A++ » TMVA » TMVA::MethodMLP

class TMVA::MethodMLP: public TMVA::MethodANNBase, public TMVA::IFitterTarget, public TMVA::ConvergenceTest


MethodMLP

Multilayer Perceptron built off of MethodANNBase

Function Members (Methods)

public:
virtual~MethodMLP()
voidTObject::AbstractMethod(const char* method) const
voidTMVA::Configurable::AddOptionsXMLTo(void* parent) const
voidTMVA::MethodBase::AddOutput(TMVA::Types::ETreeType type, TMVA::Types::EAnalysisType analysisType)
virtual voidTMVA::MethodANNBase::AddWeightsXMLTo(void* parent) const
virtual voidTObject::AppendPad(Option_t* option = "")
TDirectory*TMVA::MethodBase::BaseDir() const
virtual voidTObject::Browse(TBrowser* b)
voidTMVA::Configurable::CheckForUnusedOptions() const
virtual voidTMVA::MethodBase::CheckSetup()
static TClass*Class()
virtual const char*TObject::ClassName() const
virtual voidTNamed::Clear(Option_t* option = "")
virtual TObject*TNamed::Clone(const char* newname = "") const
virtual Int_tTNamed::Compare(const TObject* obj) const
Double_tComputeEstimator(vector<Double_t>& parameters)
TMVA::ConfigurableTMVA::Configurable::Configurable(const TString& theOption = "")
TMVA::ConfigurableTMVA::Configurable::Configurable(const TMVA::Configurable&)
TMVA::ConvergenceTestTMVA::ConvergenceTest::ConvergenceTest()
TMVA::ConvergenceTestTMVA::ConvergenceTest::ConvergenceTest(const TMVA::ConvergenceTest&)
virtual voidTNamed::Copy(TObject& named) const
virtual const TMVA::Ranking*TMVA::MethodANNBase::CreateRanking()
TMVA::DataSet*TMVA::MethodBase::Data() const
TMVA::DataSetInfo&TMVA::MethodBase::DataInfo() const
Bool_tTMVA::MethodANNBase::Debug() const
virtual voidTMVA::MethodBase::DeclareCompatibilityOptions()
virtual voidTObject::Delete(Option_t* option = "")MENU
voidTMVA::MethodBase::DisableWriting(Bool_t setter)
virtual Int_tTObject::DistancetoPrimitive(Int_t px, Int_t py)
Bool_tTMVA::MethodBase::DoMulticlass() const
Bool_tTMVA::MethodBase::DoRegression() const
virtual voidTObject::Draw(Option_t* option = "")
virtual voidTObject::DrawClass() constMENU
virtual TObject*TObject::DrawClone(Option_t* option = "") constMENU
virtual voidTObject::Dump() constMENU
virtual voidTObject::Error(const char* method, const char* msgfmt) const
virtual Double_tEstimatorFunction(vector<Double_t>& parameters)
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)
voidTMVA::MethodBase::ExitFromTraining()
virtual voidTObject::Fatal(const char* method, const char* msgfmt) const
virtual voidTNamed::FillBuffer(char*& buffer)
virtual TObject*TObject::FindObject(const char* name) const
virtual TObject*TObject::FindObject(const TObject* obj) const
TMVA::Types::EAnalysisTypeTMVA::MethodBase::GetAnalysisType() const
const char*TMVA::Configurable::GetConfigDescription() const
const char*TMVA::Configurable::GetConfigName() const
UInt_tTMVA::MethodBase::GetCurrentIter()
Float_tTMVA::ConvergenceTest::GetCurrentValue()
virtual Option_t*TObject::GetDrawOption() const
static Long_tTObject::GetDtorOnly()
virtual Double_tTMVA::MethodBase::GetEfficiency(const TString&, TMVA::Types::ETreeType, Double_t& err)
const TMVA::Event*TMVA::MethodBase::GetEvent() const
const TMVA::Event*TMVA::MethodBase::GetEvent(const TMVA::Event* ev) const
const TMVA::Event*TMVA::MethodBase::GetEvent(Long64_t ievt) const
const TMVA::Event*TMVA::MethodBase::GetEvent(Long64_t ievt, TMVA::Types::ETreeType type) const
