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TMVA::MethodANNBase Class Referenceabstract

Definition at line 80 of file MethodANNBase.h.

Public Types

enum  EEstimator { kMSE =0, kCE }
 
- Public Types inherited from TMVA::MethodBase
enum  EWeightFileType { kROOT =0, kTEXT }
 
- Public Types inherited from TObject
enum  EStatusBits {
  kCanDelete = BIT(0), kMustCleanup = BIT(3), kObjInCanvas = BIT(3), kIsReferenced = BIT(4),
  kHasUUID = BIT(5), kCannotPick = BIT(6), kNoContextMenu = BIT(8), kInvalidObject = BIT(13)
}
 
enum  { kIsOnHeap = 0x01000000, kNotDeleted = 0x02000000, kZombie = 0x04000000, kBitMask = 0x00ffffff }
 
enum  { kSingleKey = BIT(0), kOverwrite = BIT(1), kWriteDelete = BIT(2) }
 

Public Member Functions

 MethodANNBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &theData, const TString &theOption, TDirectory *theTargetDir)
 
 MethodANNBase (Types::EMVA methodType, DataSetInfo &theData, const TString &theWeightFile, TDirectory *theTargetDir)
 construct the Method from the weight file More...
 
virtual ~MethodANNBase ()
 destructor More...
 
void InitANNBase ()
 initialize ANNBase object More...
 
void SetActivation (TActivation *activation)
 
void SetNeuronInputCalculator (TNeuronInput *inputCalculator)
 
virtual void Train ()=0
 
virtual void PrintNetwork () const
 print network representation, for debugging More...
 
template<typename WriteIterator >
void GetLayerActivation (size_t layer, WriteIterator writeIterator)
 
void AddWeightsXMLTo (void *parent) const
 create XML description of ANN classifier More...
 
void ReadWeightsFromXML (void *wghtnode)
 read MLP from xml weight file More...
 
virtual void ReadWeightsFromStream (std::istream &istr)
 destroy/clear the network then read it back in from the weights file More...
 
virtual Double_t GetMvaValue (Double_t *err=0, Double_t *errUpper=0)
 get the mva value generated by the NN More...
 
virtual const std::vector
< Float_t > & 
GetRegressionValues ()
 get the regression value generated by the NN More...
 
virtual const std::vector
< Float_t > & 
GetMulticlassValues ()
 get the multiclass classification values generated by the NN More...
 
virtual void WriteMonitoringHistosToFile () const
 write histograms to file More...
 
const RankingCreateRanking ()
 compute ranking of input variables by summing function of weights More...
 
virtual void DeclareOptions ()
 define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared. More...
 
virtual void ProcessOptions ()
 do nothing specific at this moment More...
 
Bool_t Debug () const
 who the hell makes such strange Debug flags that even use "global pointers".. More...
 
- Public Member Functions inherited from TMVA::MethodBase
 MethodBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption="", TDirectory *theBaseDir=0)
 standard constructur More...
 
 MethodBase (Types::EMVA methodType, DataSetInfo &dsi, const TString &weightFile, TDirectory *theBaseDir=0)
 constructor used for Testing + Application of the MVA, only (no training), using given WeightFiles More...
 
virtual ~MethodBase ()
 destructor More...
 
void SetupMethod ()
 setup of methods 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...
 
virtual void CheckSetup ()
 check may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More...
 
void AddOutput (Types::ETreeType type, Types::EAnalysisType analysisType)
 
void TrainMethod ()
 
virtual std::map< TString,
Double_t
OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA")
 call the Optimzier with the set of paremeters and ranges that are meant to be tuned. More...
 
virtual void SetTuneParameters (std::map< TString, Double_t > tuneParameters)
 set the tuning parameters accoding to the argument This is just a dummy . More...
 
void SetTrainTime (Double_t trainTime)
 
Double_t GetTrainTime () const
 
void SetTestTime (Double_t testTime)
 
Double_t GetTestTime () const
 
virtual void TestClassification ()
 initialization More...
 
virtual Double_t GetKSTrainingVsTest (Char_t SorB, TString opt="X")
 
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 Init ()=0
 
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 weightfile at hand More...
 
virtual void Reset ()
 
