Analysis of k-nearest neighbor.

Definition at line 53 of file MethodKNN.h.

Public Member Functions
	MethodKNN (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="KNN")
	standard constructor

	MethodKNN (DataSetInfo &theData, const TString &theWeightFile)
	constructor from weight file

virtual	~MethodKNN (void)
	destructor

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

const Ranking *	CreateRanking ()
	no ranking available

Double_t	GetMvaValue (Double_t err=0, Double_t errUpper=0)
	Compute classifier response.

const std::vector< Float_t > &	GetRegressionValues ()
	Return vector of averages for target values of k-nearest neighbors.

virtual Bool_t	HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
	FDA can handle classification with 2 classes and regression with one regression-target.

virtual void	ReadWeightsFromStream (std::istream &)=0

void	ReadWeightsFromStream (std::istream &istr)
	read the weights

virtual void	ReadWeightsFromStream (TFile &)

void	ReadWeightsFromStream (TFile &rf)
	read weights from ROOT file

void	ReadWeightsFromXML (void *wghtnode)

void	Train (void)
	kNN training

void	WriteWeightsToStream (TFile &rf) const
	save weights to ROOT file

Public Member Functions inherited from TMVA::MethodBase
	MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
	standard constructor

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

virtual	~MethodBase ()
	destructor

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

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

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

DataSet *	Data () const

DataSetInfo &	DataInfo () const

void	DisableWriting (Bool_t setter)

Bool_t	DoMulticlass () const

Bool_t	DoRegression () const

void	ExitFromTraining ()

Types::EAnalysisType	GetAnalysisType () const

UInt_t	GetCurrentIter ()

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

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

TFile *	GetFile () const

const TString &	GetInputLabel (Int_t i) const

const char *	GetInputTitle (Int_t i) const

const TString &	GetInputVar (Int_t i) const

TMultiGraph *	GetInteractiveTrainingError ()

const TString &	GetJobName () const

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

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

UInt_t	GetMaxIter ()

Double_t	GetMean (Int_t ivar) const

const TString &	GetMethodName () const

Types::EMVA	GetMethodType () const

TString	GetMethodTypeName () const

virtual TMatrixD	GetMulticlassConfusionMatrix (Double_t effB, Types::ETreeType type)
	Construct a confusion matrix for a multiclass classifier.

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

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

const char *	GetName () const

UInt_t	GetNEvents () const

UInt_t	GetNTargets () const

UInt_t	GetNvar () const

UInt_t	GetNVariables () const

virtual Double_t	GetProba (const Event *ev)

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

const TString	GetProbaName () const

virtual Double_t	GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
	compute rarity:

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

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

Double_t	GetRMS (Int_t ivar) const

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

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

virtual Double_t	GetSeparation (PDF pdfS=0, PDF pdfB=0) const
	compute "separation" defined as

virtual Double_t	GetSeparation (TH1 , TH1 ) const
	compute "separation" defined as

Double_t	GetSignalReferenceCut () const

Double_t	GetSignalReferenceCutOrientation () const

virtual Double_t	GetSignificance () const
	compute significance of mean difference

const Event *	GetTestingEvent (Long64_t ievt) const

Double_t	GetTestTime () const

const TString &	GetTestvarName () const

virtual Double_t	GetTrainingEfficiency (const TString &)

const Event *	GetTrainingEvent (Long64_t ievt) const

virtual const std::vector< Float_t > &	GetTrainingHistory (const char *)

UInt_t	GetTrainingROOTVersionCode () const

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

UInt_t	GetTrainingTMVAVersionCode () const

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

Double_t	GetTrainTime () const

TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true)

const TransformationHandler &	GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const

TString	GetWeightFileName () const
	retrieve weight file name

Double_t	GetXmax (Int_t ivar) const

Double_t	GetXmin (Int_t ivar) const

Bool_t	HasMVAPdfs () const

void	InitIPythonInteractive ()

