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ROOT 6.10/09 Reference Guide |
Multilayer Perceptron class built off of MethodANNBase.
Definition at line 69 of file MethodMLP.h.
Public Types | |
enum | EBPTrainingMode { kSequential =0, kBatch } |
enum | ETrainingMethod { kBP =0, kBFGS, kGA } |
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enum | EEstimator { kMSE =0, kCE } |
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enum | EWeightFileType { kROOT =0, kTEXT } |
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enum | { kIsOnHeap = 0x01000000, kNotDeleted = 0x02000000, kZombie = 0x04000000, kBitMask = 0x00ffffff } |
enum | { kSingleKey = BIT(0), kOverwrite = BIT(1), kWriteDelete = BIT(2) } |
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) } |
Public Member Functions | |
MethodMLP (const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption) | |
standard constructor More... | |
MethodMLP (DataSetInfo &theData, const TString &theWeightFile) | |
constructor from a weight file More... | |
virtual | ~MethodMLP () |
destructor nothing to be done More... | |
Double_t | ComputeEstimator (std::vector< Double_t > ¶meters) |
this function is called by GeneticANN for GA optimization More... | |
Double_t | EstimatorFunction (std::vector< Double_t > ¶meters) |
interface to the estimate More... | |
Double_t | GetMvaValue (Double_t *err=0, Double_t *errUpper=0) |
get the mva value generated by the NN More... | |
virtual Bool_t | HasAnalysisType (Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets) |
MLP can handle classification with 2 classes and regression with one regression-target. More... | |
bool | HasInverseHessian () |
void | Train () |
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MethodANNBase (const TString &jobName, Types::EMVA methodType, const TString &methodTitle, DataSetInfo &theData, const TString &theOption) | |
standard constructor Note: Right now it is an option to choose the neuron input function, but only the input function "sum" leads to weight convergence – otherwise the weights go to nan and lead to an ABORT. More... | |
MethodANNBase (Types::EMVA methodType, DataSetInfo &theData, const TString &theWeightFile) | |
construct the Method from the weight file More... | |
virtual | ~MethodANNBase () |
destructor More... | |
void | AddWeightsXMLTo (void *parent) const |
create XML description of ANN classifier More... | |
const Ranking * | CreateRanking () |
compute ranking of input variables by summing function of weights More... | |
Bool_t | Debug () const |
who the hell makes such strange Debug flags that even use "global pointers".. More... | |
template<typename WriteIterator > | |
void | GetLayerActivation (size_t layer, WriteIterator writeIterator) |
virtual const std::vector< Float_t > & | GetMulticlassValues () |
get the multiclass classification values generated by the NN More... | |
virtual const std::vector< Float_t > & | GetRegressionValues () |
get the regression value generated by the NN More... | |
void | InitANNBase () |
initialize ANNBase object More... | |
virtual void | PrintNetwork () const |
print network representation, for debugging More... | |
virtual void | ReadWeightsFromStream (std::istream &istr) |
destroy/clear the network then read it back in from the weights file More... | |
void | ReadWeightsFromXML (void *wghtnode) |
read MLP from xml weight file More... | |
void | SetActivation (TActivation *activation) |
void | SetNeuronInputCalculator (TNeuronInput *inputCalculator) |
virtual void | WriteMonitoringHistosToFile () const |
write histograms to file More... | |
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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) |
TDirectory * | BaseDir () 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... | |
DataSet * | Data () const |
DataSetInfo & | DataInfo () const |
virtual void | DeclareCompatibilityOptions () |
options that are used ONLY for the READER to ensure backward compatibility they are hence without any effect (the reader is only reading the training options that HAD been used at the training of the .xml weight file at hand More... | |
void | DisableWriting (Bool_t setter) |
Bool_t | DoMulticlass () const |
Bool_t | DoRegression () const |
void | ExitFromTraining () |
Types::EAnalysisType | GetAnalysisType () const |
UInt_t | GetCurrentIter () |
virtual Double_t | GetEfficiency (const TString &, Types::ETreeType, Double_t &err) |
