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revision 25485, Mon Sep 22 07:52:52 2008 UTC revision 25486, Mon Sep 22 12:43:03 2008 UTC
# Line 16  Line 16 
16  #include "Fit/PoissonLikelihoodFCN.h"  #include "Fit/PoissonLikelihoodFCN.h"
17  #include "Fit/LogLikelihoodFCN.h"  #include "Fit/LogLikelihoodFCN.h"
18  #include "Math/Minimizer.h"  #include "Math/Minimizer.h"
19    #include "Math/MinimizerOptions.h"
20    #include "Fit/MinimizerControlParams.h"
21  #include "Fit/BinData.h"  #include "Fit/BinData.h"
22  #include "Fit/UnBinData.h"  #include "Fit/UnBinData.h"
23  #include "Math/Error.h"  #include "Math/Error.h"
# Line 41  Line 43 
43  Fitter::~Fitter()  Fitter::~Fitter()
44  {  {
45     // Destructor implementation.     // Destructor implementation.
46       // since function pointer is normally own by FitResult. delete only if fit result is empty
47     if (fFunc != 0) delete fFunc;     if (fFunc && fResult.FittedFunction() == 0) delete fFunc;
48  }  }
49    
50  Fitter::Fitter(const Fitter &)  Fitter::Fitter(const Fitter & rhs)
51  {  {
52     // Implementation of copy constructor.     // Implementation of copy constructor.
53       // copy FitResult, FitCOnfig and clone fit function
54       (*this) = rhs;
55  }  }
56    
57  Fitter & Fitter::operator = (const Fitter &rhs)  Fitter & Fitter::operator = (const Fitter &rhs)
58  {  {
59     // Implementation of assignment operator.     // Implementation of assignment operator.
60     if (this == &rhs) return *this;  // time saving self-test     if (this == &rhs) return *this;  // time saving self-test
61       fUseGradient = rhs.fUseGradient;
62       fResult = rhs.fResult;
63       fConfig = rhs.fConfig;
64       // function is copied and managed by FitResult (maybe should use an auto_ptr)
65       fFunc = fResult.ModelFunction();
66       if (rhs.fFunc != 0 && fResult.ModelFunction() == 0) { // case no fit has been done yet - then clone
67          if (fFunc) delete fFunc;
68          fFunc = dynamic_cast<IModelFunction *>( (rhs.fFunc)->Clone() );
69          assert(fFunc != 0);
70       }
71     return *this;     return *this;
72  }  }
73    
# Line 67  Line 81 
81     fFunc = dynamic_cast<IModelFunction *> ( func.Clone() );     fFunc = dynamic_cast<IModelFunction *> ( func.Clone() );
82    
83     // creates the parameter  settings     // creates the parameter  settings
84     fConfig.SetParamsSettings(*fFunc);     fConfig.CreateParamsSettings(*fFunc);
85  }  }
86    
87    
# Line 80  Line 94 
94     fFunc = new ROOT::Math::MultiDimParamFunctionAdapter(func);     fFunc = new ROOT::Math::MultiDimParamFunctionAdapter(func);
95    
96     // creates the parameter  settings     // creates the parameter  settings
97     fConfig.SetParamsSettings(*fFunc);     fConfig.CreateParamsSettings(*fFunc);
98  }  }
99    
100  void Fitter::SetFunction(const IGradModelFunction & func)  void Fitter::SetFunction(const IGradModelFunction & func)
# Line 91  Line 105 
105     fFunc = dynamic_cast<IModelFunction *> ( func.Clone() );     fFunc = dynamic_cast<IModelFunction *> ( func.Clone() );
106    
107     // creates the parameter  settings     // creates the parameter  settings
108     fConfig.SetParamsSettings(*fFunc);     fConfig.CreateParamsSettings(*fFunc);
109  }  }
110    
111    
# Line 103  Line 117 
117     fFunc = new ROOT::Math::MultiDimParamGradFunctionAdapter(func);     fFunc = new ROOT::Math::MultiDimParamGradFunctionAdapter(func);
118    
119     // creates the parameter  settings     // creates the parameter  settings
120     fConfig.SetParamsSettings(*fFunc);     fConfig.CreateParamsSettings(*fFunc);
121  }  }
122    
123    
# Line 111  Line 125 
125     // fit a user provided FCN function     // fit a user provided FCN function
126     // create fit parameter settings     // create fit parameter settings
127     unsigned int npar  = fcn.NDim();     unsigned int npar  = fcn.NDim();
128     if (params != 0 || fConfig.ParamsSettings().size() != npar) fConfig.SetParamsSettings(npar, params);     if (params != 0  )
129          fConfig.SetParamsSettings(npar, params);
130       else {
131          if ( fConfig.ParamsSettings().size() != npar) {
132             MATH_ERROR_MSG("Fitter::FitFCN","wrong fit parameter settings");
133             return false;
134          }
135       }
136     // create Minimizer     // create Minimizer
