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0001 // @(#)root/tmva $Id$ 0002 // Author: Asen Christov 0003 0004 /********************************************************************************** 0005 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis * 0006 * Package: TMVA * 0007 * Class : KDEKernel * 0008 * * 0009 * * 0010 * Description: * 0011 * The Probability Density Functions (PDFs) used for the Likelihood analysis * 0012 * can suffer from low statistics of the training samples. This can cause * 0013 * the PDFs to fluctuate instead to be smooth. Nonparamatric Kernel Density * 0014 * Estimation is one of the methods to produce "smooth" PDFs. * 0015 * * 0016 * Authors (alphabetical): * 0017 * Asen Christov <christov@physik.uni-freiburg.de> - Freiburg U., Germany * 0018 * * 0019 * Copyright (c) 2007: * 0020 * CERN, Switzerland * 0021 * MPI-K Heidelberg, Germany * 0022 * Freiburg U., Germany * 0023 * * 0024 * Redistribution and use in source and binary forms, with or without * 0025 * modification, are permitted according to the terms listed in LICENSE * 0026 * (see tmva/doc/LICENSE) * 0027 **********************************************************************************/ 0028 0029 #ifndef ROOT_TMVA_KDEKernel 0030 #define ROOT_TMVA_KDEKernel 0031 0032 ////////////////////////////////////////////////////////////////////////// 0033 // // 0034 // KDEKernel // 0035 // // 0036 // KDE Kernel for "smoothing" the PDFs // 0037 // // 0038 ////////////////////////////////////////////////////////////////////////// 0039 0040 #include "Rtypes.h" 0041 0042 class TH1; 0043 class TH1F; 0044 class TF1; 0045 0046 namespace TMVA { 0047 0048 class MsgLogger; 0049 0050 class KDEKernel { 0051 0052 public: 0053 0054 enum EKernelType { kNone = 0, kGauss = 1 }; 0055 enum EKernelIter { kNonadaptiveKDE = 1, kAdaptiveKDE = 2 }; 0056 enum EKernelBorder { kNoTreatment = 1, kKernelRenorm = 2, kSampleMirror = 3 }; 0057 0058 public: 0059 0060 KDEKernel( EKernelIter kiter = kNonadaptiveKDE, const TH1* hist = nullptr, Float_t lower_edge=0., Float_t upper_edge=1., EKernelBorder kborder = kNoTreatment, Float_t FineFactor = 1.); 0061 0062 virtual ~KDEKernel( void ); 0063 0064 // calculates the integral of the Kernel function in the given bin. 0065 Float_t GetBinKernelIntegral(Float_t lowr, Float_t highr, Float_t mean, Int_t binnum); 0066 0067 // sets the type of Kernel to be used (Default 1 mean Gaussian) 0068 void SetKernelType( EKernelType ktype = kGauss ); 0069 0070 // modified name (remove TMVA::) 0071 const char* GetName() const { return "KDEKernel"; } 0072 0073 private: 0074 0075 Float_t fSigma; ///< Width of the Kernel function 0076 EKernelIter fIter; ///< iteration number 0077 Float_t fLowerEdge; ///< the lower edge of the PDF 0078 Float_t fUpperEdge; ///< the upper edge of the PDF 0079 Float_t fFineFactor; ///< fine tuning factor for Adaptive KDE: factor to multiply the "width" of the Kernel function 0080 TF1 *fKernel_integ; ///< the integral of the Kernel function 0081 EKernelBorder fKDEborder; ///< The method to take care about "border" effects 0082 TH1F *fHist; ///< copy of input histogram 0083 TH1F *fFirstIterHist; ///< histogram to be filled in the hidden iteration 0084 TH1F *fSigmaHist; ///< contains the Sigmas Widths for adaptive KDE 0085 Bool_t fHiddenIteration; ///< Defines if whats currently running is the 0086 // (first) hidden iteration when doing adaptive KDE 0087 0088 mutable MsgLogger* fLogger; ///<! message logger 0089 MsgLogger& Log() const { return *fLogger; } 0090 0091 ClassDef(KDEKernel,0); // Kernel density estimator for PDF smoothing 0092 0093 };// namespace TMVA 0094 } 0095 #endif // KDEKernel_H
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