fill background efficiency (resp. More... | |
const Event * | GetEvent () const |
const Event * | GetEvent (const TMVA::Event *ev) const |
const Event * | GetEvent (Long64_t ievt) const |
const Event * | GetEvent (Long64_t ievt, Types::ETreeType type) const |
const std::vector< TMVA::Event * > & | GetEventCollection (Types::ETreeType type) |
returns the event collection (i.e. More... | |
TFile * | GetFile () const |
const TString & | GetInputLabel (Int_t i) const |
const char * | GetInputTitle (Int_t i) const |
const TString & | GetInputVar (Int_t i) const |
TMultiGraph * | GetInteractiveTrainingError () |
const TString & | GetJobName () 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 TString & | GetMethodName () 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_t > | GetMulticlassEfficiency (std::vector< std::vector< Float_t > > &purity) |
virtual std::vector< Float_t > | GetMulticlassTrainingEfficiency (std::vector< std::vector< Float_t > > &purity) |
Double_t | GetMvaValue (const TMVA::Event *const ev, Double_t *err=0, Double_t *errUpper=0) |
const char * | GetName () const |
UInt_t | GetNEvents () const |
temporary event when testing on a different DataSet than the own one More... | |
UInt_t | GetNTargets () const |
UInt_t | GetNvar () const |
UInt_t | GetNVariables () const |
virtual Double_t | GetProba (const Event *ev) |
virtual Double_t | GetProba (Double_t mvaVal, Double_t ap_sig) |
compute likelihood ratio More... | |
const TString | GetProbaName () const |
virtual Double_t | GetRarity (Double_t mvaVal, Types::ESBType reftype=Types::kBackground) const |
compute rarity: \[ R(x) = \int_{[-\infty..x]} { PDF(x') dx' } \] where PDF(x) is the PDF of the classifier's signal or background distribution More... | |
virtual void | GetRegressionDeviation (UInt_t tgtNum, Types::ETreeType type, Double_t &stddev, Double_t &stddev90Percent) const |
const std::vector< Float_t > & | GetRegressionValues (const TMVA::Event *const ev) |
Double_t | GetRMS (Int_t ivar) const |
virtual Double_t | GetROCIntegral (TH1D *histS, TH1D *histB) const |
calculate the area (integral) under the ROC curve as a overall quality measure of the classification More... | |
virtual Double_t | GetROCIntegral (PDF *pdfS=0, PDF *pdfB=0) const |
calculate the area (integral) under the ROC curve as a overall quality measure of the classification More... | |
virtual Double_t | GetSeparation (TH1 *, TH1 *) const |
compute "separation" defined as \[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \] More... | |
virtual Double_t | GetSeparation (PDF *pdfS=0, PDF *pdfB=0) const |
compute "separation" defined as \[ <s2> = \frac{1}{2} \int_{-\infty}^{+\infty} { \frac{(S(x) - B(x))^2}{(S(x) + B(x))} dx } \] More... | |
Double_t | GetSignalReferenceCut () const |
Double_t | GetSignalReferenceCutOrientation () const |
virtual Double_t | GetSignificance () const |
compute significance of mean difference \[ significance = \frac{|<S> - <B>|}{\sqrt{RMS_{S2} + RMS_{B2}}} \] More... | |
const Event * | GetTestingEvent (Long64_t ievt) const |
Double_t | GetTestTime () const |
const TString & | GetTestvarName () const |
virtual Double_t | GetTrainingEfficiency (const TString &) |
const Event * | GetTrainingEvent (Long64_t ievt) const |
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 |
TransformationHandler & | GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) |
const TransformationHandler & | GetTransformationHandler (Bool_t takeReroutedIfAvailable=true) const |
TString | GetWeightFileName () const |
retrieve weight file name More... | |
Double_t | GetXmax (Int_t ivar) const |
Double_t | GetXmin (Int_t ivar) const |
Bool_t | HasMVAPdfs () const |
void | InitIPythonInteractive () |
Bool_t | IsModelPersistence () |
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 () |
virtual void | MakeClass (const TString &classFileName=TString("")) const |
create reader class for method (classification only at present) More... | |
TDirectory * | MethodBaseDir () const |
returns the ROOT directory where all instances of the corresponding MVA method are stored More... | |
virtual std::map< TString, Double_t > | OptimizeTuningParameters (TString fomType="ROCIntegral", TString fitType="FitGA") |
call the Optimizer with the set of parameters and ranges that are meant to be tuned. More... | |
void | PrintHelpMessage () const |
prints out method-specific help method More... | |