const vector<TMVA::Event*>&TMVA::MethodBase::GetEventCollection(TMVA::Types::ETreeType type)
TFile*TMVA::MethodBase::GetFile() const
virtual const char*TObject::GetIconName() const
const TString&TMVA::MethodBase::GetInputLabel(Int_t i) const
const char*TMVA::MethodBase::GetInputTitle(Int_t i) const
const TString&TMVA::MethodBase::GetInputVar(Int_t i) const
TMultiGraph*TMVA::MethodBase::GetInteractiveTrainingError()
const TString&TMVA::MethodBase::GetJobName() const
virtual Double_tTMVA::MethodBase::GetKSTrainingVsTest(Char_t SorB, TString opt = "X")
virtual Double_tTMVA::MethodBase::GetMaximumSignificance(Double_t SignalEvents, Double_t BackgroundEvents, Double_t& optimal_significance_value) const
UInt_tTMVA::MethodBase::GetMaxIter()
Double_tTMVA::MethodBase::GetMean(Int_t ivar) const
const TString&TMVA::MethodBase::GetMethodName() const
TMVA::Types::EMVATMVA::MethodBase::GetMethodType() const
TStringTMVA::MethodBase::GetMethodTypeName() const
virtual vector<Float_t>TMVA::MethodBase::GetMulticlassEfficiency(vector<vector<Float_t> >& purity)
virtual vector<Float_t>TMVA::MethodBase::GetMulticlassTrainingEfficiency(vector<vector<Float_t> >& purity)
virtual const vector<Float_t>&TMVA::MethodANNBase::GetMulticlassValues()
virtual Double_tGetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)
virtual const char*TMVA::MethodBase::GetName() const
UInt_tTMVA::MethodBase::GetNEvents() const
UInt_tTMVA::MethodBase::GetNTargets() const
UInt_tTMVA::MethodBase::GetNvar() const
UInt_tTMVA::MethodBase::GetNVariables() const
virtual char*TObject::GetObjectInfo(Int_t px, Int_t py) const
static Bool_tTObject::GetObjectStat()
virtual Option_t*TObject::GetOption() const
const TString&TMVA::Configurable::GetOptions() const
virtual Double_tTMVA::MethodBase::GetProba(const TMVA::Event* ev)
virtual Double_tTMVA::MethodBase::GetProba(Double_t mvaVal, Double_t ap_sig)
const TStringTMVA::MethodBase::GetProbaName() const
virtual Double_tTMVA::MethodBase::GetRarity(Double_t mvaVal, TMVA::Types::ESBType reftype = Types::kBackground) const
virtual voidTMVA::MethodBase::GetRegressionDeviation(UInt_t tgtNum, TMVA::Types::ETreeType type, Double_t& stddev, Double_t& stddev90Percent) const
virtual const vector<Float_t>&TMVA::MethodANNBase::GetRegressionValues()
Double_tTMVA::MethodBase::GetRMS(Int_t ivar) const
virtual Double_tTMVA::MethodBase::GetROCIntegral(TH1D* histS, TH1D* histB) const
virtual Double_tTMVA::MethodBase::GetROCIntegral(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
virtual Double_tTMVA::MethodBase::GetSeparation(TH1*, TH1*) const
virtual Double_tTMVA::MethodBase::GetSeparation(TMVA::PDF* pdfS = 0, TMVA::PDF* pdfB = 0) const
Double_tTMVA::MethodBase::GetSignalReferenceCut() const
Double_tTMVA::MethodBase::GetSignalReferenceCutOrientation() const
virtual Double_tTMVA::MethodBase::GetSignificance() const
const TMVA::Event*TMVA::MethodBase::GetTestingEvent(Long64_t ievt) const
Double_tTMVA::MethodBase::GetTestTime() const
const TString&TMVA::MethodBase::GetTestvarName() const
virtual const char*TNamed::GetTitle() const
virtual Double_tTMVA::MethodBase::GetTrainingEfficiency(const TString&)
const TMVA::Event*TMVA::MethodBase::GetTrainingEvent(Long64_t ievt) const
UInt_tTMVA::MethodBase::GetTrainingROOTVersionCode() const
TStringTMVA::MethodBase::GetTrainingROOTVersionString() const