Bool_t	IsModelPersistence () const

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

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 be selected as signal or background

Bool_t	IsSilentFile () const

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

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

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

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

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)

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

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

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

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

void	RerouteTransformationHandler (TransformationHandler *fTargetTransformation)

virtual void	Reset ()

virtual void	SetAnalysisType (Types::EAnalysisType type)

void	SetBaseDir (TDirectory *methodDir)

void	SetFile (TFile *file)

void	SetMethodBaseDir (TDirectory *methodDir)

void	SetMethodDir (TDirectory *methodDir)

void	SetModelPersistence (Bool_t status)

void	SetSignalReferenceCut (Double_t cut)

void	SetSignalReferenceCutOrientation (Double_t cutOrientation)

void	SetSilentFile (Bool_t status)

void	SetTestTime (Double_t testTime)

void	SetTestvarName (const TString &v="")

void	SetTrainTime (Double_t trainTime)

virtual void	SetTuneParameters (std::map< TString, Double_t > tuneParameters)
	set the tuning parameters according to the argument This is just a dummy .

void	SetupMethod ()
	setup of methods

virtual void	TestClassification ()
	initialization

virtual void	TestMulticlass ()
	test multiclass classification

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

bool	TrainingEnded ()

void	TrainMethod ()

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

virtual void	WriteMonitoringHistosToFile () const
	write special monitoring histograms to file dummy implementation here --------------—

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

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

virtual	~IMethod ()

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

virtual	~Configurable ()
	default destructor

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

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

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

template<class T >
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)

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

const char *	GetConfigDescription () const

const char *	GetConfigName () const

const TString &	GetOptions () const

MsgLogger &	Log () const

virtual void	ParseOptions ()
	options parser

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

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

void	ReadOptionsFromXML (void *node)

void	SetConfigDescription (const char *d)

void	SetConfigName (const char *n)

void	SetMsgType (EMsgType t)

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

Public Member Functions inherited from TNamed
	TNamed ()

	TNamed (const char name, const char title)

	TNamed (const TNamed &named)
	TNamed copy ctor.

	TNamed (const TString &name, const TString &title)

virtual	~TNamed ()
	TNamed destructor.

virtual void	Clear (Option_t *option="")
	Set name and title to empty strings ("").

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

virtual Int_t	Compare (const TObject *obj) const
	Compare two TNamed objects.

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

virtual void	FillBuffer (char *&buffer)
	Encode TNamed into output buffer.

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

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

virtual Bool_t	IsSortable () const

virtual void	ls (Option_t *option="") const
	List TNamed name and title.

TNamed &	operator= (const TNamed &rhs)
	TNamed assignment operator.

virtual void	Print (Option_t *option="") const
	Print TNamed name and title.

virtual void	SetName (const char *name)
	Set the name of the TNamed.

virtual void	SetNameTitle (const char name, const char title)
	Set all the TNamed parameters (name and title).

virtual void	SetTitle (const char *title="")
	Set the title of the TNamed.

virtual Int_t	Sizeof () const
	Return size of the TNamed part of the TObject.

Public Member Functions inherited from TObject
	TObject ()
	TObject constructor.

	TObject (const TObject &object)
	TObject copy ctor.

virtual	~TObject ()
	TObject destructor.

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

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

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

ULong_t	CheckedHash ()
	Check and record whether this class has a consistent Hash/RecursiveRemove setup (*) and then return the regular Hash value for this object.

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

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

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

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

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

virtual TObject *	DrawClone (Option_t *option="") const
	Draw a clone of this object in the current selected pad for instance with: `gROOT->SetSelectedPad(gPad)`.

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

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

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.

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

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

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

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

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

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

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

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

virtual Option_t *	GetOption () const

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

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

Bool_t	HasInconsistentHash () const
	Return true is the type of this object is known to have an inconsistent setup for Hash and RecursiveRemove (i.e.