void | ProcessSetup () |
process all options the "CheckForUnusedOptions" is done in an independent call, since it may be overridden by derived class (sometimes, eg, fitters are used which can only be implemented during training phase) More... | |
void | ReadStateFromFile () |
Function to write options and weights to file. More... | |
void | ReadStateFromStream (std::istream &tf) |
read the header from the weight files of the different MVA methods More... | |
void | ReadStateFromStream (TFile &rf) |
write reference MVA distributions (and other information) to a ROOT type weight file More... | |
void | ReadStateFromXMLString (const char *xmlstr) |
for reading from memory More... | |
void | RerouteTransformationHandler (TransformationHandler *fTargetTransformation) |
virtual void | Reset () |
virtual void | SetAnalysisType (Types::EAnalysisType type) |
void | SetBaseDir (TDirectory *methodDir) |
void | SetFile (TFile *file) |
void | SetMethodBaseDir (TDirectory *methodDir) |
void | SetMethodDir (TDirectory *methodDir) |
void | SetModelPersistence (Bool_t status) |
void | SetSignalReferenceCut (Double_t cut) |
void | SetSignalReferenceCutOrientation (Double_t cutOrientation) |
void | SetSilentFile (Bool_t status) |
void | SetTestTime (Double_t testTime) |
void | SetTestvarName (const TString &v="") |
void | SetTrainTime (Double_t trainTime) |
virtual void | SetTuneParameters (std::map< TString, Double_t > tuneParameters) |
set the tuning parameters according to the argument This is just a dummy . More... | |
void | SetupMethod () |
setup of methods More... | |
virtual void | TestClassification () |
initialization More... | |
virtual void | TestMulticlass () |
test multiclass classification More... | |
virtual void | TestRegression (Double_t &bias, Double_t &biasT, Double_t &dev, Double_t &devT, Double_t &rms, Double_t &rmsT, Double_t &mInf, Double_t &mInfT, Double_t &corr, Types::ETreeType type) |
calculate <sum-of-deviation-squared> of regression output versus "true" value from test sample More... | |
bool | TrainingEnded () |
void | TrainMethod () |
virtual void | WriteEvaluationHistosToFile (Types::ETreeType treetype) |
writes all MVA evaluation histograms to file More... | |
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... | |
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IMethod () | |
virtual | ~IMethod () |
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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 > | |
OptionBase * | DeclareOptionRef (T &ref, const TString &name, const TString &desc="") |
template<class T > | |
OptionBase * | DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc="") |
template<class T > | |
TMVA::OptionBase * | DeclareOptionRef (T &ref, const TString &name, const TString &desc) |
template<class T > | |
TMVA::OptionBase * | DeclareOptionRef (T *&ref, Int_t size, const TString &name, const TString &desc) |
const char * | GetConfigDescription () const |
const char * | GetConfigName () const |
const TString & | GetOptions () const |
MsgLogger & | Log () 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... | |
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TNamed () | |
TNamed (const char *name, const char *title) | |
TNamed (const TString &name, const TString &title) | |
TNamed (const TNamed &named) | |
TNamed copy ctor. More... | |
virtual | ~TNamed () |
virtual void | Clear (Option_t *option="") |
Set name and title to empty strings (""). More... | |
virtual TObject * | Clone (const char *newname="") const |
Make a clone of an object using the Streamer facility. More... | |
virtual Int_t | Compare (const TObject *obj) const |
Compare two TNamed objects. More... | |
virtual void | Copy (TObject &named) const |
Copy this to obj. More... | |
virtual void | FillBuffer (char *&buffer) |
Encode TNamed into output buffer. More... | |
virtual const char * | GetTitle () const |
Returns title of object. More... | |
virtual ULong_t | Hash () const |
Return hash value for this object. More... | |
virtual Bool_t | IsSortable () const |
virtual void | ls (Option_t *option="") const |
List TNamed name and title. More... | |
TNamed & | operator= (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... | |
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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... | |
virtual const char * | ClassName () const |
Returns name of class to which the object belongs. More... | |
