// @(#)root/mathcore:$Id$ // Author: L. Moneta Wed Aug 30 11:05:19 2006 /********************************************************************** * * * Copyright (c) 2006 LCG ROOT Math Team, CERN/PH-SFT * * * * * **********************************************************************/ // Header file for class Fitter #ifndef ROOT_Fit_Fitter #define ROOT_Fit_Fitter /** @defgroup Fit Fitting and Parameter Estimation Classes used for fitting (regression analysis) and estimation of parameter values given a data sample. @ingroup MathCore */ #include "Fit/BinData.h" #include "Fit/UnBinData.h" #include "Fit/FitConfig.h" #include "Fit/FitExecutionPolicy.h" #include "Fit/FitResult.h" #include "Math/IParamFunction.h" #include namespace ROOT { namespace Math { class Minimizer; // should maybe put this in a FitMethodFunctionfwd file template class BasicFitMethodFunction; // define the normal and gradient function typedef BasicFitMethodFunction FitMethodFunction; typedef BasicFitMethodFunction FitMethodGradFunction; } /** Namespace for the fitting classes @ingroup Fit */ namespace Fit { /** @defgroup FitMain User Fitting classes Main Classes used for fitting a given data set @ingroup Fit */ //___________________________________________________________________________________ /** Fitter class, entry point for performing all type of fits. Fits are performed using the generic ROOT::Fit::Fitter::Fit method. The inputs are the data points and a model function (using a ROOT::Math::IParamFunction) The result of the fit is returned and kept internally in the ROOT::Fit::FitResult class. The configuration of the fit (parameters, options, etc...) are specified in the ROOT::Math::FitConfig class. After fitting the config of the fit will be modified to have the new values the resulting parameter of the fit with step sizes equal to the errors. FitConfig can be preserved with initial parameters by calling FitConfig.SetUpdateAfterFit(false); @ingroup FitMain */ class Fitter { public: typedef ROOT::Math::IParamMultiFunction IModelFunction; template using IModelFunctionTempl = ROOT::Math::IParamMultiFunctionTempl; #ifdef R__HAS_VECCORE typedef ROOT::Math::IParametricFunctionMultiDimTempl IModelFunction_v; typedef ROOT::Math::IParamMultiGradFunctionTempl IGradModelFunction_v; #else typedef ROOT::Math::IParamMultiFunction IModelFunction_v; typedef ROOT::Math::IParamMultiGradFunction IGradModelFunction_v; #endif typedef ROOT::Math::IParamMultiGradFunction IGradModelFunction; typedef ROOT::Math::IParamFunction IModel1DFunction; typedef ROOT::Math::IParamGradFunction IGradModel1DFunction; typedef ROOT::Math::IMultiGenFunction BaseFunc; typedef ROOT::Math::IMultiGradFunction BaseGradFunc; /** Default constructor */ Fitter (); /** Constructor from a result */ Fitter (const std::shared_ptr & result); /** Destructor */ ~Fitter (); private: /** Copy constructor (disabled, class is not copyable) */ Fitter(const Fitter &); /** Assignment operator (disabled, class is not copyable) */ Fitter & operator = (const Fitter & rhs); public: /** fit a data set using any generic model function If data set is binned a least square fit is performed If data set is unbinned a maximum likelihood fit (not extended) is done Pre-requisite on the function: it must implement the 1D or multidimensional parametric function interface */ template ::value || std::is_same::value), Function>::type> bool Fit(const Data &data, const Function &func, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetFunction(func); return Fit(data, executionPolicy); } /** Fit a binned data set using a least square fit (default method) */ bool Fit(const BinData & data, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoLeastSquareFit(executionPolicy); } bool Fit(const std::shared_ptr & data, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoLeastSquareFit(executionPolicy); } /** Fit a binned data set using a least square fit */ bool LeastSquareFit(const BinData & data) { return Fit(data); } /** fit an unbinned data set using loglikelihood method */ bool Fit(const UnBinData & data, bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoUnbinnedLikelihoodFit(extended, executionPolicy); } /** Binned Likelihood fit. Default is extended */ bool LikelihoodFit(const BinData &data, bool extended = true, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoBinnedLikelihoodFit(extended, executionPolicy); } bool LikelihoodFit(const std::shared_ptr &data, bool extended = true, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoBinnedLikelihoodFit(extended, executionPolicy); } /** Unbinned Likelihood fit. Default is not extended */ bool LikelihoodFit(const UnBinData & data, bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoUnbinnedLikelihoodFit(extended, executionPolicy); } bool LikelihoodFit(const