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Reference Guide
TMVA::MethodFisher Class Reference

Fisher and Mahalanobis Discriminants (Linear Discriminant Analysis)

In the method of Fisher discriminants event selection is performed in a transformed variable space with zero linear correlations, by distinguishing the mean values of the signal and background distributions.

The linear discriminant analysis determines an axis in the (correlated) hyperspace of the input variables such that, when projecting the output classes (signal and background) upon this axis, they are pushed as far as possible away from each other, while events of a same class are confined in a close vicinity. The linearity property of this method is reflected in the metric with which "far apart" and "close vicinity" are determined: the covariance matrix of the discriminant variable space.

The classification of the events in signal and background classes relies on the following characteristics (only): overall sample means, \( x_i \), for each input variable, \( i \), class-specific sample means, \( x_{S(B),i}\), and total covariance matrix \( T_{ij} \). The covariance matrix can be decomposed into the sum of a within ( \( W_{ij} \)) and a between-class ( \( B_{ij} \)) class matrix. They describe the dispersion of events relative to the means of their own class (within-class matrix), and relative to the overall sample means (between-class matrix). The Fisher coefficients, \( F_i \), are then given by

\[ F_i = \frac{\sqrt{N_s N_b}}{N_s + N_b} \sum_{j=1}^{N_{SB}} W_{ij}^{-1} (\bar{X}_{Sj} - \bar{X}_{Bj}) \]

where in TMVA is set \( N_S = N_B \), so that the factor in front of the sum simplifies to \( \frac{1}{2}\). The Fisher discriminant then reads

\[ X_{Fi} = F_0 + \sum_{i=1}^{N_{SB}} F_i X_i \]

The offset \( F_0 \) centers the sample mean of \( x_{Fi} \) at zero. Instead of using the within-class matrix, the Mahalanobis variant determines the Fisher coefficients as follows:

\[ F_i = \frac{\sqrt{N_s N_b}}{N_s + N_b} \sum_{j=1}^{N_{SB}} (W + B)_{ij}^{-1} (\bar{X}_{Sj} - \bar{X}_{Bj}) \]

with resulting \( x_{Ma} \) that are very similar to the \( x_{Fi} \).

TMVA provides two outputs for the ranking of the input variables:

  • Fisher test: the Fisher analysis aims at simultaneously maximising the between-class separation, while minimising the within-class dispersion. A useful measure of the discrimination power of a variable is hence given by the diagonal quantity: \( \frac{B_{ii}}{W_{ii}} \) .
  • Discrimination power: the value of the Fisher coefficient is a measure of the discriminating power of a variable. The discrimination power of set of input variables can therefore be measured by the scalar

\[ \lambda = \frac{\sqrt{N_s N_b}}{N_s + N_b} \sum_{j=1}^{N_{SB}} F_i (\bar{X}_{Sj} - \bar{X}_{Bj}) \]

The corresponding numbers are printed on standard output.

Definition at line 54 of file MethodFisher.h.

Public Types

enum  EFisherMethod { kFisher, kMahalanobis }
 
- 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)
}
 

Public Member Functions

 MethodFisher (const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="Fisher")
 standard constructor for the "Fisher" More...
 
 MethodFisher (DataSetInfo &dsi, const TString &theWeightFile)
 constructor from weight file More...
 
virtual ~MethodFisher (void)
 destructor More...
 
void AddWeightsXMLTo (void *parent) const
 create XML description of Fisher classifier More...
 
const RankingCreateRanking ()
 computes ranking of input variables More...
 
EFisherMethod GetFisherMethod (void)
 
Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)
 returns the Fisher value (no fixed range) More...
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
 Fisher can only handle classification with 2 classes. More...
 
void PrintCoefficients (void)
 display Fisher coefficients and discriminating power for each variable check maximum length of variable name More...
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
virtual void ReadWeightsFromStream (TFile &)
 
void ReadWeightsFromStream (std::istream &i)
 read Fisher coefficients from weight file More...
 
void ReadWeightsFromXML (void *wghtnode)
 read Fisher coefficients from xml weight file More...
 
void Train (void)
 computation of Fisher coefficients by series of matrix operations More...
 
- Public Member Functions inherited from TMVA::MethodBase
 MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="")
 standard constructor More...
 
 MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile)
 constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles More...
 
virtual ~MethodBase ()
 destructor More...
 
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 More...
 
virtual void CheckSetup ()
 check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More...
 
