library: libTMVA
#include "MethodPDERS.h"

TMVA::MethodPDERS

class description - header file - source file
viewCVS header - viewCVS source

class TMVA::MethodPDERS: public TMVA::MethodBase

Inheritance Inherited Members Includes Libraries
Class Charts

Function Members (Methods)

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public:
virtual~MethodPDERS()
voidTObject::AbstractMethod(const char* method) const
virtual voidTObject::AppendPad(Option_t* option = "")
virtual voidTObject::Browse(TBrowser* b)
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual voidTObject::Clear(Option_t* = "")
virtual TObject*TObject::Clone(const char* newname = "") const
virtual Int_tTObject::Compare(const TObject* obj) const
virtual voidTObject::Copy(TObject& object) const
TMVA::DataSet&TMVA::MethodBase::Data() const
virtual voidTObject::Delete(Option_t* option = "")
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() const
virtual TObject*TObject::DrawClone(Option_t* option = "") const
virtual voidTObject::Dump() const
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual voidTObject::Execute(const char* method, const char* params, Int_t* error = 0)
virtual voidTObject::Execute(TMethod* method, TObjArray* params, Int_t* error = 0)
virtual voidTObject::ExecuteEvent(Int_t event, Int_t px, Int_t py)
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
Double_tTMVA::MethodBase::GetEffForRoot(Double_t)
virtual Double_tTMVA::MethodBase::GetEfficiency(TString, TTree*)
Double_tTMVA::MethodBase::GetEventVal(Int_t ivar) const
Double_tTMVA::MethodBase::GetEventValNormalized(Int_t ivar) const
Double_tTMVA::MethodBase::GetEventWeight() const
virtual const char*TObject::GetIconName() const
const TString&TMVA::MethodBase::GetInputExp(int i) const
const TString&TMVA::MethodBase::GetInputVar(int i) const
virtual const TString&TMVA::MethodBase::GetJobName() const
virtual const TString&TMVA::MethodBase::GetMethodName() const
virtual const TString&TMVA::MethodBase::GetMethodTitle() const
virtual const TMVA::Types::EMVATMVA::MethodBase::GetMethodType() const
virtual Double_tTMVA::MethodBase::GetmuTransform(TTree*)
virtual Double_tGetMvaValue()
virtual const char*TMVA::MethodBase::GetName() const
Int_tTMVA::MethodBase::GetNvar() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Double_tTMVA::MethodBase::GetOptimalSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
virtual Option_t*TObject::GetOption() const
TStringTMVA::MethodBase::GetOptions() const
virtual TMVA::Types::EPreprocessingMethodTMVA::MethodBase::GetPreprocessingMethod() const
virtual Double_tTMVA::MethodBase::GetSeparation()
virtual Double_tTMVA::MethodBase::GetSignificance()
TTree*TMVA::MethodBase::GetTestTree() const
static TMVA::MethodBase*TMVA::MethodBase::GetThisBase()
virtual const char*TObject::GetTitle() const
virtual Double_tTMVA::MethodBase::GetTrainingEfficiency(TString)
TTree*TMVA::MethodBase::GetTrainingTree() const
virtual UInt_tTObject::GetUniqueID() const
Double_tGetVolumeContentForRoot(Double_t)
virtual TStringTMVA::MethodBase::GetWeightFileDir() const
virtual TStringTMVA::MethodBase::GetWeightFileExtension() const
TStringTMVA::MethodBase::GetWeightFileName() const
TMVA::MethodBase::EWeightFileTypeTMVA::MethodBase::GetWeightFileType() const
Double_tTMVA::MethodBase::GetXmax(Int_t ivar, TMVA::Types::EPreprocessingMethod corr = Types::kNone) const
Double_tTMVA::MethodBase::GetXmax(const TString& var, TMVA::Types::EPreprocessingMethod corr = Types::kNone) const
Double_tTMVA::MethodBase::GetXmin(Int_t ivar, TMVA::Types::EPreprocessingMethod corr = Types::kNone) const