Double_t GetMvaValue (const TMVA::Event *const ev, Double_t *err=0, Double_t *errUpper=0)
 
const std::vector< Float_t > & GetRegressionValues (const TMVA::Event *const ev)
 
virtual Double_t GetProba (const Event *ev)
 
virtual Double_t GetProba (Double_t mvaVal, Double_t ap_sig)
 compute likelihood ratio More...
 
virtual Double_t GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const
 compute rarity: R(x) = Integrate_[-oo..x] { PDF(x') dx' } where PDF(x) is the PDF of the classifier's signal or background distribution More...
 
virtual void MakeClass (const TString &classFileName=TString("")) const
 create reader class for method (classification only at present) More...
 
void PrintHelpMessage () const
 prints out method-specific help method 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...
 
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...
 
virtual void WriteEvaluationHistosToFile (Types::ETreeType treetype)
 writes all MVA evaluation histograms to file More...
 
virtual Double_t GetEfficiency (const TString &, Types::ETreeType, Double_t &err)
 fill background efficiency (resp. More...
 
virtual Double_t GetTrainingEfficiency (const TString &)
 
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 Double_t GetSignificance () const
 compute significance of mean difference significance = |<S> - |/Sqrt(RMS_S2 + RMS_B2) 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 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 GetMaximumSignificance (Double_t SignalEvents, Double_t BackgroundEvents, Double_t &optimal_significance_value) const
 plot significance, 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...
 
virtual Double_t GetSeparation (TH1 *, TH1 *) const
 compute "separation" defined as <s2> = (1/2) Int_-oo..+oo { (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> = (1/2) Int_-oo..+oo { (S(x) - B(x))^2/(S(x) + B(x)) dx } More...
 
virtual void GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const
 
const TStringGetJobName () const
 
const TStringGetMethodName () const
 
TString GetMethodTypeName () const
 
Types::EMVA GetMethodType () const
 
const char * GetName () const
 Returns name of object. More...
 
const TStringGetTestvarName () const
 
const TString GetProbaName () const
 
TString GetWeightFileName () const
 retrieve weight file name More...
 
void SetTestvarName (const TString &v="")
 
UInt_t GetNvar () const
 
UInt_t GetNVariables () const
 
UInt_t GetNTargets () const
 
const TStringGetInputVar (Int_t i) const
 
const TStringGetInputLabel (Int_t i) const
 
const TStringGetInputTitle (Int_t i) const
 
Double_t GetMean (Int_t ivar) const
 
Double_t GetRMS (Int_t ivar) const
 
Double_t GetXmin (Int_t ivar) const
 
Double_t GetXmax (Int_t ivar) const
 
Double_t GetSignalReferenceCut () const
 
Double_t GetSignalReferenceCutOrientation () const
 
void SetSignalReferenceCut (Double_t cut)
 
void SetSignalReferenceCutOrientation (Double_t cutOrientation)
 
TDirectoryBaseDir () const
 returns the ROOT directory where info/histograms etc of the corresponding MVA method instance are stored More...
 
TDirectoryMethodBaseDir () const
 returns the ROOT directory where all instances of the corresponding MVA method are stored More...
 
void SetMethodDir (TDirectory *methodDir)
 
void SetBaseDir (TDirectory *methodDir)
 
void SetMethodBaseDir (TDirectory *methodDir)
 
UInt_t GetTrainingTMVAVersionCode () const
 
UInt_t GetTrainingROOTVersionCode () const
 
TString GetTrainingTMVAVersionString () const
 calculates the TMVA version string from the training version code on the fly More...
 
TString GetTrainingROOTVersionString () const
 calculates the ROOT version string from the training version code on the fly More...
 
TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true)
 
const TransformationHandlerGetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const
 
void RerouteTransformationHandler (TransformationHandler *fTargetTransformation)
 
DataSetData () const
 
DataSetInfoDataInfo () const
 
UInt_t GetNEvents () const
 temporary event when testing on a different DataSet than the own one 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 EventGetTrainingEvent (Long64_t ievt) const
 
const EventGetTestingEvent (Long64_t ievt) const
 
const std::vector< TMVA::Event * > & GetEventCollection (Types::ETreeType type)
 returns the event collection (i.e. More...
 
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 tbe selected as signal or background More...
 
