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MCFitter.h
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1// @(#)root/tmva $Id$
2// Author: Andreas Hoecker, Peter Speckmayer, Joerg Stelzer, Helge Voss
3
4/**********************************************************************************
5 * Project: TMVA - a Root-integrated toolkit for multivariate data analysis *
6 * Package: TMVA *
7 * Class : MCFitter *
8 * *
9 * *
10 * Description: *
11 * Fitter using Monte Carlo sampling of parameters *
12 * *
13 * Authors (alphabetical): *
14 * Andreas Hoecker <Andreas.Hocker@cern.ch> - CERN, Switzerland *
15 * Peter Speckmayer <speckmay@mail.cern.ch> - CERN, Switzerland *
16 * Joerg Stelzer <Joerg.Stelzer@cern.ch> - CERN, Switzerland *
17 * Helge Voss <Helge.Voss@cern.ch> - MPI-K Heidelberg, Germany *
18 * *
19 * Copyright (c) 2005: *
20 * CERN, Switzerland *
21 * MPI-K Heidelberg, Germany *
22 * *
23 * Redistribution and use in source and binary forms, with or without *
24 * modification, are permitted according to the terms listed in LICENSE *
25 * (see tmva/doc/LICENSE) *
26 **********************************************************************************/
27
28#ifndef ROOT_TMVA_MCFitter
29#define ROOT_TMVA_MCFitter
30
31//////////////////////////////////////////////////////////////////////////
32// //
33// MCFitter //
34// //
35// Fitter using Monte Carlo sampling of parameters //
36// //
37//////////////////////////////////////////////////////////////////////////
38
39#include "TMVA/FitterBase.h"
40#include <vector>
41
42namespace TMVA {
43
44 class MCFitter : public FitterBase {
45
46 public:
47
49 const std::vector<TMVA::Interval*>& ranges, const TString& theOption );
50
51 virtual ~MCFitter() {}
52
53 void SetParameters( Int_t cycles );
54
55 Double_t Run( std::vector<Double_t>& pars );
56
57 private:
58
59 void DeclareOptions();
60
61 Int_t fSamples; ///< number of MC samples
62 Double_t fSigma; ///< new samples are generated randomly with a gaussian probability with fSigma around the current best value
63 UInt_t fSeed; ///< Seed for the random generator (0 takes random seeds)
64
65 ClassDef(MCFitter,0); // Fitter using Monte Carlo sampling of parameters
66 };
67
68} // namespace TMVA
69
70#endif
71
72
double Double_t
Definition RtypesCore.h:59
#define ClassDef(name, id)
Definition Rtypes.h:342
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 target
char name[80]
Definition TGX11.cxx:110
Base class for TMVA fitters.
Definition FitterBase.h:51
Double_t Run()
estimator function interface for fitting
Interface for a fitter 'target'.
Fitter using Monte Carlo sampling of parameters.
Definition MCFitter.h:44
UInt_t fSeed
Seed for the random generator (0 takes random seeds)
Definition MCFitter.h:63
void SetParameters(Int_t cycles)
set MC fitter configuration parameters
Definition MCFitter.cxx:78
virtual ~MCFitter()
Definition MCFitter.h:51
Double_t fSigma
new samples are generated randomly with a gaussian probability with fSigma around the current best va...
Definition MCFitter.h:62
void DeclareOptions()
Declare MCFitter options.
Definition MCFitter.cxx:67
Int_t fSamples
number of MC samples
Definition MCFitter.h:61
Basic string class.
Definition TString.h:139
create variable transformations