UInt_tTMVA::MethodBase::GetTrainingTMVAVersionCode() const
TStringTMVA::MethodBase::GetTrainingTMVAVersionString() const
Double_tTMVA::MethodBase::GetTrainTime() const
TMVA::TransformationHandler&TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true)
const TMVA::TransformationHandler&TMVA::MethodBase::GetTransformationHandler(Bool_t takeReroutedIfAvailable = true) const
virtual UInt_tTObject::GetUniqueID() const
TStringTMVA::MethodBase::GetWeightFileName() const
Double_tTMVA::MethodBase::GetXmax(Int_t ivar) const
Double_tTMVA::MethodBase::GetXmin(Int_t ivar) const
virtual Bool_tTObject::HandleTimer(TTimer* timer)
virtual Bool_tHasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
Bool_tTMVA::ConvergenceTest::HasConverged(Bool_t withinConvergenceBand = kFALSE)
virtual ULong_tTNamed::Hash() const
boolHasInverseHessian()
Bool_tTMVA::MethodBase::HasMVAPdfs() const
TMVA::IFitterTargetTMVA::IFitterTarget::IFitterTarget()
TMVA::IFitterTargetTMVA::IFitterTarget::IFitterTarget(const TMVA::IFitterTarget&)
TMVA::IMethodTMVA::IMethod::IMethod()
TMVA::IMethodTMVA::IMethod::IMethod(const TMVA::IMethod&)
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
voidTMVA::MethodANNBase::InitANNBase()
voidTMVA::MethodBase::InitIPythonInteractive()
virtual voidTObject::Inspect() constMENU
voidTObject::InvertBit(UInt_t f)
virtual TClass*IsA() const
virtual Bool_tTObject::IsEqual(const TObject* obj) const
virtual Bool_tTObject::IsFolder() const
Bool_tTMVA::MethodBase::IsModelPersistence()
Bool_tTObject::IsOnHeap() const
virtual Bool_tTMVA::MethodBase::IsSignalLike()
virtual Bool_tTMVA::MethodBase::IsSignalLike(Double_t mvaVal)
Bool_tTMVA::MethodBase::IsSilentFile()
virtual Bool_tTNamed::IsSortable() const
Bool_tTObject::IsZombie() const
TMVA::MsgLogger&TMVA::Configurable::Log() const
virtual voidTNamed::ls(Option_t* option = "") const
virtual voidTMVA::MethodBase::MakeClass(const TString& classFileName = TString("")) const
voidTObject::MayNotUse(const char* method) const
TMVA::MethodANNBaseTMVA::MethodANNBase::MethodANNBase(const TMVA::MethodANNBase&)
TMVA::MethodANNBaseTMVA::MethodANNBase::MethodANNBase(TMVA::Types::EMVA methodType, TMVA::DataSetInfo& theData, const TString& theWeightFile)
TMVA::MethodANNBaseTMVA::MethodANNBase::MethodANNBase(const TString& jobName, TMVA::Types::EMVA methodType, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption)
TMVA::MethodBaseTMVA::MethodBase::MethodBase(const TMVA::MethodBase&)
TMVA::MethodBaseTMVA::MethodBase::MethodBase(TMVA::Types::EMVA methodType, TMVA::DataSetInfo& dsi, const TString& weightFile)
TMVA::MethodBaseTMVA::MethodBase::MethodBase(const TString& jobName, TMVA::Types::EMVA methodType, const TString& methodTitle, TMVA::DataSetInfo& dsi, const TString& theOption = "")
TDirectory*TMVA::MethodBase::MethodBaseDir() const
TMVA::MethodMLPMethodMLP(const TMVA::MethodMLP&)
TMVA::MethodMLPMethodMLP(TMVA::DataSetInfo& theData, const TString& theWeightFile)
TMVA::MethodMLPMethodMLP(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption)
virtual Bool_tTObject::Notify()
voidTObject::Obsolete(const char* method, const char* asOfVers, const char* removedFromVers) const
voidTObject::operator delete(void* ptr)
voidTObject::operator delete(void* ptr, void* vp)
voidTObject::operator delete[](void* ptr)
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::MethodMLP&operator=(const TMVA::MethodMLP&)