Bool_t HasMVAPdfs () const
 
virtual void SetAnalysisType (Types::EAnalysisType type)
 
Types::EAnalysisType GetAnalysisType () const
 
Bool_t DoRegression () const
 
Bool_t DoMulticlass () const
 
void DisableWriting (Bool_t setter)
 
- Public Member Functions inherited from TMVA::IMethod
 IMethod ()
 
virtual ~IMethod ()
 
virtual Bool_t HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)=0
 
- Public Member Functions inherited from TMVA::Configurable
 Configurable (const TString &theOption="")
 
virtual ~Configurable ()
 default destructur More...
 
virtual void ParseOptions ()
 options parser More...
 
void PrintOptions () const
 prints out the options set in the options string and the defaults More...
 
const char * GetConfigName () const
 
const char * GetConfigDescription () const
 
void SetConfigName (const char *n)
 
void SetConfigDescription (const char *d)
 
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 >
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...
 
const TStringGetOptions () const
 
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...
 
void ReadOptionsFromStream (std::istream &istr)
 read option back from the weight file More...
 
void AddOptionsXMLTo (void *parent) const
 write options to XML file More...
 
void ReadOptionsFromXML (void *node)
 
void SetMsgType (EMsgType t)
 
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)
 
- Public Member Functions inherited from TObject
 TObject ()
 
 TObject (const TObject &object)
 TObject copy ctor. More...
 
TObjectoperator= (const TObject &rhs)
 TObject assignment operator. More...
 
virtual ~TObject ()
 TObject destructor. 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...
 
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 pad. More...
 
virtual void Dump () const
 Dump contents of object on stdout. 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 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 UInt_t GetUniqueID () const
 Return the unique object id. More...
 
virtual const char * GetIconName () const
 Returns mime type name of object. More...
 
virtual Option_tGetOption () const
 
virtual char * GetObjectInfo (Int_t px, Int_t py) const
 Returns string containing info about the object at position (px,py). More...
 
virtual const char * GetTitle () const
 Returns title of object. 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...
 
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...
 
virtual Bool_t IsFolder () const
 Returns kTRUE in case object contains browsable objects (like containers or lists of other objects). More...
 
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 IsSortable () const
 
Bool_t IsOnHeap () const
 
Bool_t IsZombie () const
 
virtual Bool_t Notify ()
 This method must be overridden to handle object notification. More...
 
virtual void ls (Option_t *option="") const
 The ls function lists the contents of a class on stdout. 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...
 
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...
 
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 UseCurrentStyle ()
 Set current style settings in this object This function is called when either TCanvas::UseCurrentStyle or TROOT::ForceStyle have been invoked. 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...
 
voidoperator new (size_t sz)
 
voidoperator new[] (size_t sz)
 
voidoperator new (size_t sz, void *vp)
 
voidoperator new[] (size_t sz, void *vp)
 
void operator delete (void *ptr)
 Operator delete. More...
 
void operator delete[] (void *ptr)
 Operator delete []. 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)
 
void ResetBit (UInt_t f)
 
Bool_t TestBit (UInt_t f) const
 
Int_t TestBits (UInt_t f) const
 
void InvertBit (UInt_t f)
 
virtual void Info (const char *method, const char *msgfmt,...) const
 Issue info message. More...
 
virtual void Warning (const char *method, const char *msgfmt,...) const
 Issue warning message. More...
 
virtual void Error (const char *method, const char *msgfmt,...) const
 Issue error message. More...
 
virtual void SysError (const char *method, const char *msgfmt,...) const
 Issue system error message. More...
 
virtual void Fatal (const char *method, const char *msgfmt,...) const
 Issue fatal error message. 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...
 
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...
 
void Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const
 Use this method to declare a method obsolete. More...
 

Protected Member Functions

virtual void MakeClassSpecific (std::ostream &, const TString &) const
 write specific classifier response More...
 
std::vector< Int_t > * ParseLayoutString (TString layerSpec)
 parse layout specification string and return a vector, each entry containing the number of neurons to go in each successive layer More...
 
virtual void BuildNetwork (std::vector< Int_t > *layout, std::vector< Double_t > *weights=NULL, Bool_t fromFile=kFALSE)
 build network given a layout (number of neurons in each layer) and optional weights array More...
 
void ForceNetworkInputs (const Event *ev, Int_t ignoreIndex=-1)
 force the input values of the input neurons force the value for each input neuron More...
 
