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

overridevirtual

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

Implements TMVA::Node.

Definition at line 178 of file DecisionTreeNode.cxx.

◆ GoesRight()

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

overridevirtual

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

Implements TMVA::Node.

Definition at line 154 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()

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

inlineoverridevirtual

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 556 of file DecisionTreeNode.cxx.

◆ Log()

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

staticprotected

Definition at line 542 of file DecisionTreeNode.cxx.

◆ Print()

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

overridevirtual

print the node

Implements TMVA::Node.

Definition at line 208 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 380 of file DecisionTreeNode.cxx.

◆ PrintRec()

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

overridevirtual

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

Implements TMVA::Node.

Definition at line 240 of file DecisionTreeNode.cxx.

◆ PrintRecPrune()

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

recursive printout of the node and its daughters

Definition at line 392 of file DecisionTreeNode.cxx.

◆ ReadAttributes()

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

overridevirtual

Implements TMVA::Node.

Definition at line 457 of file DecisionTreeNode.cxx.

◆ ReadContent()

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

overridevirtual

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 537 of file DecisionTreeNode.cxx.

◆ ReadDataRecord()

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

overridevirtual

Read the data block.

Implements TMVA::Node.

Definition at line 271 of file DecisionTreeNode.cxx.

◆ ReadXML()

void TMVA::Node::ReadXML	(	void *	node,
		UInt_t	tmva_Version_Code = 262657 )

inherited

read attributes from XML

Definition at line 162 of file Node.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 365 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 403 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.

◆ SetDepth()

void TMVA::Node::SetDepth ( UInt_t d )

inlineinherited

Definition at line 113 of file Node.h.

◆ SetFisherCoeff()

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

set fisher coefficients

Definition at line 517 of file DecisionTreeNode.cxx.

◆ SetIsTraining()

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

static

Definition at line 548 of file DecisionTreeNode.cxx.

◆ SetLeft()

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

inlineoverridevirtual

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()

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

inlineoverridevirtual

Reimplemented from TMVA::Node.

Definition at line 290 of file DecisionTreeNode.h.

◆ SetParentTree()

virtual void TMVA::Node::SetParentTree ( TMVA::BinaryTree * t )

inlinevirtualinherited

Definition at line 128 of file Node.h.

◆ SetPos()

void TMVA::Node::SetPos ( char s )

inlineinherited

Definition at line 119 of file Node.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 190 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()

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

inlineoverridevirtual

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 448 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 437 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 552 of file DecisionTreeNode.cxx.

◆ Streamer()

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

overridevirtual

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.

◆ fDepth

UInt_t TMVA::Node::fDepth

protectedinherited

depth of the node within the tree (seen from root node)

Definition at line 143 of file Node.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.

◆ fgCount

Int_t TMVA::Node::fgCount = 0

staticprivateinherited

counter of all nodes present.. for debug.. to spot memory leaks...

Definition at line 148 of file Node.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.

◆ fLeft

Node* TMVA::Node::fLeft

protectedinherited

pointers to the two "daughter" nodes

Definition at line 139 of file Node.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.

◆ fParent

Node* TMVA::Node::fParent

protectedinherited

the previous (parent) node

Definition at line 138 of file Node.h.

◆ fParentTree

BinaryTree* TMVA::Node::fParentTree

protectedinherited

pointer to the parent tree to which the Node belongs

Definition at line 145 of file Node.h.

◆ fPos

char TMVA::Node::fPos

protectedinherited

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

Definition at line 142 of file Node.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.

◆ fRight

Node* TMVA::Node::fRight

protectedinherited

pointers to the two "daughter" nodes

Definition at line 140 of file Node.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.

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

Static Private Attributes

Constructor & Destructor Documentation

◆ DecisionTreeNode() [1/3]

◆ DecisionTreeNode() [2/3]

◆ DecisionTreeNode() [3/3]

◆ ~DecisionTreeNode()

Member Function Documentation

◆ AddAttributesToNode()

◆ AddContentToNode()

◆ AddToSumTarget()

◆ AddToSumTarget2()

◆ AddXMLTo()

◆ Class()

◆ Class_Name()

◆ Class_Version()

◆ ClearNodeAndAllDaughters()

◆ CountMeAndAllDaughters()

◆ CreateNode()

◆ DeclFileName()

◆ GetAlpha()

◆ GetAlphaMinSubtree()

◆ GetCC()

◆ GetCount()

◆ GetCutType()

◆ GetCutValue()

◆ GetDepth()

◆ GetFisherCoeff()

◆ GetLeft()

◆ GetNBkgEvents()

◆ GetNBkgEvents_unboosted()

◆ GetNBkgEvents_unweighted()

◆ GetNBValidation()

◆ GetNEvents()

◆ GetNEvents_unboosted()

◆ GetNEvents_unweighted()

◆ GetNFisherCoeff()

◆ GetNodeR()

◆ GetNodeType()

◆ GetNSigEvents()

◆ GetNSigEvents_unboosted()

◆ GetNSigEvents_unweighted()

◆ GetNSValidation()

◆ GetNTerminal()

◆ GetParent()

◆ GetParentTree()

◆ GetPos()

◆ 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()