Double_tTMVA::MethodBase::GetXmin(const TString& var, TMVA::Types::EPreprocessingMethod corr = Types::kNone) const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual ULong_tTObject::Hash() const
Bool_tTMVA::MethodBase::HasTrainingTree() const
static Double_tTMVA::MethodBase::IGetEffForRoot(Double_t)
static Double_tIGetVolumeContentForRoot(Double_t)
virtual voidTObject::Info(const char* method, const char* msgfmt) const
virtual Bool_tTObject::InheritsFrom(const char* classname) const
virtual Bool_tTObject::InheritsFrom(const TClass* cl) const
virtual voidTObject::Inspect() const
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
virtual Bool_tTMVA::MethodBase::IsOK() const
Bool_tTObject::IsOnHeap() const
virtual Bool_tTMVA::MethodBase::IsSignalLike()
virtual Bool_tTObject::IsSortable() const
Bool_tTObject::IsZombie() const
virtual voidTObject::ls(Option_t* option = "") const
voidTObject::MayNotUse(const char* method) const
TMVA::MethodPDERSMethodPDERS(TMVA::DataSet& theData, TString theWeightFile, TDirectory* theTargetDir = NULL)
TMVA::MethodPDERSMethodPDERS(TString jobName, TString methodTitle, TMVA::DataSet& theData, TString theOption, TDirectory* theTargetDir = 0)
Double_tTMVA::MethodBase::Norm(Int_t ivar, Double_t x) const
Double_tTMVA::MethodBase::Norm(TString var, Double_t x) const
virtual Bool_tTObject::Notify()
static voidTObject::operator delete(void* ptr)
static voidTObject::operator delete(void* ptr, void* vp)
static voidTObject::operator delete[](void* ptr)
static voidTObject::operator delete[](void* ptr, void* vp)
void*TObject::operator new(size_t sz)
void*TObject::operator new(size_t sz, void* vp)
void*TObject::operator new[](size_t sz)
void*TObject::operator new[](size_t sz, void* vp)
TMVA::IMethod&TMVA::IMethod::operator=(const TMVA::IMethod&)
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTObject::Pop()
virtual voidTMVA::MethodBase::PrepareEvaluationTree(TTree* theTestTree)
virtual voidTObject::Print(Option_t* option = "") const
virtual Int_tTObject::Read(const char* name)
virtual voidTMVA::MethodBase::ReadStateFromFile()
virtual voidTMVA::MethodBase::ReadStateFromStream(istream& i)
virtual Bool_tTMVA::MethodBase::ReadTestEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType)
Bool_tTMVA::MethodBase::ReadTrainingEvent(UInt_t ievt, TMVA::Types::ESBType type = Types::kMaxSBType)
virtual voidReadWeightsFromStream(istream& istr)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTObject::ResetBit(UInt_t f)
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") const
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
virtual voidTObject::SetDrawOption(Option_t* option = "")
static voidTObject::SetDtorOnly(void* obj)
virtual voidTMVA::MethodBase::SetJobName(TString jobName)
voidTMVA::MethodBase::SetMethodName(TString methodName)
voidTMVA::MethodBase::SetMethodTitle(TString methodTitle)
voidTMVA::MethodBase::SetMethodType(TMVA::Types::EMVA methodType)
voidTMVA::MethodBase::SetNvar(Int_t n)
static voidTObject::SetObjectStat(Bool_t stat)
voidTMVA::MethodBase::SetPreprocessingMethod(TMVA::Types::EPreprocessingMethod m)
virtual voidTObject::SetUniqueID(UInt_t uid)
voidTMVA::MethodBase::SetVerbose(Bool_t v = kTRUE)
virtual voidTMVA::MethodBase::SetWeightFileDir(TString fileDir)
virtual voidTMVA::MethodBase::SetWeightFileExtension(TString fileExtension)
voidTMVA::MethodBase::SetWeightFileName(TString)
voidTMVA::MethodBase::SetWeightFileType(TMVA::MethodBase::EWeightFileType w)
voidTMVA::MethodBase::SetXmax(Int_t ivar, Double_t x, TMVA::Types::EPreprocessingMethod corr = Types::kNone)