virtual void | Delete (Option_t *option="") |
Delete this object. More... | |
virtual Int_t | DistancetoPrimitive (Int_t px, Int_t py) |
Computes distance from point (px,py) to the object. More... | |
virtual void | Draw (Option_t *option="") |
Default Draw method for all objects. More... | |
virtual void | DrawClass () const |
Draw class inheritance tree of the class to which this object belongs. More... | |
virtual TObject * | DrawClone (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 TObject * | FindObject (const char *name) const |
Must be redefined in derived classes. More... | |
virtual TObject * | FindObject (const TObject *obj) const |
Must be redefined in derived classes. More... | |
virtual Option_t * | GetDrawOption () const |
Get option used by the graphics system to draw this object. More... | |
virtual const char * | GetIconName () const |
Returns mime type name of object. More... | |
virtual char * | GetObjectInfo (Int_t px, Int_t py) const |
Returns string containing info about the object at position (px,py). More... | |
virtual Option_t * | GetOption () const |
virtual UInt_t | GetUniqueID () const |
Return the unique object id. More... | |
virtual Bool_t | HandleTimer (TTimer *timer) |
Execute action in response of a timer timing out. More... | |
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... | |
Bool_t | IsOnHeap () const |
Bool_t | IsZombie () const |
void | MayNotUse (const char *method) const |
Use this method to signal that a method (defined in a base class) may not be called in a derived class (in principle against good design since a child class should not provide less functionality than its parent, however, sometimes it is necessary). More... | |
virtual Bool_t | Notify () |
This method must be overridden to handle object notification. More... | |
void | Obsolete (const char *method, const char *asOfVers, const char *removedFromVers) const |
Use this method to declare a method obsolete. More... | |
void | operator delete (void *ptr) |
Operator delete. More... | |
void | operator delete[] (void *ptr) |
Operator delete []. More... | |
void * | operator new (size_t sz) |
void * | operator new (size_t sz, void *vp) |
void * | operator new[] (size_t sz) |
void * | operator new[] (size_t sz, void *vp) |
TObject & | operator= (const TObject &rhs) |
TObject assignment operator. More... | |
virtual void | Paint (Option_t *option="") |
This method must be overridden if a class wants to paint itself. More... | |
virtual void | Pop () |
Pop on object drawn in a pad to the top of the display list. More... | |
virtual Int_t | Read (const char *name) |
Read contents of object with specified name from the current directory. More... | |
virtual void | RecursiveRemove (TObject *obj) |
Recursively remove this object from a list. More... | |
void | ResetBit (UInt_t f) |
virtual void | SaveAs (const char *filename="", Option_t *option="") const |
Save this object in the file specified by filename. More... | |
virtual void | SavePrimitive (std::ostream &out, Option_t *option="") |
Save a primitive as a C++ statement(s) on output stream "out". More... | |
void | SetBit (UInt_t f, Bool_t set) |
Set or unset the user status bits as specified in f. More... | |
void | SetBit (UInt_t f) |
virtual void | SetDrawOption (Option_t *option="") |
Set drawing option for object. More... | |
virtual void | SetUniqueID (UInt_t uid) |
Set the unique object id. More... | |
virtual void | SysError (const char *method, const char *msgfmt,...) const |
Issue system error message. More... | |
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... | |
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IFitterTarget () | |
constructor More... | |
virtual | ~IFitterTarget () |
virtual void | ProgressNotifier (TString, TString) |
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ConvergenceTest () | |
constructor More... | |
~ConvergenceTest () | |
destructor More... | |
Float_t | GetCurrentValue () |
Bool_t | HasConverged (Bool_t withinConvergenceBand=kFALSE) |
gives back true if the last "steps" steps have lead to an improvement of the "fitness" of the "individuals" of at least "improvement" More... | |
Float_t | Progress () |
returns a float from 0 (just started) to 1 (finished) More... | |
void | ResetConvergenceCounter () |
void | SetConvergenceParameters (Int_t steps, Double_t improvement) |
void | SetCurrentValue (Float_t value) |