Double_t GetNetworkOutput ()
 
void PrintMessage (TString message, Bool_t force=kFALSE) const
 print messages, turn off printing by setting verbose and debug flag appropriately More...
 
void ForceNetworkCalculations ()
 calculate input values to each neuron More...
 
void WaitForKeyboard ()
 wait for keyboard input, for debugging More...
 
Int_t NumCycles ()
 
TNeuronGetInputNeuron (Int_t index)
 
TNeuronGetOutputNeuron (Int_t index=0)
 
void CreateWeightMonitoringHists (const TString &bulkname, std::vector< TH1 * > *hv=0) const
 
- Protected Member Functions inherited from TMVA::MethodBase
void NoErrorCalc (Double_t *const err, Double_t *const errUpper)
 
virtual void ReadWeightsFromStream (TFile &)
 
void SetWeightFileName (TString)
 set the weight file name (depreciated) More...
 
const TStringGetWeightFileDir () const
 
void SetWeightFileDir (TString fileDir)
 set directory of weight file More...
 
Bool_t IsNormalised () const
 
void SetNormalised (Bool_t norm)
 
Bool_t Verbose () const
 
Bool_t Help () const
 
const TStringGetInternalVarName (Int_t ivar) const
 
const TStringGetOriginalVarName (Int_t ivar) const
 
Bool_t HasTrainingTree () const
 
virtual void MakeClassSpecificHeader (std::ostream &, const TString &="") const
 
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 IsConstructedFromWeightFile () const
 
Bool_t IgnoreEventsWithNegWeightsInTraining () const
 
- Protected Member Functions inherited from TMVA::IMethod
virtual void GetHelpMessage () const =0
 
- Protected Member Functions inherited from TMVA::Configurable
Bool_t LooseOptionCheckingEnabled () const
 
void EnableLooseOptions (Bool_t b=kTRUE)
 
void WriteOptionsReferenceToFile ()
 write complete options to output stream More...
 
void ResetSetFlag ()
 resets the IsSet falg for all declare options to be called before options are read from stream More...
 
const TStringGetReferenceFile () const
 
MsgLoggerLog () const
 
- Protected Member Functions inherited from TObject
void MakeZombie ()
 
virtual void DoError (int level, const char *location, const char *fmt, va_list va) const
 Interface to ErrorHandler (protected). More...
 

Protected Attributes

TObjArrayfNetwork
 
TObjArrayfSynapses
 
TActivationfActivation
 
TActivationfOutput
 
TActivationfIdentity
 
TRandom3frgen
 
TNeuronInputfInputCalculator
 
std::vector< Int_tfRegulatorIdx
 
std::vector< Double_tfRegulators
 
EEstimator fEstimator
 
TString fEstimatorS
 
TH1FfEstimatorHistTrain
 
TH1FfEstimatorHistTest
 
std::vector< TH1 * > fEpochMonHistS
 
std::vector< TH1 * > fEpochMonHistB
 
std::vector< TH1 * > fEpochMonHistW
 
TMatrixD fInvHessian
 
bool fUseRegulator
 
Int_t fRandomSeed
 
Int_t fNcycles
 
TString fNeuronType
 
TString fNeuronInputType
 
- Protected Attributes inherited from TMVA::MethodBase
RankingfRanking
 
std::vector< TString > * fInputVars
 
Int_t fNbins
 
Int_t fNbinsMVAoutput
 
Int_t fNbinsH
 
Types::EAnalysisType fAnalysisType
 
std::vector< Float_t > * fRegressionReturnVal
 
std::vector< Float_t > * fMulticlassReturnVal
 
UInt_t fSignalClass
 
UInt_t fBackgroundClass
 

Private Member Functions

void BuildLayers (std::vector< Int_t > *layout, Bool_t from_file=false)
 build the network layers More...
 
void BuildLayer (Int_t numNeurons, TObjArray *curLayer, TObjArray *prevLayer, Int_t layerIndex, Int_t numLayers, Bool_t from_file=false)
 build a single layer with neurons and synapses connecting this layer to the previous layer More...
 
void AddPreLinks (TNeuron *neuron, TObjArray *prevLayer)
 add synapses connecting a neuron to its preceding layer More...
 
void InitWeights ()
 initialize the synapse weights randomly More...
 
void ForceWeights (std::vector< Double_t > *weights)
 force the synapse weights More...
 
void DeleteNetwork ()
 delete/clear network More...
 
void DeleteNetworkLayer (TObjArray *&layer)
 delete a network layer More...
 
void PrintLayer (TObjArray *layer) const
 print a single layer, for debugging More...
 
void PrintNeuron (TNeuron *neuron) const
 print a neuron, for debugging More...
 