virtual map<TString,Double_t>TMVA::MethodBase::OptimizeTuningParameters(TString fomType = "ROCIntegral", TString fitType = "FitGA")
virtual voidTObject::Paint(Option_t* option = "")
virtual voidTMVA::Configurable::ParseOptions()
virtual voidTObject::Pop()
virtual voidTNamed::Print(Option_t* option = "") const
virtual voidTMVA::MethodBase::PrintHelpMessage() const
virtual voidTMVA::MethodANNBase::PrintNetwork() const
voidTMVA::Configurable::PrintOptions() const
voidTMVA::MethodBase::ProcessSetup()
Float_tTMVA::ConvergenceTest::Progress()
virtual voidTMVA::IFitterTarget::ProgressNotifier(TString, TString)
virtual Int_tTObject::Read(const char* name)
voidTMVA::Configurable::ReadOptionsFromStream(istream& istr)
voidTMVA::Configurable::ReadOptionsFromXML(void* node)
voidTMVA::MethodBase::ReadStateFromFile()
voidTMVA::MethodBase::ReadStateFromStream(istream& tf)
voidTMVA::MethodBase::ReadStateFromStream(TFile& rf)
voidTMVA::MethodBase::ReadStateFromXMLString(const char* xmlstr)
virtual voidTMVA::MethodANNBase::ReadWeightsFromStream(istream& istr)
virtual voidTMVA::MethodANNBase::ReadWeightsFromXML(void* wghtnode)
virtual voidTObject::RecursiveRemove(TObject* obj)
voidTMVA::MethodBase::RerouteTransformationHandler(TMVA::TransformationHandler* fTargetTransformation)
virtual voidTMVA::MethodBase::Reset()
voidTObject::ResetBit(UInt_t f)
voidTMVA::ConvergenceTest::ResetConvergenceCounter()
virtual voidTObject::SaveAs(const char* filename = "", Option_t* option = "") constMENU
virtual voidTObject::SavePrimitive(ostream& out, Option_t* option = "")
voidTMVA::MethodANNBase::SetActivation(TMVA::TActivation* activation)
virtual voidTMVA::MethodBase::SetAnalysisType(TMVA::Types::EAnalysisType type)
voidTMVA::MethodBase::SetBaseDir(TDirectory* methodDir)
voidTObject::SetBit(UInt_t f)
voidTObject::SetBit(UInt_t f, Bool_t set)
voidTMVA::Configurable::SetConfigDescription(const char* d)
voidTMVA::Configurable::SetConfigName(const char* n)
voidTMVA::ConvergenceTest::SetConvergenceParameters(Int_t steps, Double_t improvement)
voidTMVA::ConvergenceTest::SetCurrentValue(Float_t value)
virtual voidTObject::SetDrawOption(Option_t* option = "")MENU
static voidTObject::SetDtorOnly(void* obj)
voidTMVA::MethodBase::SetFile(TFile* file)
voidTMVA::MethodBase::SetMethodBaseDir(TDirectory* methodDir)
voidTMVA::MethodBase::SetMethodDir(TDirectory* methodDir)
voidTMVA::MethodBase::SetModelPersistence(Bool_t status)
voidTMVA::Configurable::SetMsgType(TMVA::EMsgType t)
virtual voidTNamed::SetName(const char* name)MENU
virtual voidTNamed::SetNameTitle(const char* name, const char* title)
voidTMVA::MethodANNBase::SetNeuronInputCalculator(TMVA::TNeuronInput* inputCalculator)
static voidTObject::SetObjectStat(Bool_t stat)
voidTMVA::Configurable::SetOptions(const TString& s)
voidTMVA::MethodBase::SetSignalReferenceCut(Double_t cut)
voidTMVA::MethodBase::SetSignalReferenceCutOrientation(Double_t cutOrientation)
voidTMVA::MethodBase::SetSilentFile(Bool_t status)
voidTMVA::MethodBase::SetTestTime(Double_t testTime)
voidTMVA::MethodBase::SetTestvarName(const TString& v = "")
virtual voidTNamed::SetTitle(const char* title = "")MENU
voidTMVA::MethodBase::SetTrainTime(Double_t trainTime)
virtual voidTMVA::MethodBase::SetTuneParameters(map<TString,Double_t> tuneParameters)
virtual voidTObject::SetUniqueID(UInt_t uid)