Float_t | SpeedControl (UInt_t ofSteps) |
this function provides the ability to change the learning rate according to the success of the last generations. More... | |
Protected Member Functions | |
void | GetHelpMessage () const |
get help message text More... | |
void | MakeClassSpecific (std::ostream &, const TString &) const |
write specific classifier response More... | |
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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 | CreateWeightMonitoringHists (const TString &bulkname, std::vector< TH1 *> *hv=0) const |
void | ForceNetworkCalculations () |
calculate input values to each neuron 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... | |
TNeuron * | GetInputNeuron (Int_t index) |
Double_t | GetNetworkOutput () |
TNeuron * | GetOutputNeuron (Int_t index=0) |
Int_t | NumCycles () |
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... | |
void | PrintMessage (TString message, Bool_t force=kFALSE) const |
print messages, turn off printing by setting verbose and debug flag appropriately More... | |
void | WaitForKeyboard () |
wait for keyboard input, for debugging More... | |
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const TString & | GetInternalVarName (Int_t ivar) const |
virtual std::vector< Double_t > | GetMvaValues (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 TString & | GetOriginalVarName (Int_t ivar) const |
const TString & | GetWeightFileDir () const |
Bool_t | HasTrainingTree () const |
Bool_t | Help () const |
Bool_t | IgnoreEventsWithNegWeightsInTraining () const |
Bool_t | IsConstructedFromWeightFile () const |
Bool_t | IsNormalised () const |
virtual void | MakeClassSpecificHeader (std::ostream &, const TString &="") const |
void | NoErrorCalc (Double_t *const err, Double_t *const errUpper) |
virtual void | ReadWeightsFromStream (TFile &) |
void | SetNormalised (Bool_t norm) |
void | SetWeightFileDir (TString fileDir) |
set directory of weight file More... | |
void | SetWeightFileName (TString) |
set the weight file name (depreciated) More... | |
void | Statistics (Types::ETreeType treeType, const TString &theVarName, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &, Double_t &) |
calculates rms,mean, xmin, xmax of the event variable this can be either done for the variables as they are or for normalised variables (in the range of 0-1) if "norm" is set to kTRUE More... | |
Bool_t | TxtWeightsOnly () const |
Bool_t | Verbose () const |
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void | EnableLooseOptions (Bool_t b=kTRUE) |
const TString & | GetReferenceFile () 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... | |
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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 | AdjustSynapseWeights () |
just adjust the synapse weights (should be called in batch mode) More... | |
void | BackPropagationMinimize (Int_t nEpochs) |
minimize estimator / train network with back propagation algorithm More... | |
void | BFGSMinimize (Int_t nEpochs) |
train network with BFGS algorithm More... | |
Double_t | CalculateEstimator (Types::ETreeType treeType=Types::kTraining, Int_t iEpoch=-1) |
calculate the estimator that training is attempting to minimize More... | |
void | CalculateNeuronDeltas () |
have each neuron calculate its delta by back propagation More... | |
void | ComputeDEDw () |
void | DecaySynapseWeights (Bool_t lateEpoch) |
decay synapse weights in last 10 epochs, lower learning rate even more to find a good minimum More... | |
void | DeclareOptions () |
define the options (their key words) that can be set in the option string More... | |
Double_t | DerivDir (TMatrixD &Dir) |
void | GeneticMinimize () |
create genetics class similar to GeneticCut give it vector of parameter ranges (parameters = weights) link fitness function of this class to ComputeEstimator instantiate GA (see MethodCuts) run it then this should exist for GA, Minuit and random sampling More... | |
void | GetApproxInvHessian (TMatrixD &InvHessian, bool regulate=true) |
Double_t | GetCEErr (const Event *ev, UInt_t index=0) |
Double_t | GetDesiredOutput (const Event *ev) |
get the desired output of this event More... | |
Double_t | GetError () |
Bool_t | GetHessian (TMatrixD &Hessian, TMatrixD &Gamma, TMatrixD &Delta) |
Double_t | GetMSEErr (const Event *ev, UInt_t index=0) |
void | Init () |