Private Attributes

TObjArrayfInputLayer
 
std::vector< TNeuron * > fOutputNeurons
 
TString fLayerSpec
 

Static Private Attributes

static const Bool_t fgDEBUG = kTRUE
 

Additional Inherited Members

- Static Public Member Functions inherited from TObject
static Long_t GetDtorOnly ()
 Return destructor only flag. More...
 
static void SetDtorOnly (void *obj)
 Set destructor only flag. More...
 
static Bool_t GetObjectStat ()
 Get status of object stat flag. More...
 
static void SetObjectStat (Bool_t stat)
 Turn on/off tracking of objects in the TObjectTable. More...
 
- Public Attributes inherited from TMVA::MethodBase
const EventfTmpEvent
 
Bool_t fSetupCompleted
 
- Static Protected Member Functions inherited from TMVA::MethodBase
static MethodBaseGetThisBase ()
 return a pointer the base class of this method More...
 

#include <TMVA/MethodANNBase.h>

Inheritance diagram for TMVA::MethodANNBase:
[legend]

Member Enumeration Documentation

Enumerator
kMSE 
kCE 

Definition at line 156 of file MethodANNBase.h.

Constructor & Destructor Documentation

TMVA::MethodANNBase::MethodANNBase ( const TString jobName,
Types::EMVA  methodType,
const TString methodTitle,
DataSetInfo theData,
const TString theOption,
TDirectory theTargetDir 
)
TMVA::MethodANNBase::MethodANNBase ( Types::EMVA  methodType,
DataSetInfo theData,
const TString theWeightFile,
TDirectory theTargetDir 
)

construct the Method from the weight file

Definition at line 99 of file MethodANNBase.cxx.

TMVA::MethodANNBase::~MethodANNBase ( )
virtual

destructor

Definition at line 236 of file MethodANNBase.cxx.

Member Function Documentation

void TMVA::MethodANNBase::AddPreLinks ( TNeuron neuron,
TObjArray prevLayer 
)
private

add synapses connecting a neuron to its preceding layer

Definition at line 423 of file MethodANNBase.cxx.

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

create XML description of ANN classifier

Implements TMVA::MethodBase.

Definition at line 712 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::BuildLayer ( Int_t  numNeurons,
TObjArray curLayer,
TObjArray prevLayer,
Int_t  layerIndex,
Int_t  numLayers,
Bool_t  from_file = false 
)
private

build a single layer with neurons and synapses connecting this layer to the previous layer

Definition at line 371 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::BuildLayers ( std::vector< Int_t > *  layout,
Bool_t  from_file = false 
)
private

build the network layers

Definition at line 335 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::BuildNetwork ( std::vector< Int_t > *  layout,
std::vector< Double_t > *  weights = NULL,
Bool_t  fromFile = kFALSE 
)
protectedvirtual

build network given a layout (number of neurons in each layer) and optional weights array

Definition at line 291 of file MethodANNBase.cxx.

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

compute ranking of input variables by summing function of weights

Implements TMVA::MethodBase.

Definition at line 924 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::CreateWeightMonitoringHists ( const TString bulkname,
std::vector< TH1 * > *  hv = 0 
) const
protected

Definition at line 966 of file MethodANNBase.cxx.

Bool_t TMVA::MethodANNBase::Debug ( ) const

who the hell makes such strange Debug flags that even use "global pointers"..

Definition at line 1167 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::DeclareOptions ( )
virtual

define the options (their key words) that can be set in the option string here the options valid for ALL MVA methods are declared.

know options: NCycles=xx :the number of training cycles Normalize=kTRUE,kFALSe :if normalised in put variables should be used HiddenLayser="N-1,N-2" :the specification of the hidden layers NeuronType=sigmoid,tanh,radial,linar : the type of activation function used at the neuronn

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 123 of file MethodANNBase.cxx.