voidTMVA::MethodBase::SetupMethod()
virtual voidShowMembers(TMemberInspector& insp) const
virtual Int_tTNamed::Sizeof() const
Float_tTMVA::ConvergenceTest::SpeedControl(UInt_t ofSteps)
virtual voidStreamer(TBuffer&)
voidStreamerNVirtual(TBuffer& ClassDef_StreamerNVirtual_b)
virtual voidTObject::SysError(const char* method, const char* msgfmt) const
Bool_tTObject::TestBit(UInt_t f) const
Int_tTObject::TestBits(UInt_t f) const
virtual voidTMVA::MethodBase::TestClassification()
virtual voidTMVA::MethodBase::TestMulticlass()
virtual voidTMVA::MethodBase::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, TMVA::Types::ETreeType type)
virtual voidTrain()
boolTMVA::MethodBase::TrainingEnded()
voidTMVA::MethodBase::TrainMethod()
virtual voidTObject::UseCurrentStyle()
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(TMVA::Types::ETreeType treetype)
virtual voidTMVA::MethodANNBase::WriteMonitoringHistosToFile() const
voidTMVA::Configurable::WriteOptionsToStream(ostream& o, const TString& prefix) const
voidTMVA::MethodBase::WriteStateToFile() const
protected:
virtual voidTMVA::MethodANNBase::BuildNetwork(vector<Int_t>* layout, vector<Double_t>* weights = __null, Bool_t fromFile = kFALSE)
voidTMVA::MethodANNBase::CreateWeightMonitoringHists(const TString& bulkname, vector<TH1*>* hv = 0) const
virtual voidTObject::DoError(int level, const char* location, const char* fmt, va_list va) const
voidTMVA::Configurable::EnableLooseOptions(Bool_t b = kTRUE)
voidTMVA::MethodANNBase::ForceNetworkCalculations()
voidTMVA::MethodANNBase::ForceNetworkInputs(const TMVA::Event* ev, Int_t ignoreIndex = -1)
virtual voidGetHelpMessage() const
TMVA::TNeuron*TMVA::MethodANNBase::GetInputNeuron(Int_t index)
const TString&TMVA::MethodBase::GetInternalVarName(Int_t ivar) const
virtual vector<Double_t>TMVA::MethodBase::GetMvaValues(Long64_t firstEvt = 0, Long64_t lastEvt = -1, Bool_t logProgress = false)
Double_tTMVA::MethodANNBase::GetNetworkOutput()
const TString&TMVA::MethodBase::GetOriginalVarName(Int_t ivar) const
TMVA::TNeuron*TMVA::MethodANNBase::GetOutputNeuron(Int_t index = 0)
const TString&TMVA::Configurable::GetReferenceFile() const
const TString&TMVA::MethodBase::GetWeightFileDir() const
Bool_tTMVA::MethodBase::HasTrainingTree() const
Bool_tTMVA::MethodBase::Help() const
Bool_tTMVA::MethodBase::IgnoreEventsWithNegWeightsInTraining() const
Bool_tTMVA::MethodBase::IsConstructedFromWeightFile() const
Bool_tTMVA::MethodBase::IsNormalised() const
Bool_tTMVA::Configurable::LooseOptionCheckingEnabled() const
virtual voidMakeClassSpecific(ostream&, const TString&) const
virtual voidTMVA::MethodBase::MakeClassSpecificHeader(ostream&, const TString& = "") const
voidTObject::MakeZombie()
voidTMVA::MethodBase::NoErrorCalc(Double_t*const err, Double_t*const errUpper)
Int_tTMVA::MethodANNBase::NumCycles()
vector<Int_t>*TMVA::MethodANNBase::ParseLayoutString(TString layerSpec)
voidTMVA::MethodANNBase::PrintMessage(TString message, Bool_t force = kFALSE) const
voidTMVA::Configurable::ResetSetFlag()
voidTMVA::MethodBase::SetNormalised(Bool_t norm)
voidTMVA::MethodBase::SetWeightFileDir(TString fileDir)
voidTMVA::MethodBase::SetWeightFileName(TString)
voidTMVA::MethodBase::Statistics(TMVA::Types::ETreeType treeType, const TString& theVarName, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&, Double_t&)
