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Reference Guide
TMVA::MethodCFMlpANN Class Reference

Interface to Clermond-Ferrand artificial neural network.

The CFMlpANN belong to the class of Multilayer Perceptrons (MLP), which are feed-forward networks according to the following propagation schema:

Schema for artificial neural network.

The input layer contains as many neurons as input variables used in the MVA. The output layer contains two neurons for the signal and background event classes. In between the input and output layers are a variable number of k hidden layers with arbitrary numbers of neurons. (While the structure of the input and output layers is determined by the problem, the hidden layers can be configured by the user through the option string of the method booking.)

As indicated in the sketch, all neuron inputs to a layer are linear combinations of the neuron output of the previous layer. The transfer from input to output within a neuron is performed by means of an "activation function". In general, the activation function of a neuron can be zero (deactivated), one (linear), or non-linear. The above example uses a sigmoid activation function. The transfer function of the output layer is usually linear. As a consequence: an ANN without hidden layer should give identical discrimination power as a linear discriminant analysis (Fisher). In case of one hidden layer, the ANN computes a linear combination of sigmoid.

The learning method used by the CFMlpANN is only stochastic.

Definition at line 95 of file MethodCFMlpANN.h.

Public Member Functions

 MethodCFMlpANN (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="3000:N-1:N-2")
 standard constructor More...
 
 MethodCFMlpANN (DataSetInfo &theData, const TString &theWeightFile)
 constructor from weight file More...
 
virtual ~MethodCFMlpANN (void)
 destructor More...
 
void AddWeightsXMLTo (void *parent) const
 write weights to xml file More...
 
const RankingCreateRanking ()
 
Int_t GetClass (Int_t ivar) const
 
Double_t GetData (Int_t isel, Int_t ivar) const
 
Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)
 returns CFMlpANN output (normalised within [0,1]) More...
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t)
 CFMlpANN can handle classification with 2 classes. More...
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
void ReadWeightsFromStream (std::istream &istr)
 read back the weight from the training from file (stream) More...
 
virtual void ReadWeightsFromStream (TFile &)
 
void ReadWeightsFromXML (void *wghtnode)
 read weights from xml file More...
 
void Train (void)
 training of the Clement-Ferrand NN classifier 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...
 
virtual const RankingCreateRanking ()=0
 
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...
 
virtual void DeclareOptions ()=0
 
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)
 
virtual Double_t GetMvaValue (Double_t *errLower=0, Double_t *errUpper=0)=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 More...
 
const TString GetProbaName () const
 
virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
 compute rarity: More...
 
virtual void GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const
 
virtual const std::vector< Float_t > & GetRegressionValues ()
 
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 More...
 
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 GetSeparation (PDF *pdfS=0, PDF *pdfB=0) const
 compute "separation" defined as More...
 
virtual Double_t GetSeparation (TH1 *, TH1 *) const
 compute "separation" defined as More...
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
virtual Double_t GetSignificance () const
 compute significance of mean difference 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
 
virtual void Init ()=0
 
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...
 
virtual void ProcessOptions ()=0
 
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...
 
virtual void Train ()=0
 
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 ()
 
virtual const RankingCreateRanking ()=0
 
virtual void DeclareOptions ()=0
 
virtual Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)=0
 
virtual const char * GetName () const =0
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)=0
 
virtual void Init ()=0
 
virtual void MakeClass (const TString &classFileName=TString("")) const =0
 
virtual void PrintHelpMessage () const =0
 
virtual void ProcessOptions ()=0
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
virtual void Train (void)=0
 
virtual void WriteMonitoringHistosToFile (void) const =0
 
- 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 >
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 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 TNamed &named)
 TNamed copy ctor. More...
 
 TNamed (const TString &name, const TString &title)
 
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 * GetName () const
 Returns name of object. 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 Clear (Option_t *="")
 
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 abstract method. More...
 
virtual void Copy (TObject &object) const
 Copy this to obj. 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 const char * GetName () const
 Returns 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 const char * GetTitle () const
 Returns title of object. More...
 
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...
 
virtual ULong_t Hash () const
 Return hash value for this object. 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
 
virtual Bool_t IsSortable () const
 
R__ALWAYS_INLINE Bool_t IsZombie () const
 
virtual void ls (Option_t *option="") const
 The ls function lists the contents of a class on stdout. More...
 