default initializations More... | |
void | InitializeLearningRates () |
initialize learning rates of synapses, used only by back propagation More... | |
Bool_t | LineSearch (TMatrixD &Dir, std::vector< Double_t > &Buffer, Double_t *dError=0) |
void | ProcessOptions () |
process user options More... | |
void | SetDir (TMatrixD &Hessian, TMatrixD &Dir) |
void | SetDirWeights (std::vector< Double_t > &Origin, TMatrixD &Dir, Double_t alpha) |
void | SetGammaDelta (TMatrixD &Gamma, TMatrixD &Delta, std::vector< Double_t > &Buffer) |
void | Shuffle (Int_t *index, Int_t n) |
Input: More... | |
void | SimulateEvent (const Event *ev) |
void | SteepestDir (TMatrixD &Dir) |
void | Train (Int_t nEpochs) |
void | TrainOneEpoch () |
train network over a single epoch/cycle of events More... | |
void | TrainOneEvent (Int_t ievt) |
train network over a single event this uses the new event model More... | |
void | TrainOneEventFast (Int_t ievt, Float_t *&branchVar, Int_t &type) |
fast per-event training More... | |
void | UpdateNetwork (Double_t desired, Double_t eventWeight=1.0) |
update the network based on how closely the output matched the desired output More... | |
void | UpdateNetwork (const std::vector< Float_t > &desired, Double_t eventWeight=1.0) |
update the network based on how closely the output matched the desired output More... | |
void | UpdatePriors () |
void | UpdateRegulators () |
void | UpdateSynapses () |
update synapse error fields and adjust the weights (if in sequential mode) More... | |
Static Private Attributes | |
static const Bool_t | fgPRINT_BATCH = kFALSE |
static const Int_t | fgPRINT_ESTIMATOR_INC = 10 |
static const Bool_t | fgPRINT_SEQ = kFALSE |
#include <TMVA/MethodMLP.h>
enum TMVA::MethodMLP::EBPTrainingMode |
Enumerator | |
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kSequential | |
kBatch |
Definition at line 92 of file MethodMLP.h.
enum TMVA::MethodMLP::ETrainingMethod |
Enumerator | |
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kBP | |
kBFGS | |
kGA |
Definition at line 91 of file MethodMLP.h.
TMVA::MethodMLP::MethodMLP | ( | const TString & | jobName, |
const TString & | methodTitle, | ||
DataSetInfo & | theData, | ||
const TString & | theOption | ||
) |
standard constructor
Definition at line 92 of file MethodMLP.cxx.
TMVA::MethodMLP::MethodMLP | ( | DataSetInfo & | theData, |
const TString & | theWeightFile | ||
) |
constructor from a weight file
Definition at line 117 of file MethodMLP.cxx.
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virtual |
destructor nothing to be done
Definition at line 140 of file MethodMLP.cxx.
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just adjust the synapse weights (should be called in batch mode)
Definition at line 1438 of file MethodMLP.cxx.
minimize estimator / train network with back propagation algorithm
Definition at line 1041 of file MethodMLP.cxx.
train network with BFGS algorithm
Definition at line 491 of file MethodMLP.cxx.
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calculate the estimator that training is attempting to minimize
Definition at line 294 of file MethodMLP.cxx.
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have each neuron calculate its delta by back propagation
Definition at line 1332 of file MethodMLP.cxx.
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Definition at line 701 of file MethodMLP.cxx.
this function is called by GeneticANN for GA optimization
Definition at line 1397 of file MethodMLP.cxx.
decay synapse weights in last 10 epochs, lower learning rate even more to find a good minimum
Definition at line 1212 of file MethodMLP.cxx.
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privatevirtual |
define the options (their key words) that can be set in the option string
know options:
Reimplemented from TMVA::MethodANNBase.
Definition at line 197 of file MethodMLP.cxx.
Definition at line 829 of file MethodMLP.cxx.
interface to the estimate
Implements TMVA::IFitterTarget.
Definition at line 1389 of file MethodMLP.cxx.
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private |
create genetics class similar to GeneticCut give it vector of parameter ranges (parameters = weights) link fitness function of this class to ComputeEstimator instantiate GA (see MethodCuts) run it then this should exist for GA, Minuit and random sampling
Definition at line 1360 of file MethodMLP.cxx.
Definition at line 1512 of file MethodMLP.cxx.