Referenced by MethodANNBase().

void TMVA::MethodANNBase::DeleteNetwork ( )
private

delete/clear network

Definition at line 244 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::DeleteNetworkLayer ( TObjArray *&  layer)
private

delete a network layer

Definition at line 275 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::ForceNetworkCalculations ( )
protected

calculate input values to each neuron

Definition at line 492 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::ForceNetworkInputs ( const Event ev,
Int_t  ignoreIndex = -1 
)
protected

force the input values of the input neurons force the value for each input neuron

Definition at line 474 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::ForceWeights ( std::vector< Double_t > *  weights)
private

force the synapse weights

Definition at line 458 of file MethodANNBase.cxx.

TNeuron* TMVA::MethodANNBase::GetInputNeuron ( Int_t  index)
inlineprotected

Definition at line 176 of file MethodANNBase.h.

template<typename WriteIterator >
void TMVA::MethodANNBase::GetLayerActivation ( size_t  layer,
WriteIterator  writeIterator 
)
inline

Definition at line 251 of file MethodANNBase.h.

const std::vector< Float_t > & TMVA::MethodANNBase::GetMulticlassValues ( )
virtual

get the multiclass classification values generated by the NN

Reimplemented from TMVA::MethodBase.

Definition at line 669 of file MethodANNBase.cxx.

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

get the mva value generated by the NN

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 595 of file MethodANNBase.cxx.

Referenced by TMVA::MethodMLP::GetMvaValue().

Double_t TMVA::MethodANNBase::GetNetworkOutput ( )
inlineprotected

Definition at line 167 of file MethodANNBase.h.

TNeuron* TMVA::MethodANNBase::GetOutputNeuron ( Int_t  index = 0)
inlineprotected

Definition at line 177 of file MethodANNBase.h.

Referenced by GetNetworkOutput().

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

get the regression value generated by the NN

Reimplemented from TMVA::MethodBase.

Definition at line 622 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::InitANNBase ( )

initialize ANNBase object

Definition at line 207 of file MethodANNBase.cxx.

Referenced by MethodANNBase().

void TMVA::MethodANNBase::InitWeights ( )
private

initialize the synapse weights randomly

Definition at line 442 of file MethodANNBase.cxx.

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

write specific classifier response

Reimplemented from TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 1049 of file MethodANNBase.cxx.

Referenced by TMVA::MethodMLP::MakeClassSpecific().

Int_t TMVA::MethodANNBase::NumCycles ( )
inlineprotected

Definition at line 175 of file MethodANNBase.h.

Referenced by TMVA::MethodMLP::Train().

std::vector< Int_t > * TMVA::MethodANNBase::ParseLayoutString ( TString  layerSpec)
protected

parse layout specification string and return a vector, each entry containing the number of neurons to go in each successive layer

Definition at line 170 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::PrintLayer ( TObjArray layer) const
private

print a single layer, for debugging

Definition at line 562 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::PrintMessage ( TString  message,
Bool_t  force = kFALSE 
) const
protected

print messages, turn off printing by setting verbose and debug flag appropriately

Definition at line 515 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::PrintNetwork ( ) const
virtual

print network representation, for debugging

Definition at line 538 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::PrintNeuron ( TNeuron neuron) const
private

print a neuron, for debugging

Definition at line 578 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::ProcessOptions ( )
virtual

do nothing specific at this moment

Implements TMVA::MethodBase.

Reimplemented in TMVA::MethodMLP.

Definition at line 155 of file MethodANNBase.cxx.

Referenced by TMVA::MethodMLP::ProcessOptions().

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

destroy/clear the network then read it back in from the weights file

Implements TMVA::MethodBase.

Definition at line 903 of file MethodANNBase.cxx.

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

read MLP from xml weight file

Implements TMVA::MethodBase.

Definition at line 780 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::SetActivation ( TActivation activation)
inline

Definition at line 103 of file MethodANNBase.h.

void TMVA::MethodANNBase::SetNeuronInputCalculator ( TNeuronInput inputCalculator)
inline

Definition at line 106 of file MethodANNBase.h.

virtual void TMVA::MethodANNBase::Train ( )
pure virtual

Implements TMVA::MethodBase.