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

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

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

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

void	InvertBit (UInt_t f)

Bool_t	IsDestructed () const
	IsDestructed.

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

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

R__ALWAYS_INLINE Bool_t	IsOnHeap () const

R__ALWAYS_INLINE Bool_t	IsZombie () const

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).

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

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

void	operator delete (void *ptr)
	Operator delete.

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

void *	operator new (size_t sz)

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

void *	operator new[] (size_t sz)

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

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

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

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

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

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

void	ResetBit (UInt_t f)

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

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

void	SetBit (UInt_t f)

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

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

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

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

R__ALWAYS_INLINE Bool_t	TestBit (UInt_t f) const

Int_t	TestBits (UInt_t f) const

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

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

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

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

Protected Member Functions
void	GetHelpMessage () const
	get help message text

void	MakeClassSpecific (std::ostream &, const TString &) const
	write specific classifier response

Protected Member Functions inherited from TMVA::MethodBase
virtual std::vector< Double_t >	GetDataMvaValues (DataSet *data=nullptr, Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the given Data type

const TString &	GetInternalVarName (Int_t ivar) const

virtual std::vector< Double_t >	GetMvaValues (Long64_t firstEvt=0, Long64_t lastEvt=-1, Bool_t logProgress=false)
	get all the MVA values for the events of the current Data type

const TString &	GetOriginalVarName (Int_t ivar) const

const TString &	GetWeightFileDir () const

Bool_t	HasTrainingTree () const

Bool_t	Help () const

Bool_t	IgnoreEventsWithNegWeightsInTraining () const

Bool_t	IsConstructedFromWeightFile () const

Bool_t	IsNormalised () const

virtual void	MakeClassSpecificHeader (std::ostream &, const TString &="") const

void	NoErrorCalc (Double_t const err, Double_t const errUpper)

void	SetNormalised (Bool_t norm)

void	SetWeightFileDir (TString fileDir)
	set directory of weight file

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

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

Bool_t	TxtWeightsOnly () const

Bool_t	Verbose () const

Protected Member Functions inherited from TMVA::Configurable
void	EnableLooseOptions (Bool_t b=kTRUE)

const TString &	GetReferenceFile () const

Bool_t	LooseOptionCheckingEnabled () const

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

void	WriteOptionsReferenceToFile ()
	write complete options to output stream

Protected Member Functions inherited from TObject
virtual void	DoError (int level, const char location, const char fmt, va_list va) const
	Interface to ErrorHandler (protected).

void	MakeZombie ()

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

void	DeclareOptions ()
	MethodKNN options.

Double_t	GausKernel (const kNN::Event &event_knn, const kNN::Event &event, const std::vector< Double_t > &svec) const
	Gaussian kernel.

Double_t	getKernelRadius (const kNN::List &rlist) const
	Get polynomial kernel radius.

double	getLDAValue (const kNN::List &rlist, const kNN::Event &event_knn)

const std::vector< Double_t >	getRMS (const kNN::List &rlist, const kNN::Event &event_knn) const
	Get polynomial kernel radius.

void	Init (void)
	Initialization.

void	MakeKNN (void)
	create kNN

Double_t	PolnKernel (Double_t value) const
	polynomial kernel

void	ProcessOptions ()
	process the options specified by the user

Private Attributes
Int_t	fBalanceDepth

kNN::EventVec	fEvent

TString	fKernel

LDA	fLDA
	(untouched) events used for learning

kNN::ModulekNN *	fModule

Int_t	fnkNN
	module where all work is done

Float_t	fScaleFrac

Float_t	fSigmaFact

Double_t	fSumOfWeightsB

Double_t	fSumOfWeightsS

Int_t	fTreeOptDepth
	Experimental feature for local knn analysis.