voidTMVA::MethodBase::SetXmax(const TString& var, Double_t x, TMVA::Types::EPreprocessingMethod corr = Types::kNone)
voidTMVA::MethodBase::SetXmin(Int_t ivar, Double_t x, TMVA::Types::EPreprocessingMethod corr = Types::kNone)
voidTMVA::MethodBase::SetXmin(const TString& var, Double_t x, TMVA::Types::EPreprocessingMethod corr = Types::kNone)
virtual voidShowMembers(TMemberInspector& insp, char* parent)
virtual voidStreamer(TBuffer& b)
voidStreamerNVirtual(TBuffer& b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
virtual voidTMVA::MethodBase::Test(TTree* theTestTree = 0)
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTMVA::MethodBase::TestInit(TTree* theTestTree = 0)
static TMVA::MethodPDERS*ThisPDERS()
virtual voidTrain()
voidTMVA::MethodBase::TrainMethod()
virtual voidTObject::UseCurrentStyle()
Bool_tTMVA::MethodBase::Verbose() const
virtual voidTObject::Warning(const char* method, const char* msgfmt) const
virtual Int_tTObject::Write(const char* name = "0", Int_t option = 0, Int_t bufsize = 0)
virtual Int_tTObject::Write(const char* name = "0", Int_t option = 0, Int_t bufsize = 0) const
virtual voidTMVA::MethodBase::WriteEvaluationHistosToFile(TDirectory* targetDir)
virtual voidTMVA::MethodBase::WriteMonitoringHistosToFile() const
voidTMVA::MethodBase::WriteStateToFile() const
virtual voidTMVA::MethodBase::WriteStateToStream(ostream& o) const
virtual voidWriteWeightsToStream(ostream& o) const
protected:
Double_tApplyKernelFunction(Double_t normalized_distance)
TDirectory*TMVA::MethodBase::BaseDir() const
Bool_tTMVA::MethodBase::CheckSanity(TTree* theTree = 0)
virtual const TMVA::Ranking*CreateRanking()
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTMVA::MethodBase::EnableLooseOptions(Bool_t b = kTRUE)
TMVA::BinarySearchTree*GetBinaryTreeBkg() const
TMVA::BinarySearchTree*GetBinaryTreeSig() const
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::GetCutOrientation() const
Double_tGetNormalizedDistance(const TMVA::Event& base_event, const TMVA::Event& sample_event, Double_t* dim_normalization)
TMVA::Types::ESBTypeTMVA::MethodBase::GetPreprocessingType() const
Double_tTMVA::MethodBase::GetSignalReferenceCut() const
const TString&TMVA::MethodBase::GetTestvarName() const
const TString&TMVA::MethodBase::GetTestvarPrefix() const
Double_tKernelEstimate(const TMVA::Event&, vector<TMVA::Event*,allocator<TMVA::Event*> >&, TMVA::Volume&)
Double_tKernelNormalization(Double_t pdf)
Double_tLanczosFilter(Int_t level, Double_t x)
const TList&TMVA::MethodBase::ListOfOptions() const
TDirectory*TMVA::MethodBase::LocalTDir() const
voidTObject::MakeZombie()
Double_tNormSinc(Double_t x)
voidTMVA::MethodBase::ParseOptions(Bool_t verbose = kTRUE)
voidTMVA::MethodBase::PrintOptions() const
voidTMVA::MethodBase::ReadOptionsFromStream(istream& istr)
voidTMVA::MethodBase::ResetThisBase()
voidTMVA::MethodBase::SetPreprocessingType(TMVA::Types::ESBType t)
voidTMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
voidTMVA::MethodBase::SetTestvarName()
voidTMVA::MethodBase::SetTestvarName(TString v)
voidTMVA::MethodBase::SetTestvarPrefix(TString prefix)
voidTMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Bool_t norm = kFALSE)
voidTMVA::MethodBase::WriteOptionsToStream(ostream& o) const
private:
virtual voidDeclareOptions()
Float_tGetError(Float_t countS, Float_t countB, Float_t sumW2S, Float_t sumW2B) const
TTree*GetReferenceTree() const
voidInitPDERS()
virtual voidProcessOptions()
Float_tRScalc(const TMVA::Event&)
voidSetReferenceTree(TTree* t)
voidSetVolumeElement()
voidUpdateThis()