Implemented in TMVA::MethodMLP.

void TMVA::MethodANNBase::WaitForKeyboard ( )
protected

wait for keyboard input, for debugging

Definition at line 523 of file MethodANNBase.cxx.

void TMVA::MethodANNBase::WriteMonitoringHistosToFile ( void  ) const
virtual

write histograms to file

Reimplemented from TMVA::MethodBase.

Definition at line 1007 of file MethodANNBase.cxx.

Member Data Documentation

TActivation* TMVA::MethodANNBase::fActivation
protected

Definition at line 182 of file MethodANNBase.h.

Referenced by SetActivation().

std::vector<TH1*> TMVA::MethodANNBase::fEpochMonHistB
protected

Definition at line 200 of file MethodANNBase.h.

std::vector<TH1*> TMVA::MethodANNBase::fEpochMonHistS
protected

Definition at line 199 of file MethodANNBase.h.

std::vector<TH1*> TMVA::MethodANNBase::fEpochMonHistW
protected

Definition at line 201 of file MethodANNBase.h.

EEstimator TMVA::MethodANNBase::fEstimator
protected

Definition at line 190 of file MethodANNBase.h.

TH1F* TMVA::MethodANNBase::fEstimatorHistTest
protected

Definition at line 195 of file MethodANNBase.h.

TH1F* TMVA::MethodANNBase::fEstimatorHistTrain
protected

Definition at line 194 of file MethodANNBase.h.

TString TMVA::MethodANNBase::fEstimatorS
protected

Definition at line 191 of file MethodANNBase.h.

const Bool_t TMVA::MethodANNBase::fgDEBUG = kTRUE
staticprivate

Definition at line 243 of file MethodANNBase.h.

TActivation* TMVA::MethodANNBase::fIdentity
protected

Definition at line 184 of file MethodANNBase.h.

TNeuronInput* TMVA::MethodANNBase::fInputCalculator
protected

Definition at line 186 of file MethodANNBase.h.

Referenced by SetNeuronInputCalculator().

TObjArray* TMVA::MethodANNBase::fInputLayer
private

Definition at line 238 of file MethodANNBase.h.

Referenced by GetInputNeuron().

TMatrixD TMVA::MethodANNBase::fInvHessian
protected

Definition at line 205 of file MethodANNBase.h.

TString TMVA::MethodANNBase::fLayerSpec
private

Definition at line 240 of file MethodANNBase.h.

Int_t TMVA::MethodANNBase::fNcycles
protected

Definition at line 211 of file MethodANNBase.h.

Referenced by NumCycles().

TObjArray* TMVA::MethodANNBase::fNetwork
protected

Definition at line 180 of file MethodANNBase.h.

Referenced by GetLayerActivation().

TString TMVA::MethodANNBase::fNeuronInputType
protected

Definition at line 214 of file MethodANNBase.h.

TString TMVA::MethodANNBase::fNeuronType
protected

Definition at line 213 of file MethodANNBase.h.

TActivation* TMVA::MethodANNBase::fOutput
protected

Definition at line 183 of file MethodANNBase.h.

std::vector<TNeuron*> TMVA::MethodANNBase::fOutputNeurons
private

Definition at line 239 of file MethodANNBase.h.

Referenced by GetOutputNeuron().

Int_t TMVA::MethodANNBase::fRandomSeed
protected

Definition at line 209 of file MethodANNBase.h.

std::vector<Int_t> TMVA::MethodANNBase::fRegulatorIdx
protected

Definition at line 188 of file MethodANNBase.h.

std::vector<Double_t> TMVA::MethodANNBase::fRegulators
protected

Definition at line 189 of file MethodANNBase.h.

TRandom3* TMVA::MethodANNBase::frgen
protected

Definition at line 185 of file MethodANNBase.h.

TObjArray* TMVA::MethodANNBase::fSynapses
protected

Definition at line 181 of file MethodANNBase.h.

bool TMVA::MethodANNBase::fUseRegulator
protected

Definition at line 206 of file MethodANNBase.h.

Collaboration diagram for TMVA::MethodANNBase:
[legend]

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