std::shared_ptr & data, bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial) { SetData(data); return DoUnbinnedLikelihoodFit(extended, executionPolicy); } /** fit a data set using any generic model function Pre-requisite on the function: */ template < class Data , class Function> bool LikelihoodFit( const Data & data, const Function & func, bool extended) { SetFunction(func); return LikelihoodFit(data, extended); } /** do a linear fit on a set of bin-data */ bool LinearFit(const BinData & data) { SetData(data); return DoLinearFit(); } bool LinearFit(const std::shared_ptr & data) { SetData(data); return DoLinearFit(); } /** Fit using the a generic FCN function as a C++ callable object implementing double () (const double *) Note that the function dimension (i.e. the number of parameter) is needed in this case For the options see documentation for following methods FitFCN(IMultiGenFunction & fcn,..) */ template bool FitFCN(unsigned int npar, Function & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Set a generic FCN function as a C++ callable object implementing double () (const double *) Note that the function dimension (i.e. the number of parameter) is needed in this case For the options see documentation for following methods FitFCN(IMultiGenFunction & fcn,..) */ template bool SetFCN(unsigned int npar, Function & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Fit using the given FCN function represented by a multi-dimensional function interface (ROOT::Math::IMultiGenFunction). Give optionally the initial arameter values, data size to have the fit Ndf correctly set in the FitResult and flag specifying if it is a chi2 fit. Note that if the parameters values are not given (params=0) the current parameter settings are used. The parameter settings can be created before by using the FitConfig::SetParamsSetting. If they have not been created they are created automatically when the params pointer is not zero. Note that passing a params != 0 will set the parameter settings to the new value AND also the step sizes to some pre-defined value (stepsize = 0.3 * abs(parameter_value) ) */ bool FitFCN(const ROOT::Math::IMultiGenFunction &fcn, const double *params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Fit using a FitMethodFunction interface. Same as method above, but now extra information can be taken from the function class */ bool FitFCN(const ROOT::Math::FitMethodFunction & fcn, const double * params = 0); /** Set the FCN function represented by a multi-dimensional function interface (ROOT::Math::IMultiGenFunction) and optionally the initial parameters See also note above for the initial parameters for FitFCN */ bool SetFCN(const ROOT::Math::IMultiGenFunction &fcn, const double *params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Set the FCN function represented by a multi-dimensional function interface (ROOT::Math::IMultiGenFunction) and optionally the initial parameters See also note above for the initial parameters for FitFCN With this interface we pass in addition a ModelFunction that will be attached to the FitResult and used to compute confidence interval of the fit */ bool SetFCN(const ROOT::Math::IMultiGenFunction &fcn, const IModelFunction & func, const double *params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Set the objective function (FCN) using a FitMethodFunction interface. Same as method above, but now extra information can be taken from the function class */ bool SetFCN(const ROOT::Math::FitMethodFunction & fcn, const double * params = 0); /** Fit using the given FCN function representing a multi-dimensional gradient function interface (ROOT::Math::IMultiGradFunction). In this case the minimizer will use the gradient information provided by the function. For the options same consideration as in the previous method */ bool FitFCN(const ROOT::Math::IMultiGradFunction &fcn, const double *params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Fit using a FitMethodGradFunction interface. Same as method above, but now extra information can be taken from the function class */ bool FitFCN(const ROOT::Math::FitMethodGradFunction & fcn, const double * params = 0); /** Set the FCN function represented by a multi-dimensional gradient function interface (ROOT::Math::IMultiGradFunction) and optionally the initial parameters See also note above for the initial parameters for FitFCN */ bool SetFCN(const ROOT::Math::IMultiGradFunction &fcn, const double *params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Set the FCN function represented by a multi-dimensional gradient function interface (ROOT::Math::IMultiGradFunction) and optionally the initial parameters See also note above for the initial parameters for FitFCN With this interface we pass in addition a ModelFunction