Definition at line 1024 of file MethodMLP.cxx.
get the desired output of this event
Definition at line 1281 of file MethodMLP.cxx.
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Definition at line 970 of file MethodMLP.cxx.
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protectedvirtual |
get help message text
typical length of text line: "|--------------------------------------------------------------|"
Implements TMVA::IMethod.
Definition at line 1719 of file MethodMLP.cxx.
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Definition at line 791 of file MethodMLP.cxx.
Definition at line 1007 of file MethodMLP.cxx.
get the mva value generated by the NN
Reimplemented from TMVA::MethodANNBase.
Definition at line 1553 of file MethodMLP.cxx.
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MLP can handle classification with 2 classes and regression with one regression-target.
Implements TMVA::IMethod.
Definition at line 154 of file MethodMLP.cxx.
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inline |
Definition at line 94 of file MethodMLP.h.
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initialize learning rates of synapses, used only by back propagation
Definition at line 280 of file MethodMLP.cxx.
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Definition at line 844 of file MethodMLP.cxx.
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write specific classifier response
Reimplemented from TMVA::MethodANNBase.
Definition at line 1708 of file MethodMLP.cxx.
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process user options
Reimplemented from TMVA::MethodANNBase.
Definition at line 249 of file MethodMLP.cxx.
Definition at line 812 of file MethodMLP.cxx.
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Definition at line 954 of file MethodMLP.cxx.
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Definition at line 676 of file MethodMLP.cxx.
Input:
This method is used for sequential training
Definition at line 1194 of file MethodMLP.cxx.
Definition at line 738 of file MethodMLP.cxx.
Definition at line 778 of file MethodMLP.cxx.
Implements TMVA::MethodANNBase.
Definition at line 144 of file MethodMLP.cxx.
Definition at line 440 of file MethodMLP.cxx.
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private |
train network over a single epoch/cycle of events
Definition at line 1146 of file MethodMLP.cxx.
train network over a single event this uses the new event model
Definition at line 1262 of file MethodMLP.cxx.
fast per-event training
Definition at line 1226 of file MethodMLP.cxx.
update the network based on how closely the output matched the desired output
Definition at line 1290 of file MethodMLP.cxx.
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private |
update the network based on how closely the output matched the desired output
Definition at line 1306 of file MethodMLP.cxx.
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private |
Definition at line 1458 of file MethodMLP.cxx.
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Definition at line 1472 of file MethodMLP.cxx.
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update synapse error fields and adjust the weights (if in sequential mode)
Definition at line 1416 of file MethodMLP.cxx.
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private |
Definition at line 192 of file MethodMLP.h.
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Definition at line 190 of file MethodMLP.h.
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Definition at line 191 of file MethodMLP.h.
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Definition at line 165 of file MethodMLP.h.
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Definition at line 189 of file MethodMLP.h.
Definition at line 204 of file MethodMLP.h.
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Definition at line 194 of file MethodMLP.h.
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Definition at line 197 of file MethodMLP.h.
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Definition at line 198 of file MethodMLP.h.
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Definition at line 201 of file MethodMLP.h.
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Definition at line 200 of file MethodMLP.h.
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Definition at line 199 of file MethodMLP.h.
Definition at line 217 of file MethodMLP.h.
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Definition at line 215 of file MethodMLP.h.
Definition at line 216 of file MethodMLP.h.
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Definition at line 183 of file MethodMLP.h.
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Definition at line 188 of file MethodMLP.h.
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Definition at line 166 of file MethodMLP.h.
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Definition at line 167 of file MethodMLP.h.
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Definition at line 185 of file MethodMLP.h.
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Definition at line 177 of file MethodMLP.h.
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Definition at line 176 of file MethodMLP.h.
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Definition at line 180 of file MethodMLP.h.
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Definition at line 179 of file MethodMLP.h.
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Definition at line 178 of file MethodMLP.h.
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Definition at line 184 of file MethodMLP.h.
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Definition at line 193 of file MethodMLP.h.
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Definition at line 173 of file MethodMLP.h.
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Definition at line 174 of file MethodMLP.h.
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Definition at line 171 of file MethodMLP.h.
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Definition at line 164 of file MethodMLP.h.
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Definition at line 206 of file MethodMLP.h.