DataSetData () const
 
DataSetInfoDataInfo () const
 
virtual void DeclareCompatibilityOptions ()
 options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand More...
 
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. More...
 
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. More...
 
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 More...
 
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. More...
 
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 More...
 
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 More...
 
const TString GetProbaName () const
 
virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
 compute rarity:

\[ R(x) = \int_{[-\infty..x]} { PDF(x') dx' } \]

where PDF(x) is the PDF of the classifier's signal or background distribution More...

 
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)
 
virtual const std::vector< Float_t > & GetRegressionValues ()
 
Double_t GetRMS (Int_t ivar) const
 
virtual Double_t GetROCIntegral (TH1D *histS, TH1D *histB) const
 calculate the area (integral) under the ROC curve as a overall quality measure of the classification More...
 
virtual Double_t GetROCIntegral (PDF *pdfS=0, PDF *pdfB=0) const
 calculate the area (integral) under the ROC curve as a overall quality measure of the classification More...
 
virtual Double_t GetSeparation (TH1 *, TH1 *) const
 compute "separation" defined as

\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]

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

\[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \]

More...
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
virtual Double_t GetSignificance () const
 compute significance of mean difference

\[ significance = \frac{|<S> - <B>|}{\sqrt{RMS_{S2} + RMS_{B2}}} \]

More...
 
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 More...
 
UInt_t GetTrainingTMVAVersionCode () const
 
TString GetTrainingTMVAVersionString () const
 calculates the TMVA version string from the training version code on the fly More...
 
Double_t GetTrainTime () const
 
TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true)
 
const TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const
 
TString GetWeightFileName () const
 retrieve weight file name More...
 
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 More...
 
virtual Bool_t IsSignalLike (Double_t mvaVal)
 uses a pre-set cut on the MVA output (SetSignalReferenceCut and SetSignalReferenceCutOrientation) for a quick determination if an event with this mva output value would be selected as signal or background More...
 
Bool_t IsSilentFile () const
 
virtual void MakeClass (const TString &classFileName=TString("")) const
 create reader class for method (classification only at present) More...
 
TDirectoryMethodBaseDir () const
 returns the ROOT directory where all instances of the corresponding MVA method are stored More...
 
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. More...
 
void PrintHelpMessage () const
 prints out method-specific help method More...
 
void ProcessSetup ()
 process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More...
 
void ReadStateFromFile ()
 Function to write options and weights to file. More...
 
void ReadStateFromStream (std::istream &tf)
 read the header from the weight files of the different MVA methods More...
 
void ReadStateFromStream (TFile &rf)
 write reference MVA distributions (and other information) to a ROOT type weight file More...
 
void ReadStateFromXMLString (const char *xmlstr)
 for reading from memory More...
 
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 . More...
 
void SetupMethod ()
 setup of methods More...
 
virtual void TestClassification ()
 initialization More...
 
virtual void TestMulticlass ()
 test multiclass classification More...
 
virtual void TestRegression (Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type)
 calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample More...
 
bool TrainingEnded ()
 
void TrainMethod ()
 
virtual void WriteEvaluationHistosToFile (Types::ETreeType treetype)
 writes all MVA evaluation histograms to file More...
 
virtual void WriteMonitoringHistosToFile () const
 write special monitoring histograms to file dummy implementation here --------------— More...
 
void WriteStateToFile () const
 write options and weights to file note that each one text file for the main configuration information and one ROOT file for ROOT objects are created More...
 
- Public Member Functions inherited from TMVA::IMethod
 IMethod ()
 
virtual ~IMethod ()
 
- Public Member Functions inherited from TMVA::Configurable
 Configurable (const TString &theOption="")
 constructor More...
 
virtual ~Configurable ()
 default destructor More...
 
void AddOptionsXMLTo (void *parent) const
 write options to XML file More...
 
template<class T >
void AddPreDefVal (const T &)
 
template<class T >
void AddPreDefVal (const TString &optname, const T &)
 
void CheckForUnusedOptions () const
 checks for unused options in option string More...
 
template<class T >
OptionBaseDeclareOptionRef (T &ref, const TString &name, const TString &desc="")
 
template<class T >
OptionBaseDeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="")
 
template<class T >
TMVA::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)
 
const char * GetConfigDescription () const
 
const char * GetConfigName () const
 
const TStringGetOptions () const
 
MsgLoggerLog () const
 
virtual void ParseOptions ()
 options parser More...
 
void PrintOptions () const
 prints out the options set in the options string and the defaults More...
 
void ReadOptionsFromStream (std::istream &istr)
 read option back from the weight file More...
 