that will be attached to the FitResult and used to compute confidence interval of the fit */ bool SetFCN(const ROOT::Math::IMultiGradFunction &fcn, const IModelFunction &func, const double *params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Set the objective function (FCN) using a FitMethodGradFunction interface. Same as method above, but now extra information can be taken from the function class */ bool SetFCN(const ROOT::Math::FitMethodGradFunction & fcn, const double * params = 0); /** fit using user provided FCN with Minuit-like interface If npar = 0 it is assumed that the parameters are specified in the parameter settings created before For the options same consideration as in the previous method */ typedef void (* MinuitFCN_t )(int &npar, double *gin, double &f, double *u, int flag); bool FitFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** set objective function using user provided FCN with Minuit-like interface If npar = 0 it is assumed that the parameters are specified in the parameter settings created before For the options same consideration as in the previous method */ bool SetFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false); /** Perform a fit with the previously set FCN function. Require SetFCN before */ bool FitFCN(); /** Perform a simple FCN evaluation. FitResult will be modified and contain the value of the FCN */ bool EvalFCN(); /** Set the fitted function (model function) from a parametric function interface */ void SetFunction(const IModelFunction & func, bool useGradient = false); /** Set the fitted function (model function) from a vectorized parametric function interface */ #ifdef R__HAS_VECCORE template ::value)>> void SetFunction(const IModelFunction_v &func, bool useGradient = false); template ::value)>> void SetFunction(const IGradModelFunction_v &func, bool useGradient = true); #endif /** Set the fitted function from a parametric 1D function interface */ void SetFunction(const IModel1DFunction & func, bool useGradient = false); /** Set the fitted function (model function) from a parametric gradient function interface */ void SetFunction(const IGradModelFunction & func, bool useGradient = true); /** Set the fitted function from 1D gradient parametric function interface */ void SetFunction(const IGradModel1DFunction & func, bool useGradient = true); /** get fit result */ const FitResult & Result() const { assert( fResult.get() ); return *fResult; } /** perform an error analysis on the result using the Hessian Errors are obtaied from the inverse of the Hessian matrix To be called only after fitting and when a minimizer supporting the Hessian calculations is used otherwise an error (false) is returned. A new FitResult with the Hessian result will be produced */ bool CalculateHessErrors(); /** perform an error analysis on the result using MINOS To be called only after fitting and when a minimizer supporting MINOS is used otherwise an error (false) is returned. The result will be appended in the fit result class Optionally a vector of parameter indeces can be passed for selecting the parameters to analyse using FitConfig::SetMinosErrors */ bool CalculateMinosErrors(); /** access to the fit configuration (const method) */ const FitConfig & Config() const { return fConfig; } /** access to the configuration (non const method) */ FitConfig & Config() { return fConfig; } /** query if fit is binned. In cse of false teh fit can be unbinned or is not defined (like in case of fitting through a ::FitFCN) */ bool IsBinFit() const { return fBinFit; } /** return pointer to last used minimizer (is NULL in case fit is not yet done) This pointer is guranteed to be valid as far as the fitter class is valid and a new fit is not redone. To be used only after fitting. The pointer should not be stored and will be invalided after performing a new fitting. In this case a new instance of ROOT::Math::Minimizer will be re-created and can be obtained calling again GetMinimizer() */ ROOT::Math::Minimizer * GetMinimizer() const { return fMinimizer.get(); } /** return pointer to last used objective function (is NULL in case fit is not yet done) This pointer will be valid as far as the fitter class has not been deleted. To be used after the fitting. The pointer should not be stored and will be invalided after performing a new fitting. In this case a new instance of the function pointer will be re-created and can be obtained calling again GetFCN() */ ROOT::Math::IMultiGenFunction * GetFCN() const { return fObjFunction.get(); } /** apply correction in the error matrix for the weights for likelihood fits This method can be called only after a fit. The passed function (loglw2) is a log-likelihood function impelemented using the sum of weight squared When using FitConfig.SetWeightCorrection() this