Bool_tTMVA::MethodBase::TxtWeightsOnly() const
Bool_tTMVA::MethodBase::Verbose() const
voidTMVA::MethodANNBase::WaitForKeyboard()
voidTMVA::Configurable::WriteOptionsReferenceToFile()
private:
voidAdjustSynapseWeights()
voidBackPropagationMinimize(Int_t nEpochs)
voidBFGSMinimize(Int_t nEpochs)
Double_tCalculateEstimator(TMVA::Types::ETreeType treeType = Types::kTraining, Int_t iEpoch = -1)
voidCalculateNeuronDeltas()
voidComputeDEDw()
voidDecaySynapseWeights(Bool_t lateEpoch)
virtual voidDeclareOptions()
Double_tDerivDir(TMatrixD& Dir)
voidGeneticMinimize()
voidGetApproxInvHessian(TMatrixD& InvHessian, bool regulate = true)
Double_tGetCEErr(const TMVA::Event* ev, UInt_t index = 0)
Double_tGetDesiredOutput(const TMVA::Event* ev)
Double_tGetError()
Bool_tGetHessian(TMatrixD& Hessian, TMatrixD& Gamma, TMatrixD& Delta)
Double_tGetMSEErr(const TMVA::Event* ev, UInt_t index = 0)
virtual voidInit()
voidInitializeLearningRates()
Bool_tLineSearch(TMatrixD& Dir, vector<Double_t>& Buffer, Double_t* dError = 0)
virtual voidProcessOptions()
voidSetDir(TMatrixD& Hessian, TMatrixD& Dir)
voidSetDirWeights(vector<Double_t>& Origin, TMatrixD& Dir, Double_t alpha)
voidSetGammaDelta(TMatrixD& Gamma, TMatrixD& Delta, vector<Double_t>& Buffer)
voidShuffle(Int_t* index, Int_t n)
voidSimulateEvent(const TMVA::Event* ev)
voidSteepestDir(TMatrixD& Dir)
voidTrain(Int_t nEpochs)
voidTrainOneEpoch()
voidTrainOneEvent(Int_t ievt)
voidTrainOneEventFast(Int_t ievt, Float_t*& branchVar, Int_t& type)
voidUpdateNetwork(Double_t desired, Double_t eventWeight = 1.)
voidUpdateNetwork(const vector<Float_t>& desired, Double_t eventWeight = 1.)
voidUpdatePriors()
voidUpdateRegulators()
voidUpdateSynapses()

Data Members

public:
TObjArray*TMVA::MethodANNBase::fNetworkTObjArray of TObjArrays representing network
Bool_tTMVA::MethodBase::fSetupCompletedis method setup
const TMVA::Event*TMVA::MethodBase::fTmpEvent! temporary event when testing on a different DataSet than the own one
static TMVA::MethodMLP::ETrainingMethodkBFGS
static TMVA::MethodMLP::ETrainingMethodkBP
static TMVA::MethodMLP::EBPTrainingModekBatch
static TObject::(anonymous)TObject::kBitMask
static TMVA::MethodANNBase::EEstimatorTMVA::MethodANNBase::kCE
static TObject::EStatusBitsTObject::kCanDelete
static TObject::EStatusBitsTObject::kCannotPick
static TMVA::MethodMLP::ETrainingMethodkGA
static TObject::EStatusBitsTObject::kHasUUID
static TObject::EStatusBitsTObject::kInvalidObject
static TObject::(anonymous)TObject::kIsOnHeap
static TObject::EStatusBitsTObject::kIsReferenced
static TMVA::MethodANNBase::EEstimatorTMVA::MethodANNBase::kMSE
static TObject::EStatusBitsTObject::kMustCleanup
static TObject::EStatusBitsTObject::kNoContextMenu
static TObject::(anonymous)TObject::kNotDeleted
static TObject::EStatusBitsTObject::kObjInCanvas
static TObject::(anonymous)TObject::kOverwrite
static TMVA::MethodBase::EWeightFileTypeTMVA::MethodBase::kROOT
static TMVA::MethodMLP::EBPTrainingModekSequential
static TObject::(anonymous)TObject::kSingleKey
static TMVA::MethodBase::EWeightFileTypeTMVA::MethodBase::kTEXT
static TObject::(anonymous)TObject::kWriteDelete
static TObject::(anonymous)TObject::kZombie
protected:
TMVA::TActivation*TMVA::MethodANNBase::fActivationactivation function to be used for hidden layers
TMVA::Types::EAnalysisTypeTMVA::MethodBase::fAnalysisTypemethod-mode : true --> regression, false --> classification