Bool_t	fTrim

Bool_t	fUseKernel

Bool_t	fUseLDA

Bool_t	fUseWeight

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

Public Types inherited from TObject
enum	{ kIsOnHeap = 0x01000000 , kNotDeleted = 0x02000000 , kZombie = 0x04000000 , kInconsistent = 0x08000000 , kBitMask = 0x00ffffff }

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

enum	EDeprecatedStatusBits { kObjInCanvas = BIT(3) }

enum	EStatusBits { kCanDelete = BIT(0) , kMustCleanup = BIT(3) , kIsReferenced = BIT(4) , kHasUUID = BIT(5) , kCannotPick = BIT(6) , kNoContextMenu = BIT(8) , kInvalidObject = BIT(13) }

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

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

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

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

Public Attributes inherited from TMVA::MethodBase
Bool_t	fSetupCompleted

TrainingHistory	fTrainHistory

Protected Types inherited from TObject
enum	{ kOnlyPrepStep = BIT(3) }

Protected Attributes inherited from TMVA::MethodBase
Types::EAnalysisType	fAnalysisType

UInt_t	fBackgroundClass

bool	fExitFromTraining = false

std::vector< TString > *	fInputVars

IPythonInteractive *	fInteractive = nullptr
	temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues)

UInt_t	fIPyCurrentIter = 0

UInt_t	fIPyMaxIter = 0

std::vector< Float_t > *	fMulticlassReturnVal

Int_t	fNbins

Int_t	fNbinsH

Int_t	fNbinsMVAoutput

Ranking *	fRanking

std::vector< Float_t > *	fRegressionReturnVal

Results *	fResults

UInt_t	fSignalClass

DataSet *	fTmpData = nullptr
	temporary event when testing on a different DataSet than the own one

const Event *	fTmpEvent

Protected Attributes inherited from TMVA::Configurable
MsgLogger *	fLogger

Protected Attributes inherited from TNamed
TString	fName

TString	fTitle

#include <TMVA/MethodKNN.h>

Inheritance diagram for TMVA::MethodKNN:

[legend]

Constructor & Destructor Documentation

◆ MethodKNN() [1/2]

TMVA::MethodKNN::MethodKNN	(	const TString &	jobName,
		const TString &	methodTitle,
		DataSetInfo &	theData,
		const TString &	theOption = `"KNN"`
	)

standard constructor

Definition at line 62 of file MethodKNN.cxx.

◆ MethodKNN() [2/2]

TMVA::MethodKNN::MethodKNN	(	DataSetInfo &	theData,
		const TString &	theWeightFile
	)

constructor from weight file

Definition at line 85 of file MethodKNN.cxx.

◆ ~MethodKNN()

TMVA::MethodKNN::~MethodKNN ( void )

virtual

destructor

Definition at line 106 of file MethodKNN.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

virtual

write weights to XML

Implements TMVA::MethodBase.

Definition at line 526 of file MethodKNN.cxx.

◆ CreateRanking()

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

virtual

no ranking available

Implements TMVA::MethodBase.

Definition at line 518 of file MethodKNN.cxx.

◆ DeclareCompatibilityOptions()

void TMVA::MethodKNN::DeclareCompatibilityOptions ( )

privatevirtual

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

Reimplemented from TMVA::MethodBase.

Definition at line 140 of file MethodKNN.cxx.

◆ DeclareOptions()

void TMVA::MethodKNN::DeclareOptions ( )

privatevirtual

MethodKNN options.

fnkNN = 20; // number of k-nearest neighbors
fBalanceDepth = 6; // number of binary tree levels used for tree balancing
fScaleFrac = 0.8; // fraction of events used to compute variable width
fSigmaFact = 1.0; // scale factor for Gaussian sigma
fKernel = use polynomial (1-x^3)^3 or Gaussian kernel
fTrim = false; // use equal number of signal and background events
fUseKernel = false; // use polynomial kernel weight function
fUseWeight = true; // count events using weights
fUseLDA = false

Implements TMVA::MethodBase.