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 void Print (Option_t *option="") const
 This method must be overridden when a class wants to print itself. 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)
 
void SetBit (UInt_t f, Bool_t set)
 Set or unset the user status bits as specified in f. More...
 
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

Int_t DataInterface (Double_t *, Double_t *, Int_t *, Int_t *, Int_t *, Int_t *, Double_t *, Int_t *, Int_t *)
 data interface function More...
 
void GetHelpMessage () const
 get help message text More...
 
void MakeClassSpecific (std::ostream &, const TString &) const
 
void MakeClassSpecificHeader (std::ostream &, const TString &="") const
 write specific classifier response for header More...
 
- Protected Member Functions inherited from TMVA::MethodBase
virtual void AddWeightsXMLTo (void *parent) const =0
 
virtual std::vector< Double_tGetDataMvaValues (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 More...
 
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 MakeClassSpecific (std::ostream &, const TString &="") const
 
virtual void MakeClassSpecificHeader (std::ostream &, const TString &="") const
 
void NoErrorCalc (Double_t *const err, Double_t *const errUpper)
 
virtual void ReadWeightsFromStream (std::istream &)=0
 
virtual void ReadWeightsFromStream (TFile &)
 
virtual void ReadWeightsFromXML (void *wghtnode)=0
 
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
 
virtual void GetHelpMessage () const =0
 
virtual void MakeClassSpecific (std::ostream &, const TString &) const =0
 
- 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 ()
 define the options (their key words) that can be set in the option string know options: NCycles=xx :the number of training cycles HiddenLayser="N-1,N-2" :the specification of the hidden layers More...
 
Double_t EvalANN (std::vector< Double_t > &, Bool_t &isOK)
 evaluates NN value as function of input variables More...
 
void Init (void)
 default initialisation called by all constructors More...
 
void NN_ava (Double_t *)
 auxiliary functions More...
 
Double_t NN_fonc (Int_t, Double_t) const
 activation function More...
 
void PrintWeights (std::ostream &o) const
 write the weights of the neural net More...
 
void ProcessOptions ()
 decode the options in the option string More...
 
- Private Member Functions inherited from TMVA::MethodCFMlpANN_Utils
 MethodCFMlpANN_Utils ()
 default constructor More...
 
virtual ~MethodCFMlpANN_Utils ()
 Destructor. More...
 
void Arret (const char *mot)
 
void CollectVar (Int_t *nvar, Int_t *class__, Double_t *xpg)
 [smart comments to be added] More...
 
void Cout (Int_t *, Double_t *xxx)
 [smart comments to be added] More...
 
void Cout2 (Int_t *, Double_t *yyy)
 [smart comments to be added] More...
 
virtual Int_t DataInterface (Double_t *, Double_t *, Int_t *, Int_t *, Int_t *, Int_t *, Double_t *, Int_t *, Int_t *)=0
 
void En_arriere (Int_t *ievent)
 [smart comments to be added] More...
 
void En_avant (Int_t *ievent)
 [smart comments to be added] More...
 
void En_avant2 (Int_t *ievent)
 [smart comments to be added] More...
 
void Entree_new (Int_t *, char *, Int_t *ntrain, Int_t *ntest, Int_t *numlayer, Int_t *nodes, Int_t *numcycle, Int_t)
 
Double_t Fdecroi (Int_t *i__)
 [smart comments to be added] More...
 
void Foncf (Int_t *i__, Double_t *u, Double_t *f)
 
void GraphNN (Int_t *ilearn, Double_t *, Double_t *, char *, Int_t)
 [smart comments to be added] More...
 
void Inl ()
 [smart comments to be added] More...
 
void Innit (char *det, Double_t *tout2, Double_t *tin2, Int_t)
 
void Lecev2 (Int_t *ktest, Double_t *tout2, Double_t *tin2)
 [smart comments to be added] More...
 
void Leclearn (Int_t *ktest, Double_t *tout2, Double_t *tin2)
 [smart comments to be added] More...
 
void Out (Int_t *iii, Int_t *maxcycle)
 
Double_t Sen3a (void)
 [smart comments to be added] More...
 
void SetLogger (MsgLogger *l)
 
void TestNN ()
 [smart comments to be added] More...
 
void Train_nn (Double_t *tin2, Double_t *tout2, Int_t *ntrain, Int_t *ntest, Int_t *nvar2, Int_t *nlayer, Int_t *nodes, Int_t *ncycle)
 
Double_t W_ref (const Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3) const
 
Double_tW_ref (Double_t wNN[], Int_t a_1, Int_t a_2, Int_t a_3)
 
void Wini ()
 [smart comments to be added] More...
 