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 More...
 
- Public Member Functions inherited from TNamed
 TNamed ()
 
 TNamed (const char *name, const char *title)
 
 TNamed (const TString &name, const TString &title)
 
 TNamed (const TNamed &named)
 TNamed copy ctor. More...
 
virtual ~TNamed ()
 TNamed destructor. More...
 
virtual void Clear (Option_t *option="")
 Set name and title to empty strings (""). More...
 
virtual TObjectClone (const char *newname="") const
 Make a clone of an object using the Streamer facility. More...
 
virtual Int_t Compare (const TObject *obj) const
 Compare two TNamed objects. More...
 
virtual void Copy (TObject &named) const
 Copy this to obj. More...
 
virtual void FillBuffer (char *&buffer)
 Encode TNamed into output buffer. More...
 
virtual const char * GetTitle () const
 Returns title of object. More...
 
virtual ULong_t Hash () const
 Return hash value for this object. More...
 
virtual Bool_t IsSortable () const
 
virtual void ls (Option_t *option="") const
 List TNamed name and title. More...
 
TNamedoperator= (const TNamed &rhs)
 TNamed assignment operator. More...
 
virtual void Print (Option_t *option="") const
 Print TNamed name and title. More...
 
virtual void SetName (const char *name)
 Set the name of the TNamed. More...
 
virtual void SetNameTitle (const char *name, const char *title)
 Set all the TNamed parameters (name and title). More...
 
virtual void SetTitle (const char *title="")
 Set the title of the TNamed. More...
 
virtual Int_t Sizeof () const
 Return size of the TNamed part of the TObject. More...
 
- Public Member Functions inherited from TObject
 TObject ()
 TObject constructor. More...
 
 TObject (const TObject &object)
 TObject copy ctor. More...
 
virtual ~TObject ()
 TObject destructor. More...
 
void AbstractMethod (const char *method) const
 Use this method to implement an "abstract" method that you don't want to leave purely abstract. More...
 
virtual void AppendPad (Option_t *option="")
 Append graphics object to current pad. More...
 
virtual void Browse (TBrowser *b)
 Browse object. May be overridden for another default action. More...
 
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. More...
 
virtual const char * ClassName () const
 Returns name of class to which the object belongs. More...
 
virtual void Delete (Option_t *option="")
 Delete this object. More...
 
virtual Int_t DistancetoPrimitive (Int_t px, Int_t py)
 Computes distance from point (px,py) to the object. More...
 
virtual void Draw (Option_t *option="")
 Default Draw method for all objects. More...
 
virtual void DrawClass () const
 Draw class inheritance tree of the class to which this object belongs. More...
 
virtual TObjectDrawClone (Option_t *option="") const
 Draw a clone of this object in the current selected pad for instance with: gROOT->SetSelectedPad(gPad). More...
 
virtual void Dump () const
 Dump contents of object on stdout. More...
 
virtual void Error (const char *method, const char *msgfmt,...) const
 Issue error message. More...
 
virtual void Execute (const char *method, const char *params, Int_t *error=0)
 Execute method on this object with the given parameter string, e.g. More...
 
virtual void Execute (TMethod *method, TObjArray *params, Int_t *error=0)
 Execute method on this object with parameters stored in the TObjArray. More...
 
virtual void ExecuteEvent (Int_t event, Int_t px, Int_t py)
 Execute action corresponding to an event at (px,py). More...
 
virtual void Fatal (const char *method, const char *msgfmt,...) const
 Issue fatal error message. More...
 
virtual TObjectFindObject (const char *name) const
 Must be redefined in derived classes. More...
 
virtual TObjectFindObject (const TObject *obj) const
 Must be redefined in derived classes. More...
 
virtual Option_tGetDrawOption () const
 Get option used by the graphics system to draw this object. More...
 
virtual const char * GetIconName () const
 Returns mime type name of object. More...
 
virtual char * GetObjectInfo (Int_t px, Int_t py) const
 Returns string containing info about the object at position (px,py). More...
 
virtual Option_tGetOption () const
 
virtual UInt_t GetUniqueID () const
 Return the unique object id. More...
 
virtual Bool_t HandleTimer (TTimer *timer)
 Execute action in response of a timer timing out. More...
 