Data Members

public:
enum EVolumeRangeMode { kUnsupported
kMinMax
kRMS
kAdaptive
kUnscaled
};
enum EKernelEstimator { kBox
kSphere
kTeepee
kGauss
kSinc3
kSinc5
kSinc7
kSinc9
kSinc11
kLanczos2
kLanczos3
kLanczos5
kLanczos8
};
enum TMVA::MethodBase::EWeightFileType { kROOT
kTEXT
};
enum TMVA::MethodBase::ECutOrientation { kNegative
kPositive
};
enum TObject::EStatusBits { kCanDelete
kMustCleanup
kObjInCanvas
kIsReferenced
kHasUUID
kCannotPick
kNoContextMenu
kInvalidObject
};
enum TObject::[unnamed] { kIsOnHeap
kNotDeleted
kZombie
kBitMask
kSingleKey
kOverwrite
kWriteDelete
};
protected:
TMVA::Volume*fHelpVolume
Int_tfFcnCall
TMVA::Ranking*TMVA::MethodBase::fRankingranking
vector<TString>*TMVA::MethodBase::fInputVarsvector of input variables used in MVA
Bool_tTMVA::MethodBase::fIsOKstatus of sanity checks
TH1*TMVA::MethodBase::fHistS_plotbinMVA plots used for graphics representation (signal)
TH1*TMVA::MethodBase::fHistB_plotbinMVA plots used for graphics representation (background)
TH1*TMVA::MethodBase::fHistS_highbinMVA plots used for efficiency calculations (signal)
TH1*TMVA::MethodBase::fHistB_highbinMVA plots used for efficiency calculations (background)
TH1*TMVA::MethodBase::fEffSefficiency plot (signal)
TH1*TMVA::MethodBase::fEffBefficiency plot (background)
TH1*TMVA::MethodBase::fEffBvsSbackground efficiency versus signal efficiency
TH1*TMVA::MethodBase::fRejBvsSbackground rejection (=1-eff.) versus signal efficiency
TH1*TMVA::MethodBase::fHistBhatSworking histograms needed for mu-transform (signal)
TH1*TMVA::MethodBase::fHistBhatBworking histograms needed for mu-transform (background)
TH1*TMVA::MethodBase::fHistMuSmu-transform (signal)
TH1*TMVA::MethodBase::fHistMuBmu-transform (background)
TH1*TMVA::MethodBase::fTrainEffSTraining efficiency plot (signal)
TH1*TMVA::MethodBase::fTrainEffBTraining efficiency plot (background)
TH1*TMVA::MethodBase::fTrainEffBvsSTraining background efficiency versus signal efficiency
TH1*TMVA::MethodBase::fTrainRejBvsSTraining background rejection (=1-eff.) versus signal efficiency
Double_tTMVA::MethodBase::fX
Double_tTMVA::MethodBase::fMode
TGraph*TMVA::MethodBase::fGraphSgraphs used for splines for efficiency (signal)
TGraph*TMVA::MethodBase::fGraphBgraphs used for splines for efficiency (background)
TGraph*TMVA::MethodBase::fGrapheffBvsSgraphs used for splines for signal eff. versus background eff.
TMVA::PDF*TMVA::MethodBase::fSplSPDFs of MVA distribution (signal)