Double_t Ww_ref (const Double_t wwNN[], Int_t a_1, Int_t a_2) const
 
Double_tWw_ref (Double_t wwNN[], Int_t a_1, Int_t a_2)
 

Private Attributes

std::vector< Int_t > * fClass
 
TMatrixFfData
 
TString fLayerSpec
 
Int_t fNcycles
 
Int_t fNlayers
 
Int_tfNodes
 
Double_t ** fYNN
 
Int_t MethodCFMlpANN_nsel
 
struct {
   Double_t   ancout
 
   Int_t   ieps
 
   Double_t   tolcou
 
fCost_1
 
struct {
   Double_t   coef [max_nNodes_]
 
   Double_t   del [max_nLayers_ *max_nNodes_]
 
   Double_t   delta [max_nLayers_ *max_nNodes_
      *max_nNodes_]
 
   Double_t   delw [max_nLayers_ *max_nNodes_
      *max_nNodes_]
 
   Double_t   delww [max_nLayers_ *max_nNodes_]
 
   Double_t   demax
 
   Double_t   demin
 
   Int_t   idde
 
   Double_t   temp [max_nLayers_]
 
fDel_1
 
Int_t fg_0
 
Int_t fg_100
 
Int_t fg_999
 
struct {
   Double_t   cut [max_nNodes_]
 
   Double_t   deltaww [max_nLayers_ *max_nNodes_]
 
   Int_t   neuron [max_nLayers_]
 
   Double_t   o [max_nNodes_]
 
   Double_t   w [max_nLayers_ *max_nNodes_
      *max_nNodes_]
 
   Double_t   ww [max_nLayers_ *max_nNodes_]
 
   Double_t   x [max_nLayers_ *max_nNodes_]
 
   Double_t   y [max_nLayers_ *max_nNodes_]
 
fNeur_1
 
struct {
   Double_t   eeps
 
   Double_t   epsmax
 
   Double_t   epsmin
 
   Double_t   eta
 
   Int_t   ichoi
 
   Int_t   itest
 
   Int_t   layerm
 
   Int_t   lclass
 
   Int_t   nblearn
 
   Int_t   ndiv
 
   Int_t   ndivis
 
   Int_t   nevl
 
   Int_t   nevt
 
   Int_t   nunap
 
   Int_t   nunilec
 
   Int_t   nunishort
 
   Int_t   nunisor
 
   Int_t   nvar
 
fParam_1
 
class TMVA::MethodCFMlpANN_Utils::VARn2 fVarn2_1
 
class TMVA::MethodCFMlpANN_Utils::VARn2 fVarn3_1
 
struct {
   Int_t   iclass
 
   Int_t   mclass [max_Events_]
 
   Int_t   nclass [max_Events_]
 
   Double_t   xmax [max_nVar_]
 
   Double_t   xmin [max_nVar_]
 
fVarn_1
 

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. 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
 
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
 temporary dataset used when evaluating on a different data (used by MethodCategory::GetMvaValues) More...
 