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. More...
 
virtual void Info (const char *method, const char *msgfmt,...) const
 Issue info message. More...
 
virtual Bool_t InheritsFrom (const char *classname) const
 Returns kTRUE if object inherits from class "classname". More...
 
virtual Bool_t InheritsFrom (const TClass *cl) const
 Returns kTRUE if object inherits from TClass cl. More...
 
virtual void Inspect () const
 Dump contents of this object in a graphics canvas. More...
 
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). More...
 
virtual Bool_t IsFolder () const
 Returns kTRUE in case object contains browsable objects (like containers or lists of other objects). More...
 
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). More...
 
virtual Bool_t Notify ()
 This method must be overridden to handle object notification. More...
 
void Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
 Use this method to declare a method obsolete. More...
 
void operator delete (void *ptr)
 Operator delete. More...
 
void operator delete[] (void *ptr)
 Operator delete []. More...
 
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. More...
 
virtual void Paint (Option_t *option="")
 This method must be overridden if a class wants to paint itself. More...
 
virtual void Pop ()
 Pop on object drawn in a pad to the top of the display list. More...
 
virtual Int_t Read (const char *name)
 Read contents of object with specified name from the current directory. More...
 
virtual void RecursiveRemove (TObject *obj)
 Recursively remove this object from a list. More...
 
void ResetBit (UInt_t f)
 
virtual void SaveAs (const char *filename="", Option_t *option="") const
 Save this object in the file specified by filename. More...
 
virtual void SavePrimitive (std::ostream &out, Option_t *option="")
 Save a primitive as a C++ statement(s) on output stream "out". More...
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f. More...
 
void SetBit (UInt_t f)
 
virtual void SetDrawOption (Option_t *option="")
 Set drawing option for object. More...
 
virtual void SetUniqueID (UInt_t uid)
 Set the unique object id. More...
 
virtual void SysError (const char *method, const char *msgfmt,...) const
 Issue system error message. More...
 
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. More...
 
virtual void Warning (const char *method, const char *msgfmt,...) const
 Issue warning message. More...
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0)
 Write this object to the current directory. More...
 
virtual Int_t Write (const char *name=0, Int_t option=0, Int_t bufsize=0) const
 Write this object to the current directory. More...
 

Protected Member Functions

void GetHelpMessage () const
 get help message text More...
 
void MakeClassSpecific (std::ostream &, const TString &) const
 write Fisher-specific classifier response More...
 
- 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 More...
 
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)
 
virtual void ReadWeightsFromStream (TFile &)
 
void SetNormalised (Bool_t norm)
 
void SetWeightFileDir (TString fileDir)
 set directory of weight file More...
 
void SetWeightFileName (TString)
 set the weight file name (depreciated) More...
 
void Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &)
 calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE More...
 
Bool_t TxtWeightsOnly () const
 
Bool_t 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 More...
 
void WriteOptionsReferenceToFile ()
 write complete options to output stream More...
 
- 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). More...
 
void MakeZombie ()
 

Private Member Functions

void DeclareOptions ()
 MethodFisher options: format and syntax of option string: "type" where type is "Fisher" or "Mahalanobis". More...
 
void GetCov_BetweenClass (void)
 the matrix of covariance 'between class' reflects the dispersion of the events of a class relative to the global center of gravity of all the class hence the separation between classes More...
 
void GetCov_Full (void)
 compute full covariance matrix from sum of within and between matrices More...
 
void GetCov_WithinClass (void)
 the matrix of covariance 'within class' reflects the dispersion of the events relative to the center of gravity of their own class More...
 
void GetDiscrimPower (void)
 computation of discrimination power indicator for each variable small values of "fWith" indicates little compactness of sig & of backgd big values of "fBetw" indicates large separation between sig & backgd More...
 
void GetFisherCoeff (void)
 Fisher = Sum { [coeff]*[variables] }. More...
 
void GetMean (void)
 compute mean values of variables in each sample, and the overall means More...
 
void Init (void)
 default initialization called by all constructors More...
 
void InitMatrices (void)
 initialization method; creates global matrices and vectors More...
 
void ProcessOptions ()
 process user options More...
 