Definition at line 124 of file MethodKNN.cxx.

◆ GausKernel()

Double_t TMVA::MethodKNN::GausKernel	(	const kNN::Event &	event_knn,
		const kNN::Event &	event,
		const std::vector< Double_t > &	svec
	)		const

private

Gaussian kernel.

Definition at line 831 of file MethodKNN.cxx.

◆ GetHelpMessage()

void TMVA::MethodKNN::GetHelpMessage ( ) const

protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 770 of file MethodKNN.cxx.

◆ getKernelRadius()

Double_t TMVA::MethodKNN::getKernelRadius ( const kNN::List & rlist ) const

private

Get polynomial kernel radius.

Definition at line 869 of file MethodKNN.cxx.

◆ getLDAValue()

Double_t TMVA::MethodKNN::getLDAValue	(	const kNN::List &	rlist,
		const kNN::Event &	event_knn
	)

private

Definition at line 945 of file MethodKNN.cxx.

◆ GetMvaValue()

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

virtual

Compute classifier response.

Implements TMVA::MethodBase.

Definition at line 296 of file MethodKNN.cxx.

◆ GetRegressionValues()

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

virtual

Return vector of averages for target values of k-nearest neighbors.

Use own copy of the regression vector, I do not like using a pointer to vector.

Reimplemented from TMVA::MethodBase.

Definition at line 435 of file MethodKNN.cxx.

◆ getRMS()

const std::vector< Double_t > TMVA::MethodKNN::getRMS	(	const kNN::List &	rlist,
		const kNN::Event &	event_knn
	)		const

private

Get polynomial kernel radius.

Definition at line 893 of file MethodKNN.cxx.

◆ HasAnalysisType()

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

virtual

FDA can handle classification with 2 classes and regression with one regression-target.

Implements TMVA::IMethod.

Definition at line 180 of file MethodKNN.cxx.

◆ Init()

void TMVA::MethodKNN::Init ( void )

privatevirtual

Initialization.

Implements TMVA::MethodBase.

Definition at line 190 of file MethodKNN.cxx.

◆ MakeClassSpecific()

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

protectedvirtual

write specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 758 of file MethodKNN.cxx.

◆ MakeKNN()

void TMVA::MethodKNN::MakeKNN ( void )

private

create kNN

Definition at line 203 of file MethodKNN.cxx.

◆ PolnKernel()

Double_t TMVA::MethodKNN::PolnKernel ( Double_t value ) const

private

polynomial kernel

Definition at line 815 of file MethodKNN.cxx.

◆ ProcessOptions()

void TMVA::MethodKNN::ProcessOptions ( )

privatevirtual

process the options specified by the user

Implements TMVA::MethodBase.

Definition at line 148 of file MethodKNN.cxx.

◆ ReadWeightsFromStream() [1/4]

virtual void TMVA::MethodBase::ReadWeightsFromStream ( std::istream & )

virtual

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/4]

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

virtual

read the weights

Implements TMVA::MethodBase.

Definition at line 591 of file MethodKNN.cxx.

◆ ReadWeightsFromStream() [3/4]

virtual void TMVA::MethodBase::ReadWeightsFromStream ( TFile & )

inlinevirtual

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromStream() [4/4]

void TMVA::MethodKNN::ReadWeightsFromStream ( TFile & rf )

virtual

read weights from ROOT file

Reimplemented from TMVA::MethodBase.

Definition at line 716 of file MethodKNN.cxx.

◆ ReadWeightsFromXML()

void TMVA::MethodKNN::ReadWeightsFromXML ( void * wghtnode )

virtual

Implements TMVA::MethodBase.

Definition at line 553 of file MethodKNN.cxx.

◆ Train()

void TMVA::MethodKNN::Train ( void )

virtual

kNN training

Implements TMVA::MethodBase.

Definition at line 234 of file MethodKNN.cxx.