UInt_tTMVA::MethodBase::fBackgroundClassindex of the Background-class
Float_tTMVA::ConvergenceTest::fCurrentValue
vector<TH1*>TMVA::MethodANNBase::fEpochMonHistBepoch monitoring hitograms for background
vector<TH1*>TMVA::MethodANNBase::fEpochMonHistSepoch monitoring hitograms for signal
vector<TH1*>TMVA::MethodANNBase::fEpochMonHistWepoch monitoring hitograms for weights
TMVA::MethodANNBase::EEstimatorTMVA::MethodANNBase::fEstimator
TH1F*TMVA::MethodANNBase::fEstimatorHistTestmonitors convergence of independent test sample
TH1F*TMVA::MethodANNBase::fEstimatorHistTrainmonitors convergence of training sample
TStringTMVA::MethodANNBase::fEstimatorS
boolTMVA::MethodBase::fExitFromTraining
UInt_tTMVA::MethodBase::fIPyCurrentIter
UInt_tTMVA::MethodBase::fIPyMaxIter
TMVA::TActivation*TMVA::MethodANNBase::fIdentityactivation for input and output layers
Float_tTMVA::ConvergenceTest::fImprovement
TMVA::TNeuronInput*TMVA::MethodANNBase::fInputCalculatorinput calculator for all neurons
vector<TString>*TMVA::MethodBase::fInputVarsvector of input variables used in MVA
TMVA::IPythonInteractive*TMVA::MethodBase::fInteractive
TMatrixDTMVA::MethodANNBase::fInvHessianzjh
TMVA::MsgLogger*TMVA::Configurable::fLogger! message logger
vector<Float_t>*TMVA::MethodBase::fMulticlassReturnValholds the return-values for the multiclass classification
TStringTNamed::fNameobject identifier
Int_tTMVA::MethodBase::fNbinsnumber of bins in input variable histograms
Int_tTMVA::MethodBase::fNbinsHnumber of bins in evaluation histograms
Int_tTMVA::MethodBase::fNbinsMVAoutputnumber of bins in MVA output histograms
Int_tTMVA::MethodANNBase::fNcyclesnumber of epochs to train
TStringTMVA::MethodANNBase::fNeuronInputTypename of neuron input calculator class
TStringTMVA::MethodANNBase::fNeuronTypename of neuron activation function class
TMVA::TActivation*TMVA::MethodANNBase::fOutputactivation function to be used for output layers, depending on estimator
Int_tTMVA::MethodANNBase::fRandomSeedrandom seed for initial synapse weights
TMVA::Ranking*TMVA::MethodBase::fRankingpointer to ranking object (created by derived classifiers)
vector<Float_t>*TMVA::MethodBase::fRegressionReturnValholds the return-values for the regression
vector<Int_t>TMVA::MethodANNBase::fRegulatorIdxindex to different priors from every synapses
vector<Double_t>TMVA::MethodANNBase::fRegulatorsthe priors as regulator
TMVA::Results*TMVA::MethodBase::fResults
UInt_tTMVA::MethodBase::fSignalClassindex of the Signal-class
Int_tTMVA::ConvergenceTest::fSteps
TObjArray*TMVA::MethodANNBase::fSynapsesarray of pointers to synapses, no structural data
TStringTNamed::fTitleobject title
boolTMVA::MethodANNBase::fUseRegulatorzjh
TRandom3*TMVA::MethodANNBase::frgenrandom number generator for various uses
private:
TMVA::MethodMLP::EBPTrainingModefBPModebackprop learning mode (sequential or batch)
Int_tfBatchSizebatch size, only matters if in batch learning mode
TStringfBpModeSbackprop learning mode option string (sequential or batch)
boolfCalculateErrorscompute inverse hessian matrix at the end of the training
Double_tfDecayRatedecay rate for above learning rate
vector<pair<Float_t,Float_t> >*fDeviationsFromTargetsdeviation from the targets, event weight
Bool_tfEpochMoncreate and fill epoch-wise monitoring histograms (makes outputfile big!)