Private Attributes

TMatrixDfBetw
 
TMatrixDfCov
 
std::vector< Double_t > * fDiscrimPow
 
Double_t fF0
 
std::vector< Double_t > * fFisherCoeff
 
EFisherMethod fFisherMethod
 
TMatrixDfMeanMatx
 
Double_t fSumOfWeightsB
 
Double_t fSumOfWeightsS
 
TString fTheMethod
 
TMatrixDfWith
 

Additional Inherited Members

- Static Public Member Functions inherited from TObject
static Long_t GetDtorOnly ()
 Return destructor only flag. More...
 
static Bool_t GetObjectStat ()
 Get status of object stat flag. More...
 
static void SetDtorOnly (void *obj)
 Set destructor only flag. More...
 
static void SetObjectStat (Bool_t stat)
 Turn on/off tracking of objects in the TObjectTable. More...
 
- Public Attributes inherited from TMVA::MethodBase
Bool_t fSetupCompleted
 
const EventfTmpEvent
 
TrainingHistory fTrainHistory
 
- 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/MethodFisher.h>

Inheritance diagram for TMVA::MethodFisher:
[legend]

Member Enumeration Documentation

◆ EFisherMethod

enum TMVA::MethodFisher::EFisherMethod
Enumerator
kFisher 
kMahalanobis 

Definition at line 86 of file MethodFisher.h.

Constructor & Destructor Documentation

◆ MethodFisher() [1/2]

TMVA::MethodFisher::MethodFisher ( const TString jobName,
const TString methodTitle,
DataSetInfo dsi,
const TString theOption = "Fisher" 
)

standard constructor for the "Fisher"

Definition at line 135 of file MethodFisher.cxx.

◆ MethodFisher() [2/2]

TMVA::MethodFisher::MethodFisher ( DataSetInfo dsi,
const TString theWeightFile 
)

constructor from weight file

Definition at line 157 of file MethodFisher.cxx.

◆ ~MethodFisher()

TMVA::MethodFisher::~MethodFisher ( void  )
virtual

destructor

Definition at line 216 of file MethodFisher.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

void TMVA::MethodFisher::AddWeightsXMLTo ( void parent) const
virtual

create XML description of Fisher classifier

Implements TMVA::MethodBase.

Definition at line 618 of file MethodFisher.cxx.

◆ CreateRanking()

const TMVA::Ranking * TMVA::MethodFisher::CreateRanking ( )
virtual

computes ranking of input variables

Implements TMVA::MethodBase.

Definition at line 541 of file MethodFisher.cxx.

◆ DeclareOptions()

void TMVA::MethodFisher::DeclareOptions ( )
privatevirtual

MethodFisher options: format and syntax of option string: "type" where type is "Fisher" or "Mahalanobis".

Implements TMVA::MethodBase.

Definition at line 194 of file MethodFisher.cxx.

◆ GetCov_BetweenClass()

void TMVA::MethodFisher::GetCov_BetweenClass ( void  )
private

the matrix of covariance 'between class' reflects the dispersion of the events of a class relative to the global center of gravity of all the class hence the separation between classes

Definition at line 417 of file MethodFisher.cxx.

◆ GetCov_Full()

void TMVA::MethodFisher::GetCov_Full ( void  )
private

compute full covariance matrix from sum of within and between matrices

Definition at line 440 of file MethodFisher.cxx.

◆ GetCov_WithinClass()

void TMVA::MethodFisher::GetCov_WithinClass ( void  )
private

the matrix of covariance 'within class' reflects the dispersion of the events relative to the center of gravity of their own class

Definition at line 351 of file MethodFisher.cxx.

◆ GetDiscrimPower()

void TMVA::MethodFisher::GetDiscrimPower ( void  )
private

computation of discrimination power indicator for each variable small values of "fWith" indicates little compactness of sig & of backgd big values of "fBetw" indicates large separation between sig & backgd

we want signal & backgd classes as compact and separated as possible the discriminating power is then defined as the ration "fBetw/fWith"

Definition at line 528 of file MethodFisher.cxx.

◆ GetFisherCoeff()

void TMVA::MethodFisher::GetFisherCoeff ( void  )
private

Fisher = Sum { [coeff]*[variables] }.

let Xs be the array of the mean values of variables for signal evts let Xb be the array of the mean values of variables for backgd evts let InvWith be the inverse matrix of the 'within class' correlation matrix

then the array of Fisher coefficients is [coeff] =sqrt(fNsig*fNbgd)/fNevt*transpose{Xs-Xb}*InvWith

Definition at line 457 of file MethodFisher.cxx.

◆ GetFisherMethod()

EFisherMethod TMVA::MethodFisher::GetFisherMethod ( void  )
inline

Definition at line 87 of file MethodFisher.h.