◆ WriteWeightsToStream()

void TMVA::MethodKNN::WriteWeightsToStream ( TFile & rf ) const

save weights to ROOT file

Definition at line 680 of file MethodKNN.cxx.

Member Data Documentation

◆ fBalanceDepth

Int_t TMVA::MethodKNN::fBalanceDepth

private

Definition at line 124 of file MethodKNN.h.

◆ fEvent

kNN::EventVec TMVA::MethodKNN::fEvent

private

Definition at line 136 of file MethodKNN.h.

◆ fKernel

TString TMVA::MethodKNN::fKernel

private

Definition at line 129 of file MethodKNN.h.

◆ fLDA

LDA TMVA::MethodKNN::fLDA

private

(untouched) events used for learning

Definition at line 138 of file MethodKNN.h.

◆ fModule

kNN::ModulekNN* TMVA::MethodKNN::fModule

private

Definition at line 121 of file MethodKNN.h.

◆ fnkNN

Int_t TMVA::MethodKNN::fnkNN

private

module where all work is done

Definition at line 123 of file MethodKNN.h.

◆ fScaleFrac

Float_t TMVA::MethodKNN::fScaleFrac

private

Definition at line 126 of file MethodKNN.h.

◆ fSigmaFact

Float_t TMVA::MethodKNN::fSigmaFact

private

Definition at line 127 of file MethodKNN.h.

◆ fSumOfWeightsB

Double_t TMVA::MethodKNN::fSumOfWeightsB

private

Definition at line 119 of file MethodKNN.h.

◆ fSumOfWeightsS

Double_t TMVA::MethodKNN::fSumOfWeightsS

private

Definition at line 118 of file MethodKNN.h.

◆ fTreeOptDepth

Int_t TMVA::MethodKNN::fTreeOptDepth

private

Experimental feature for local knn analysis.

Definition at line 141 of file MethodKNN.h.

◆ fTrim

Bool_t TMVA::MethodKNN::fTrim

private

Definition at line 131 of file MethodKNN.h.

◆ fUseKernel

Bool_t TMVA::MethodKNN::fUseKernel

private

Definition at line 132 of file MethodKNN.h.

◆ fUseLDA

Bool_t TMVA::MethodKNN::fUseLDA

private

Definition at line 134 of file MethodKNN.h.

◆ fUseWeight

Bool_t TMVA::MethodKNN::fUseWeight

private

Definition at line 133 of file MethodKNN.h.

Libraries for TMVA::MethodKNN:

[legend]

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

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

Public Member Functions

Protected Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Constructor & Destructor Documentation

◆ MethodKNN() [1/2]

◆ MethodKNN() [2/2]

◆ ~MethodKNN()

Member Function Documentation

◆ AddWeightsXMLTo()

◆ CreateRanking()

◆ DeclareCompatibilityOptions()

◆ DeclareOptions()

◆ GausKernel()

◆ GetHelpMessage()

◆ getKernelRadius()

◆ getLDAValue()

◆ GetMvaValue()

◆ GetRegressionValues()

◆ getRMS()

◆ HasAnalysisType()

◆ Init()

◆ MakeClassSpecific()

◆ MakeKNN()

◆ PolnKernel()

◆ ProcessOptions()

◆ ReadWeightsFromStream() [1/4]

◆ ReadWeightsFromStream() [2/4]

◆ ReadWeightsFromStream() [3/4]

◆ ReadWeightsFromStream() [4/4]

◆ ReadWeightsFromXML()

◆ Train()

◆ WriteWeightsToStream()

Member Data Documentation

◆ fBalanceDepth

◆ fEvent

◆ fKernel

◆ fLDA

◆ fModule

◆ fnkNN

◆ fScaleFrac

◆ fSigmaFact

◆ fSumOfWeightsB

◆ fSumOfWeightsS

◆ fTreeOptDepth

◆ fTrim

◆ fUseKernel

◆ fUseLDA

◆ fUseWeight