TMVA::PDF*TMVA::MethodBase::fSplBPDFs of MVA distribution (background)
TSpline*TMVA::MethodBase::fSpleffBvsSsplines for signal eff. versus background eff.
TGraph*TMVA::MethodBase::fGraphTrainSgraphs used for splines for training efficiency (signal)
TGraph*TMVA::MethodBase::fGraphTrainBgraphs used for splines for training efficiency (background)
TGraph*TMVA::MethodBase::fGraphTrainEffBvsSgraphs used for splines for training signal eff. versus background eff.
TMVA::PDF*TMVA::MethodBase::fSplTrainSPDFs of training MVA distribution (signal)
TMVA::PDF*TMVA::MethodBase::fSplTrainBPDFs of training MVA distribution (background)
TSpline*TMVA::MethodBase::fSplTrainEffBvsSsplines for training signal eff. versus background eff.
Int_tTMVA::MethodBase::fNbinsnumber of bins in representative histograms
Int_tTMVA::MethodBase::fNbinsHnumber of bins in evaluation histograms
TMVA::MethodBase::ECutOrientationTMVA::MethodBase::fCutOrientation+1 if Sig>Bkg, -1 otherwise
TMVA::TSpline1*TMVA::MethodBase::fSplRefShelper splines for RootFinder (signal)
TMVA::TSpline1*TMVA::MethodBase::fSplRefBhelper splines for RootFinder (background)
TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefShelper splines for RootFinder (signal)
TMVA::TSpline1*TMVA::MethodBase::fSplTrainRefBhelper splines for RootFinder (background)
TMVA::OptionBase*TMVA::MethodBase::fLastDeclaredOptionlast declared option
TListTMVA::MethodBase::fListOfOptionsoption list
TMVA::MsgLoggerTMVA::MethodBase::fLoggermessage logger
private:
TStringfVolumeRangeoption volume range
TStringfKernelStringoption kernel estimator
TMVA::MethodPDERS::EVolumeRangeModefVRangeMode
TMVA::MethodPDERS::EKernelEstimatorfKernelEstimator
TTree*fReferenceTreetree used to create binary search trees
TMVA::BinarySearchTree*fBinaryTreeSbinary tree for signal
TMVA::BinarySearchTree*fBinaryTreeBbinary tree for background
vector<Float_t>*fDeltasize of volume
vector<Float_t>*fShiftvolume center
Float_tfScaleSweight for signal events
Float_tfScaleBweight for background events
Float_tfDeltaFracfraction of RMS
Double_tfGaussSigmasize of Gauss in adaptive volume
TFile*fFinweight file
Float_tfNEventsMinminimum number of events in adaptive volume
Float_tfNEventsMaxmaximum number of events in adaptive volume
Float_tfMaxVIterationsmaximum number of iterations to adapt volume size
Float_tfInitialScaleinitial scale for adaptive volume
Bool_tfInitializedVolumeEleis volume element initialized ?
static TMVA::MethodPDERS*fgThisPDERSthis pointer (required by root finder)