correction is applied automatically when doing a likelihood fit (binned or unbinned) */ bool ApplyWeightCorrection(const ROOT::Math::IMultiGenFunction & loglw2, bool minimizeW2L=false); protected: /// least square fit bool DoLeastSquareFit(const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial); /// binned likelihood fit bool DoBinnedLikelihoodFit(bool extended = true, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial); /// un-binned likelihood fit bool DoUnbinnedLikelihoodFit( bool extended = false, const ROOT::Fit::ExecutionPolicy &executionPolicy = ROOT::Fit::ExecutionPolicy::kSerial); /// linear least square fit bool DoLinearFit(); // initialize the minimizer bool DoInitMinimizer(); /// do minimization bool DoMinimization(const BaseFunc & f, const ROOT::Math::IMultiGenFunction * chifunc = 0); // do minimization after having set obj function bool DoMinimization(const ROOT::Math::IMultiGenFunction * chifunc = 0); // update config after fit void DoUpdateFitConfig(); // update minimizer options for re-fitting bool DoUpdateMinimizerOptions(bool canDifferentMinim = true); // get function calls from the FCN int GetNCallsFromFCN(); //set data for the fit void SetData(const FitData & data) { fData = std::shared_ptr(const_cast(&data),DummyDeleter()); } // set data and function without cloning them template void SetFunctionAndData(const IModelFunctionTempl & func, const FitData & data) { SetData(data); fFunc = std::shared_ptr>(const_cast*>(&func),DummyDeleter>()); } //set data for the fit using a shared ptr template void SetData(const std::shared_ptr & data) { fData = std::static_pointer_cast(data); } /// look at the user provided FCN and get data and model function is /// they derive from ROOT::Fit FCN classes void ExamineFCN(); /// internal functions to get data set and model function from FCN /// useful for fits done with customized FCN classes template bool GetDataFromFCN(); private: bool fUseGradient; // flag to indicate if using gradient or not bool fBinFit; // flag to indicate if fit is binned // in case of false the fit is unbinned or undefined) // flag it is used to compute chi2 for binned likelihood fit int fFitType; // type of fit (0 undefined, 1 least square, 2 likelihood) int fDataSize; // size of data sets (need for Fumili or LM fitters) FitConfig fConfig; // fitter configuration (options and parameter settings) std::shared_ptr fFunc_v; //! copy of the fitted function containing on output the fit result std::shared_ptr fFunc; //! copy of the fitted function containing on output the fit result std::shared_ptr fResult; //! pointer to the object containing the result of the fit std::shared_ptr fMinimizer; //! pointer to used minimizer std::shared_ptr fData; //! pointer to the fit data (binned or unbinned data) std::shared_ptr fObjFunction; //! pointer to used objective function }; // internal functions to get data set and model function from FCN // useful for fits done with customized FCN classes template bool Fitter::GetDataFromFCN() { ObjFuncType * objfunc = dynamic_cast(fObjFunction.get() ); if (objfunc) { fFunc = objfunc->ModelFunctionPtr(); fData = objfunc->DataPtr(); return true; } else { return false; } } #ifdef R__HAS_VECCORE template void Fitter::SetFunction(const IModelFunction_v &func, bool useGradient) { fUseGradient = useGradient; if (fUseGradient) { const IGradModelFunction_v *gradFunc = dynamic_cast(&func); if (gradFunc) { SetFunction(*gradFunc, true); return; } else { MATH_WARN_MSG("Fitter::SetFunction", "Requested function does not provide gradient - use it as non-gradient function "); } } // set the fit model function (clone the given one and keep a copy ) // std::cout << "set a non-grad function" << std::endl; fUseGradient = false; fFunc_v = std::shared_ptr(dynamic_cast(func.Clone())); assert(fFunc_v); // creates the parameter settings fConfig.CreateParamsSettings(*fFunc_v); fFunc.reset(); } template void Fitter::SetFunction(const IGradModelFunction_v &func, bool useGradient) { fUseGradient = useGradient; // set the fit model function (clone the given one and keep a copy ) fFunc_v = std::shared_ptr(dynamic_cast(func.Clone())); assert(fFunc_v); // creates the parameter settings fConfig.CreateParamsSettings(*fFunc_v); fFunc.reset(); } #endif } // end namespace Fit } // end namespace ROOT // implementation of inline methods #ifndef __CINT__ #include "Math/WrappedFunction.h" template bool ROOT::Fit::Fitter::FitFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) { ROOT::Math::WrappedMultiFunction wf(f,npar); return FitFCN(wf,par,datasize,chi2fit); } template bool ROOT::Fit::Fitter::SetFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) { ROOT::Math::WrappedMultiFunction wf(f,npar); return SetFCN(wf,par,datasize,chi2fit); } #endif // endif __CINT__ #endif /* ROOT_Fit_Fitter */