// @(#)root/tmva $Id$ // Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss, Jan Therhaag /********************************************************************************** * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * * Package: TMVA * * Class : Event * * Web : http://tmva.sourceforge.net * * * * Description: * * Event container * * * * Authors (alphabetical): * * Andreas Hoecker - CERN, Switzerland * * Joerg Stelzer - CERN, Switzerland * * Peter Speckmayer - CERN, Switzerland * * Jan Therhaag - U of Bonn, Germany * * Helge Voss - MPI-K Heidelberg, Germany * * * * Copyright (c) 2005-2011: * * CERN, Switzerland * * U. of Victoria, Canada * * MPI-K Heidelberg, Germany * * U. of Bonn, Germany * * * * Redistribution and use in source and binary forms, with or without * * modification, are permitted according to the terms listed in LICENSE * * (http://mva.sourceforge.net/license.txt) * **********************************************************************************/ #ifndef ROOT_TMVA_Event #define ROOT_TMVA_Event #include #include #ifndef ROOT_Rtypes #include "Rtypes.h" #endif #ifndef ROOT_TMVA_Types #include "TMVA/Types.h" #endif class TCut; namespace TMVA { class Event; std::ostream& operator<<( std::ostream& os, const Event& event ); class Event { friend std::ostream& operator<<( std::ostream& os, const Event& event ); public: // constructors Event(); Event( const Event& ); explicit Event( const std::vector& values, const std::vector& targetValues, const std::vector& spectatorValues, UInt_t theClass = 0, Double_t weight = 1.0, Double_t boostweight = 1.0 ); explicit Event( const std::vector& values, const std::vector& targetValues, UInt_t theClass = 0, Double_t weight = 1.0, Double_t boostweight = 1.0 ); explicit Event( const std::vector&, UInt_t theClass, Double_t weight = 1.0, Double_t boostweight = 1.0 ); explicit Event( const std::vector*&, UInt_t nvar ); ~Event(); // accessors Bool_t IsDynamic() const {return fDynamic; } // Double_t GetWeight() const { return fWeight*fBoostWeight; } Double_t GetWeight() const; Double_t GetOriginalWeight() const { return fWeight; } Double_t GetBoostWeight() const { return TMath::Max(Double_t(0.0001),fBoostWeight); } UInt_t GetClass() const { return fClass; } UInt_t GetNVariables() const; UInt_t GetNTargets() const; UInt_t GetNSpectators() const; Float_t GetValue( UInt_t ivar) const; std::vector& GetValues() { //For a detailed explanation, please see the heading "Avoid Duplication in const and Non-const Member Function," on p. 23, in Item 3 "Use const whenever possible," in Effective C++, 3d ed by Scott Meyers, ISBN-13: 9780321334879. // http://stackoverflow.com/questions/123758/how-do-i-remove-code-duplication-between-similar-const-and-non-const-member-func return const_cast&>( static_cast(*this).GetValues() ); } const std::vector& GetValues() const; Float_t GetTarget( UInt_t itgt ) const { return fTargets.at(itgt); } std::vector& GetTargets() { return fTargets; } const std::vector& GetTargets() const { return fTargets; } Float_t GetSpectator( UInt_t ivar) const; std::vector& GetSpectators() { return fSpectators; } const std::vector& GetSpectators() const { return fSpectators; } void SetWeight ( Double_t w ) { fWeight=w; } void SetBoostWeight ( Double_t w ) const { fDoNotBoost ? fDoNotBoost = kFALSE : fBoostWeight=w; } void ScaleBoostWeight ( Double_t s ) const { fDoNotBoost ? fDoNotBoost = kFALSE : fBoostWeight *= s; } void SetClass ( UInt_t t ) { fClass=t; } void SetVal ( UInt_t ivar, Float_t val ); void SetTarget ( UInt_t itgt, Float_t value ); void SetSpectator ( UInt_t ivar, Float_t value ); void SetVariableArrangement( std::vector* const m ) const; void SetDoNotBoost () const { fDoNotBoost = kTRUE; } static void ClearDynamicVariables() {} void CopyVarValues( const Event& other ); void Print ( std::ostream & o ) const; static void SetIsTraining(Bool_t); static void SetIgnoreNegWeightsInTraining(Bool_t); private: static Bool_t fgIsTraining; // mark if we are in an actual training or "evaluation/testing" phase --> ignoreNegWeights only in actual training ! static Bool_t fgIgnoreNegWeightsInTraining; mutable std::vector fValues; // the event values ; mutable, to be able to copy the dynamic values in there mutable std::vector fValuesRearranged; // the event values ; mutable, to be able to copy the dynamic values in there mutable std::vector* fValuesDynamic; // the event values std::vector fTargets; // target values for regression mutable std::vector fSpectators; // "visisting" variables not used in MVAs ; mutable, to be able to copy the dynamic values in there mutable std::vector* fVariableArrangement; // needed for MethodCategories, where we can train on other than the main variables UInt_t fClass; // class number Double_t fWeight; // event weight (product of global and individual weights) mutable Double_t fBoostWeight; // internal weight to be set by boosting algorithm Bool_t fDynamic; // is set when the dynamic values are taken mutable Bool_t fDoNotBoost; // mark event as not to be boosted (used to compensate for events with negative event weights }; } #endif