Double_tfGA_SC_factorGA settings: SC_factor
Int_tfGA_SC_rateGA settings: SC_rate
Int_tfGA_SC_stepsGA settings: SC_steps
Int_tfGA_nstepsGA settings: number of steps
Int_tfGA_preCalcGA settings: number of pre-calc steps
Double_tfLastAlphaline search variable
Double_tfLearnRatelearning rate for synapse weight adjustments
Double_tfPriorzjh
vector<Double_t>fPriorDevzjh
Int_tfResetStepreset time (how often we clear hessian matrix)
Float_tfSamplingEpochfraction of epochs where sampling is used
Float_tfSamplingFractionfraction of events which is sampled for training
Bool_tfSamplingTestingThe testing sample is sampled
Bool_tfSamplingTrainingThe training sample is sampled
Float_tfSamplingWeightchanging factor for event weights when sampling is turned on
Double_tfTauline search variable
Int_tfTestRatetest for overtraining performed at each #th epochs
TStringfTrainMethodStraining method option param
TMVA::MethodMLP::ETrainingMethodfTrainingMethodmethod of training, BP or GA
Int_tfUpdateLimitzjh
boolfUseRegulatorzjh
Float_tfWeightRangesuppress outliers for the estimator calculation
static const Bool_tfgPRINT_BATCHdebug flags
static const Int_tfgPRINT_ESTIMATOR_INCdebug flags
static const Bool_tfgPRINT_SEQdebug flags

Class Charts

Inheritance Chart:
TMVA::IMethod
TObject
←
TNamed
←
TMVA::Configurable
←
TMVA::MethodBase
←
TMVA::MethodANNBase
TMVA::IFitterTarget
TMVA::ConvergenceTest
←
TMVA::MethodMLP

Function documentation

MethodMLP(const TString& jobName, const TString& methodTitle, TMVA::DataSetInfo& theData, const TString& theOption)
 standard constructors
virtual ~MethodMLP()
Bool_t HasAnalysisType(TMVA::Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets)
void Train()
 for GA
Double_t ComputeEstimator(vector<Double_t>& parameters)
Double_t EstimatorFunction(vector<Double_t>& parameters)
bool HasInverseHessian()
{ return fCalculateErrors; }
Double_t GetMvaValue(Double_t* err = 0, Double_t* errUpper = 0)
void MakeClassSpecific(ostream& , const TString& ) const
 make ROOT-independent C++ class for classifier response (classifier-specific implementation)
void GetHelpMessage() const
 get help message text
void DeclareOptions()
 the option handling methods
void ProcessOptions()
void Train(Int_t nEpochs)
 general helper functions
void Init()
void InitializeLearningRates()
Double_t CalculateEstimator(TMVA::Types::ETreeType treeType = Types::kTraining, Int_t iEpoch = -1)
 used as a measure of success in all minimization techniques
void BFGSMinimize(Int_t nEpochs)
 BFGS functions
void SetGammaDelta(TMatrixD& Gamma, TMatrixD& Delta, vector<Double_t>& Buffer)
void SteepestDir(TMatrixD& Dir)
Bool_t GetHessian(TMatrixD& Hessian, TMatrixD& Gamma, TMatrixD& Delta)
void SetDir(TMatrixD& Hessian, TMatrixD& Dir)
Double_t DerivDir(TMatrixD& Dir)
Bool_t LineSearch(TMatrixD& Dir, vector<Double_t>& Buffer, Double_t* dError = 0)
void ComputeDEDw()
void SimulateEvent(const TMVA::Event* ev)
void SetDirWeights(vector<Double_t>& Origin, TMatrixD& Dir, Double_t alpha)
Double_t GetError()
Double_t GetMSEErr(const TMVA::Event* ev, UInt_t index = 0)
Double_t GetCEErr(const TMVA::Event* ev, UInt_t index = 0)
void BackPropagationMinimize(Int_t nEpochs)
 backpropagation functions
void TrainOneEpoch()
void Shuffle(Int_t* index, Int_t n)
void DecaySynapseWeights(Bool_t lateEpoch)
void TrainOneEvent(Int_t ievt)
Double_t GetDesiredOutput(const TMVA::Event* ev)
void UpdateNetwork(Double_t desired, Double_t eventWeight = 1.)
void UpdateNetwork(const vector<Float_t>& desired, Double_t eventWeight = 1.)
void CalculateNeuronDeltas()
void UpdateSynapses()
void AdjustSynapseWeights()
void TrainOneEventFast(Int_t ievt, Float_t*& branchVar, Int_t& type)
 faster backpropagation
void GeneticMinimize()
 genetic algorithm functions
void GetApproxInvHessian(TMatrixD& InvHessian, bool regulate = true)
void UpdateRegulators()
void UpdatePriors()