◆ GetHelpMessage()

void TMVA::MethodFisher::GetHelpMessage ( ) const
protectedvirtual

get help message text

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

Implements TMVA::IMethod.

Definition at line 702 of file MethodFisher.cxx.

◆ GetMean()

void TMVA::MethodFisher::GetMean ( void  )
private

compute mean values of variables in each sample, and the overall means

Definition at line 302 of file MethodFisher.cxx.

◆ GetMvaValue()

Double_t TMVA::MethodFisher::GetMvaValue ( Double_t err = 0,
Double_t errUpper = 0 
)
virtual

returns the Fisher value (no fixed range)

Implements TMVA::MethodBase.

Definition at line 268 of file MethodFisher.cxx.

◆ HasAnalysisType()

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

Fisher can only handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 228 of file MethodFisher.cxx.

◆ Init()

void TMVA::MethodFisher::Init ( void  )
privatevirtual

default initialization called by all constructors

Implements TMVA::MethodBase.

Definition at line 177 of file MethodFisher.cxx.

◆ InitMatrices()

void TMVA::MethodFisher::InitMatrices ( void  )
private

initialization method; creates global matrices and vectors

Definition at line 285 of file MethodFisher.cxx.

◆ MakeClassSpecific()

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

write Fisher-specific classifier response

Reimplemented from TMVA::MethodBase.

Definition at line 655 of file MethodFisher.cxx.

◆ PrintCoefficients()

void TMVA::MethodFisher::PrintCoefficients ( void  )

display Fisher coefficients and discriminating power for each variable check maximum length of variable name

Definition at line 557 of file MethodFisher.cxx.

◆ ProcessOptions()

void TMVA::MethodFisher::ProcessOptions ( )
privatevirtual

process user options

Implements TMVA::MethodBase.

Definition at line 204 of file MethodFisher.cxx.

◆ ReadWeightsFromStream() [1/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream
inline

Definition at line 265 of file MethodBase.h.

◆ ReadWeightsFromStream() [2/3]

virtual void TMVA::MethodBase::ReadWeightsFromStream

◆ ReadWeightsFromStream() [3/3]

void TMVA::MethodFisher::ReadWeightsFromStream ( std::istream &  i)
virtual

read Fisher coefficients from weight file

Implements TMVA::MethodBase.

Definition at line 609 of file MethodFisher.cxx.

◆ ReadWeightsFromXML()

void TMVA::MethodFisher::ReadWeightsFromXML ( void wghtnode)
virtual

read Fisher coefficients from xml weight file

Implements TMVA::MethodBase.

Definition at line 635 of file MethodFisher.cxx.

◆ Train()

void TMVA::MethodFisher::Train ( void  )
virtual

computation of Fisher coefficients by series of matrix operations

Implements TMVA::MethodBase.

Definition at line 237 of file MethodFisher.cxx.

Member Data Documentation

◆ fBetw

TMatrixD* TMVA::MethodFisher::fBetw
private

Definition at line 139 of file MethodFisher.h.

◆ fCov

TMatrixD* TMVA::MethodFisher::fCov
private

Definition at line 141 of file MethodFisher.h.

◆ fDiscrimPow

std::vector<Double_t>* TMVA::MethodFisher::fDiscrimPow
private

Definition at line 147 of file MethodFisher.h.

◆ fF0

Double_t TMVA::MethodFisher::fF0
private

Definition at line 149 of file MethodFisher.h.

◆ fFisherCoeff

std::vector<Double_t>* TMVA::MethodFisher::fFisherCoeff
private

Definition at line 148 of file MethodFisher.h.

◆ fFisherMethod

EFisherMethod TMVA::MethodFisher::fFisherMethod
private

Definition at line 136 of file MethodFisher.h.

◆ fMeanMatx

TMatrixD* TMVA::MethodFisher::fMeanMatx
private

Definition at line 132 of file MethodFisher.h.

◆ fSumOfWeightsB

Double_t TMVA::MethodFisher::fSumOfWeightsB
private

Definition at line 145 of file MethodFisher.h.

◆ fSumOfWeightsS

Double_t TMVA::MethodFisher::fSumOfWeightsS
private

Definition at line 144 of file MethodFisher.h.

◆ fTheMethod

TString TMVA::MethodFisher::fTheMethod
private

Definition at line 135 of file MethodFisher.h.

◆ fWith

TMatrixD* TMVA::MethodFisher::fWith
private

Definition at line 140 of file MethodFisher.h.

Libraries for TMVA::MethodFisher:
[legend]

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