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
 
DataSetfTmpData = nullptr
 temporary event when testing on a different DataSet than the own one More...
 
const EventfTmpEvent
 
- Protected Attributes inherited from TMVA::Configurable
MsgLoggerfLogger
 
- Protected Attributes inherited from TNamed
TString fName
 
TString fTitle
 
- Static Private Attributes inherited from TMVA::MethodCFMlpANN_Utils
static const Int_t fg_max_nNodes_ = max_nNodes_
 
static const Int_t fg_max_nVar_ = max_nVar_
 
static const char *const fg_MethodName = "--- CFMlpANN "
 

#include <TMVA/MethodCFMlpANN.h>

Inheritance diagram for TMVA::MethodCFMlpANN:
[legend]

Constructor & Destructor Documentation

◆ MethodCFMlpANN() [1/2]

TMVA::MethodCFMlpANN::MethodCFMlpANN ( const TString jobName,
const TString methodTitle,
DataSetInfo theData,
const TString theOption = "3000:N-1:N-2" 
)

standard constructor

option string: "n_training_cycles:n_hidden_layers"

default is: n_training_cycles = 5000, n_layers = 4

  • note that the number of hidden layers in the NN is: n_hidden_layers = n_layers - 2
  • since there is one input and one output layer. The number of nodes (neurons) is predefined to be:

    n_nodes[i] = nvars + 1 - i (where i=1..n_layers)

    with nvars being the number of variables used in the NN.

Hence, the default case is:

n_neurons(layer 1 (input)) : nvars
n_neurons(layer 2 (hidden)): nvars-1
n_neurons(layer 3 (hidden)): nvars-1
n_neurons(layer 4 (out))   : 2

This artificial neural network usually needs a relatively large number of cycles to converge (8000 and more). Overtraining can be efficiently tested by comparing the signal and background output of the NN for the events that were used for training and an independent data sample (with equal properties). If the separation performance is significantly better for the training sample, the NN interprets statistical effects, and is hence overtrained. In this case, the number of cycles should be reduced, or the size of the training sample increased.

Definition at line 130 of file MethodCFMlpANN.cxx.

◆ MethodCFMlpANN() [2/2]

TMVA::MethodCFMlpANN::MethodCFMlpANN ( DataSetInfo theData,
const TString theWeightFile 
)

constructor from weight file

Definition at line 149 of file MethodCFMlpANN.cxx.

◆ ~MethodCFMlpANN()

TMVA::MethodCFMlpANN::~MethodCFMlpANN ( void  )
virtual

destructor

Definition at line 269 of file MethodCFMlpANN.cxx.

Member Function Documentation

◆ AddWeightsXMLTo()

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

write weights to xml file

Implements TMVA::MethodBase.

Definition at line 537 of file MethodCFMlpANN.cxx.

◆ CreateRanking()

const Ranking * TMVA::MethodCFMlpANN::CreateRanking ( )
inlinevirtual

Implements TMVA::MethodBase.

Definition at line 131 of file MethodCFMlpANN.h.

◆ DataInterface()

Int_t TMVA::MethodCFMlpANN::DataInterface ( Double_t ,
Double_t ,
Int_t ,
Int_t ,
Int_t ,
Int_t nvar,
Double_t xpg,
Int_t iclass,
Int_t ikend 
)
protectedvirtual

data interface function

Implements TMVA::MethodCFMlpANN_Utils.

Definition at line 506 of file MethodCFMlpANN.cxx.

◆ DeclareOptions()

void TMVA::MethodCFMlpANN::DeclareOptions ( )
privatevirtual

define the options (their key words) that can be set in the option string know options: NCycles=xx :the number of training cycles HiddenLayser="N-1,N-2" :the specification of the hidden layers

Implements TMVA::MethodBase.

Definition at line 176 of file MethodCFMlpANN.cxx.

◆ EvalANN()

Double_t TMVA::MethodCFMlpANN::EvalANN ( std::vector< Double_t > &  inVar,
Bool_t isOK 
)
private

evaluates NN value as function of input variables

Definition at line 343 of file MethodCFMlpANN.cxx.

◆ GetClass()

Int_t TMVA::MethodCFMlpANN::GetClass ( Int_t  ivar) const
inline

Definition at line 127 of file MethodCFMlpANN.h.

◆ GetData()

Double_t TMVA::MethodCFMlpANN::GetData ( Int_t  isel,
Int_t  ivar 
) const
inline

Definition at line 126 of file MethodCFMlpANN.h.

◆ GetHelpMessage()

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

get help message text

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

Implements TMVA::IMethod.

Definition at line 711 of file MethodCFMlpANN.cxx.

◆ GetMvaValue()

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

returns CFMlpANN output (normalised within [0,1])

Implements TMVA::MethodBase.

