29#ifndef ROOT_TMVA_MethodTMlpANN
30#define ROOT_TMVA_MethodTMlpANN
64 void Train(
void )
override;
119 void Init(
void )
override;
#define ClassDefOverride(name, id)
ROOT::Detail::TRangeCast< T, true > TRangeDynCast
TRangeDynCast is an adapter class that allows the typed iteration through a TCollection.
Option_t Option_t TPoint TPoint const char GetTextMagnitude GetFillStyle GetLineColor GetLineWidth GetMarkerStyle GetTextAlign GetTextColor GetTextSize void char Point_t Rectangle_t WindowAttributes_t Float_t Float_t Float_t Int_t Int_t UInt_t UInt_t Rectangle_t Int_t Int_t Window_t TString Int_t GCValues_t GetPrimarySelectionOwner GetDisplay GetScreen GetColormap GetNativeEvent const char const char dpyName wid window const char font_name cursor keysym reg const char only_if_exist regb h Point_t winding char text const char depth char const char Int_t count const char ColorStruct_t color const char Pixmap_t Pixmap_t PictureAttributes_t attr const char char ret_data h unsigned char height h Atom_t Int_t ULong_t ULong_t unsigned char prop_list Atom_t Atom_t Atom_t Time_t type
Class that contains all the data information.
Virtual base Class for all MVA method.
void ReadWeightsFromStream(std::istream &) override=0
This is the TMVA TMultiLayerPerceptron interface class.
Double_t fValidationFraction
fraction of events in training tree used for cross validation
void ReadWeightsFromStream(std::istream &istr) override
read weights from stream since the MLP can not read from the stream, we 1st: write the weights to tem...
void MakeClass(const TString &classFileName=TString("")) const override
create reader class for classifier -> overwrites base class function create specific class for TMulti...
void Train(void) override
performs TMlpANN training available learning methods:
const Ranking * CreateRanking() override
TString fLearningMethod
the learning method (given via option string)
TString fMLPBuildOptions
option string to build the mlp
void ReadWeightsFromXML(void *wghtnode) override
rebuild temporary textfile from xml weightfile and load this file into MLP
void AddWeightsXMLTo(void *parent) const override
write weights to xml file
void DeclareOptions() override
define the options (their key words) that can be set in the option string
TTree * fLocalTrainingTree
local copy of training tree
void CreateMLPOptions(TString)
translates options from option string into TMlpANN language
void ProcessOptions() override
builds the neural network as specified by the user
MethodTMlpANN(const TString &jobName, const TString &methodTitle, DataSetInfo &theData, const TString &theOption="3000:N-1:N-2")
standard constructor
TMultiLayerPerceptron * fMLP
the TMLP
Double_t GetMvaValue(Double_t *err=nullptr, Double_t *errUpper=nullptr) override
calculate the value of the neural net for the current event
TString fLayerSpec
Layer specification option.
void Init(void) override
default initialisations
virtual ~MethodTMlpANN(void)
destructor
Int_t fNcycles
number of training cycles
Bool_t HasAnalysisType(Types::EAnalysisType type, UInt_t numberClasses, UInt_t numberTargets) override
TMlpANN can handle classification with 2 classes.
void GetHelpMessage() const override
get help message text
void MakeClassSpecific(std::ostream &, const TString &) const override
write specific classifier response nothing to do here - all taken care of by TMultiLayerPerceptron
TString fHiddenLayer
string containing the hidden layer structure
void SetHiddenLayer(TString hiddenlayer="")
Ranking for variables in method (implementation)
This class describes a neural network.
A TTree represents a columnar dataset.
create variable transformations