137     std::auto_ptr<ROOT::Math::Minimizer> minimizer = std::auto_ptr<ROOT::Math::Minimizer> ( fConfig.CreateMinimizer() );     std::auto_ptr<ROOT::Math::Minimizer> minimizer = std::auto_ptr<ROOT::Math::Minimizer> ( fConfig.CreateMinimizer() );
138     if (minimizer.get() == 0) return false;     if (minimizer.get() == 0) return false;
# Line 122  Line 143 
143  bool Fitter::FitFCN(const BaseGradFunc & fcn, const double * params, unsigned int dataSize) {  bool Fitter::FitFCN(const BaseGradFunc & fcn, const double * params, unsigned int dataSize) {
144     // fit a user provided FCN gradient function     // fit a user provided FCN gradient function
145     unsigned int npar  = fcn.NDim();     unsigned int npar  = fcn.NDim();
146     if (params != 0 || fConfig.ParamsSettings().size() != npar) fConfig.SetParamsSettings(npar, params);     if (params != 0  )
147          fConfig.SetParamsSettings(npar, params);
148       else {
149          if ( fConfig.ParamsSettings().size() != npar) {
150             MATH_ERROR_MSG("Fitter::FitFCN","wrong fit parameter settings");
151             return false;
152          }
153       }
154     // create Minimizer  (need to be done afterwards)     // create Minimizer  (need to be done afterwards)
155     std::auto_ptr<ROOT::Math::Minimizer> minimizer = std::auto_ptr<ROOT::Math::Minimizer> ( fConfig.CreateMinimizer() );     std::auto_ptr<ROOT::Math::Minimizer> minimizer = std::auto_ptr<ROOT::Math::Minimizer> ( fConfig.CreateMinimizer() );
156     if (minimizer.get() == 0) return false;     if (minimizer.get() == 0) return false;
# Line 174  Line 202 
202     if (minimizer.get() == 0) return false;     if (minimizer.get() == 0) return false;
203     if (fFunc == 0) return false;     if (fFunc == 0) return false;
204    
205     // logl fit (error is 0.5)     // logl fit (error should be 0.5) set if different than default values (of 1)
206       if (fConfig.MinimizerOptions().ErrorDef() == ROOT::Math::MinimizerOptions::DefaultErrorDef() ) {
207          fConfig.MinimizerOptions().SetErrorDef(0.5);
208     minimizer->SetErrorUp(0.5);     minimizer->SetErrorUp(0.5);
209       }
210    
211     // create a chi2 function to be used for the equivalent chi-square     // create a chi2 function to be used for the equivalent chi-square
212     Chi2FCN<BaseFunc> chi2(data,*fFunc);     Chi2FCN<BaseFunc> chi2(data,*fFunc);
# Line 214  Line 245 
245     std::cout << "Fitter ParamSettings " << Config().ParamsSettings()[ipar].IsBound() << " lower limit " <<  Config().ParamsSettings()[ipar].LowerLimit() << " upper limit " <<  Config().ParamsSettings()[ipar].UpperLimit() << std::endl;     std::cout << "Fitter ParamSettings " << Config().ParamsSettings()[ipar].IsBound() << " lower limit " <<  Config().ParamsSettings()[ipar].LowerLimit() << " upper limit " <<  Config().ParamsSettings()[ipar].UpperLimit() << std::endl;
246  #endif  #endif
247    
248     // logl fit (error is 0.5)     // logl fit (error should be 0.5) set if different than default values (of 1)
249       if (fConfig.MinimizerOptions().ErrorDef() == ROOT::Math::MinimizerOptions::DefaultErrorDef() ) {
250          fConfig.MinimizerOptions().SetErrorDef(0.5);
251     minimizer->SetErrorUp(0.5);     minimizer->SetErrorUp(0.5);
252       }
253    
254     if (!fUseGradient) {     if (!fUseGradient) {
255        // do minimzation without using the gradient        // do minimzation without using the gradient
# Line 281  Line 315 
315  //    }  //    }
316  //    return false;  //    return false;
317    
318     // if requested parabolic error ansure correct analysis by the minimizer     // if requested parabolic error do correct error  analysis by the minimizer (call HESSE)
319     if (fConfig.MinimizerOptions().ParabErrors()) minimizer.SetValidError(true);     if (fConfig.ParabErrors()) minimizer.SetValidError(true);
320    
321    
322     bool ret = minimizer.Minimize();     bool ret = minimizer.Minimize();
# Line 292  Line 326 
326  #endif  #endif
327    
328     unsigned int ncalls = ObjFuncTrait<ObjFunc>::NCalls(objFunc);     unsigned int ncalls = ObjFuncTrait<ObjFunc>::NCalls(objFunc);
329     fResult = FitResult(minimizer,fConfig, *fFunc, ret, dataSize, chi2func, fConfig.MinimizerOptions().MinosErrors(), ncalls );     fResult = FitResult(minimizer,fConfig, *fFunc, ret, dataSize, chi2func, fConfig.MinosErrors(), ncalls );
330     if (fConfig.NormalizeErrors() ) fResult.NormalizeErrors();     if (fConfig.NormalizeErrors() ) fResult.NormalizeErrors();
331     return ret;     return ret;
332  }  }
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