Definition at line 321 of file MethodCFMlpANN.cxx.

◆ HasAnalysisType()

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

CFMlpANN can handle classification with 2 classes.

Implements TMVA::IMethod.

Definition at line 165 of file MethodCFMlpANN.cxx.

◆ Init()

void TMVA::MethodCFMlpANN::Init ( void  )
privatevirtual

default initialisation called by all constructors

Implements TMVA::MethodBase.

Definition at line 257 of file MethodCFMlpANN.cxx.

◆ MakeClassSpecific()

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

Reimplemented from TMVA::MethodBase.

Definition at line 691 of file MethodCFMlpANN.cxx.

◆ MakeClassSpecificHeader()

void TMVA::MethodCFMlpANN::MakeClassSpecificHeader ( std::ostream &  ,
const TString = "" 
) const
protectedvirtual

write specific classifier response for header

Reimplemented from TMVA::MethodBase.

Definition at line 701 of file MethodCFMlpANN.cxx.

◆ NN_ava()

void TMVA::MethodCFMlpANN::NN_ava ( Double_t xeev)
private

auxiliary functions

Definition at line 377 of file MethodCFMlpANN.cxx.

◆ NN_fonc()

Double_t TMVA::MethodCFMlpANN::NN_fonc ( Int_t  i,
Double_t  u 
) const
private

activation function

Definition at line 397 of file MethodCFMlpANN.cxx.

◆ PrintWeights()

void TMVA::MethodCFMlpANN::PrintWeights ( std::ostream &  o) const
private

write the weights of the neural net

Definition at line 630 of file MethodCFMlpANN.cxx.

◆ ProcessOptions()

void TMVA::MethodCFMlpANN::ProcessOptions ( )
privatevirtual

decode the options in the option string

Implements TMVA::MethodBase.

Definition at line 185 of file MethodCFMlpANN.cxx.

◆ ReadWeightsFromStream() [1/3]

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

Implements TMVA::MethodBase.

◆ ReadWeightsFromStream() [2/3]

void TMVA::MethodCFMlpANN::ReadWeightsFromStream ( std::istream &  istr)
virtual

read back the weight from the training from file (stream)

Implements TMVA::MethodBase.

Definition at line 414 of file MethodCFMlpANN.cxx.

◆ ReadWeightsFromStream() [3/3]

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

Reimplemented from TMVA::MethodBase.

Definition at line 266 of file MethodBase.h.

◆ ReadWeightsFromXML()

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

read weights from xml file

Implements TMVA::MethodBase.

Definition at line 581 of file MethodCFMlpANN.cxx.

◆ Train()

void TMVA::MethodCFMlpANN::Train ( void  )
virtual

training of the Clement-Ferrand NN classifier

Implements TMVA::MethodBase.

Definition at line 285 of file MethodCFMlpANN.cxx.

Member Data Documentation

◆ fClass

std::vector<Int_t>* TMVA::MethodCFMlpANN::fClass
private

Definition at line 157 of file MethodCFMlpANN.h.

◆ fData

TMatrixF* TMVA::MethodCFMlpANN::fData
private

Definition at line 156 of file MethodCFMlpANN.h.

◆ fLayerSpec

TString TMVA::MethodCFMlpANN::fLayerSpec
private

Definition at line 165 of file MethodCFMlpANN.h.

◆ fNcycles

Int_t TMVA::MethodCFMlpANN::fNcycles
private

Definition at line 160 of file MethodCFMlpANN.h.

◆ fNlayers

Int_t TMVA::MethodCFMlpANN::fNlayers
private

Definition at line 159 of file MethodCFMlpANN.h.

◆ fNodes

Int_t* TMVA::MethodCFMlpANN::fNodes
private

Definition at line 161 of file MethodCFMlpANN.h.

◆ fYNN

Double_t** TMVA::MethodCFMlpANN::fYNN
private

Definition at line 164 of file MethodCFMlpANN.h.

◆ MethodCFMlpANN_nsel

Int_t TMVA::MethodCFMlpANN::MethodCFMlpANN_nsel
private

Definition at line 166 of file MethodCFMlpANN.h.

Libraries for TMVA::MethodCFMlpANN:
[legend]

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