Logo ROOT  
Reference Guide
 
Loading...
Searching...
No Matches
TMVA::MethodKNN Class Reference

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 RankingCreateRanking ()
 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)
 
TDirectoryBaseDir () 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)
 
DataSetData () const
 
DataSetInfoDataInfo () 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 EventGetEvent () const
 
const EventGetEvent (const TMVA::Event *ev) const
 
const EventGetEvent (Long64_t ievt) const
 
const EventGetEvent (Long64_t ievt, Types::ETreeType type) const
 
const std::vector< TMVA::Event * > & GetEventCollection (Types::ETreeType type)
 returns the event collection (i.e.
 
TFileGetFile () const
 
const TStringGetInputLabel (Int_t i) const
 
const char * GetInputTitle (Int_t i) const
 
const TStringGetInputVar (Int_t i) const
 
TMultiGraphGetInteractiveTrainingError ()
 
const TStringGetJobName () 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 TStringGetMethodName () 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_tGetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity)
 
virtual std::vector< Float_tGetMulticlassTrainingEfficiency (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
 temporary event when testing on a different DataSet than the own one
 
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 EventGetTestingEvent (Long64_t ievt) const
 
Double_t GetTestTime () const
 
const TStringGetTestvarName () const
 
virtual Double_t GetTrainingEfficiency (const TString &)
 
const EventGetTrainingEvent (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
 
TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true)
 
const TransformationHandlerGetTransformationHandler (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)
 
TDirectoryMethodBaseDir () const
 returns the ROOT directory where all instances of the corresponding MVA method are stored
 
virtual std::map< TString, Double_tOptimizeTuningParameters (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::OptionBaseDeclareOptionRef (T &ref, const TString &name, const TString &desc)
 
template<class T >
OptionBaseDeclareOptionRef (T &ref, const TString &name, const TString &desc="")
 
template<class T >
TMVA::OptionBaseDeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc)
 
template<class T >
OptionBaseDeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")
 
const char * GetConfigDescription () const
 
const char * GetConfigName () const
 
const TStringGetOptions () const
 
MsgLoggerLog () 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 TObjectClone (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.
 
TNamedoperator= (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 TObjectDrawClone (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 TObjectFindObject (const char *name) const
 Must be redefined in derived classes.
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes.
 
virtual Option_tGetDrawOption () 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_tGetOption () 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)
 
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 [].
 
voidoperator new (size_t sz)
 
voidoperator new (size_t sz, void *vp)
 
voidoperator new[] (size_t sz)
 
voidoperator new[] (size_t sz, void *vp)
 
TObjectoperator= (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
const TStringGetInternalVarName (Int_t ivar) const
 
virtual std::vector< Double_tGetMvaValues (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 TStringGetOriginalVarName (Int_t ivar) const
 
const TStringGetWeightFileDir () 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 TStringGetReferenceFile () 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_tgetRMS (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::ModulekNNfModule
 
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 Long_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
 
const EventfTmpEvent
 
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
 
IPythonInteractivefInteractive = nullptr
 
UInt_t fIPyCurrentIter = 0
 
UInt_t fIPyMaxIter = 0
 
std::vector< Float_t > * fMulticlassReturnVal
 
Int_t fNbins
 
Int_t fNbinsH
 
Int_t fNbinsMVAoutput
 
RankingfRanking
 
std::vector< Float_t > * fRegressionReturnVal
 
ResultsfResults
 
UInt_t fSignalClass
 
- Protected Attributes inherited from TMVA::Configurable
MsgLoggerfLogger
 
- 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 265 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:

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