Class Description
/* This is a generalization of the above Likelihood methods to Nvar dimensions, where Nvar is the number of input variables used in the MVA. If the multi-dimensional probability density functions (PDFs) for signal and background were known, this method contains the entire physical information, and is therefore optimal. Usually, kernel estimation methods are used to approximate the PDFs using the events from the training sample.

A very simple probability density estimator (PDE) has been suggested in hep-ex/0211019. The PDE for a given test event is obtained from counting the (normalized) number of signal and background (training) events that occur in the "vicinity" of the test event. The volume that describes "vicinity" is user-defined. A search method based on binary-trees is used to effectively reduce the selection time for the range search. Three different volume definitions are optional:

The adaptive range search is used by default.
MethodPDERS( TString jobName, TString methodTitle, DataSet& theData, TString theOption, TDirectory* theTargetDir )
 standard constructor for the PDERS method
 format and syntax of option string: "VolumeRangeMode:options"
 where:
    VolumeRangeMode - all methods defined in private enum "VolumeRangeMode" 
    options         - deltaFrac in case of VolumeRangeMode=MinMax/RMS
                    - nEventsMin/Max, maxVIterations, scale for VolumeRangeMode=Adaptive
MethodPDERS( DataSet& theData, TString theWeightFile, TDirectory* theTargetDir )
 construct MethodPDERS through from file
void InitPDERS( void )
 default initialisation routine called by all constructors
~MethodPDERS( void )
 destructor
void DeclareOptions()
 define the options (their key words) that can be set in the option string 
 know options:
 VolumeRangeMode   <string>  Method to determine volume range
    available values are:        MinMax <default>
                                 Unscaled
                                 RMS
                                 Adaptive

 KernelEstimator   <string>  Kernel estimation function
    available values are:        Box <default>
                                 Sphere
                                 Teepee
                                 Gauss
                                 Sinc3
                                 Sinc5
                                 Sinc7
                                 Sinc9
                                 Sinc11
                                 Lanczos2
                                 Lanczos3
                                 Lanczos5
                                 Lanczos8

 DeltaFrac         <float>   Ratio of #EventsMin/#EventsMax for MinMax and RMS volume range
 NEventsMin        <int>     Minimum number of events for adaptive volume range             
 NEventsMax        <int>     Maximum number of events for adaptive volume range
 MaxVIterations    <int>     Maximum number of iterations for adaptive volume range
 InitialScale      <float>   Initial scale for adaptive volume range           
 GaussSigma        <float>   Width with respect to the volume size of Gaussian kernel estimator
void ProcessOptions()
 process the options specified by the user
void Train( void )
 this is a dummy training: the preparation work to do is the construction
 of the binary tree as a pointer chain. It is easier to directly save the
 trainingTree in the weight file, and to rebuild the binary tree in the
 test phase from scratch
Double_t GetMvaValue()
 init the size of a volume element using a defined fraction of the
 volume containing the entire events
void SetVolumeElement( void )
 defines volume dimensions
Double_t IGetVolumeContentForRoot( Double_t scale )
 Interface to RootFinder
Double_t GetVolumeContentForRoot( Double_t scale )
 count number of events in rescaled volume
Float_t RScalc( const TMVA::Event& e )
 computes event weight by counting number of signal and background 
 events (of reference sample) that are found within given volume
 defined by the event
Double_t KernelEstimate( const TMVA::Event & event, vector<TMVA::Event*>& events, TMVA::Volume& v )
 Final estimate
Float_t GetError( Float_t countS, Float_t countB, Float_t sumW2S, Float_t sumW2B )
 statistical error estimate for RS estimator
void WriteWeightsToStream( ostream& o )
 write training sample (TTree) to file
void ReadWeightsFromStream( istream& istr )
 read training sample from file
MethodPDERS* ThisPDERS( void )
 static pointer to this object
{ return fgThisPDERS; }
BinarySearchTree* GetBinaryTreeSig( void )
 accessors
{ return fBinaryTreeS; }
BinarySearchTree* GetBinaryTreeBkg( void )
{ return fBinaryTreeB; }
Double_t ApplyKernelFunction( Double_t normalized_distance )
Double_t KernelNormalization( Double_t pdf )
Double_t GetNormalizedDistance( const TMVA::Event &base_event, const TMVA::Event &sample_event, Double_t *dim_normalization)
Double_t LanczosFilter( Int_t level, Double_t x )
const Ranking* CreateRanking()
 ranking of input variables
{ return 0; }
TTree* GetReferenceTree()
{ return fReferenceTree; }
void SetReferenceTree( TTree* t )
{ fReferenceTree = t; }
void UpdateThis()
{ fgThisPDERS = this; }

Author: Andreas Hoecker, Yair Mahalalel, Joerg Stelzer, Helge Voss, Kai Voss
Last update: root/tmva $Id: MethodPDERS.cxx,v 1.11 2006/11/20 15:35:28 brun Exp $
Copyright (c) 2005: *

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