// @(#)root/mathcore:$Id: Fitter.h 26866 2008-12-12 10:50:07Z moneta $ // 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. */ #ifndef ROOT_Fit_DataVectorfwd #include "Fit/DataVectorfwd.h" #endif #ifndef ROOT_Fit_FitConfig #include "Fit/FitConfig.h" #endif #ifndef ROOT_Fit_FitResult #include "Fit/FitResult.h" #endif #ifndef ROOT_Math_IParamFunctionfwd #include "Math/IParamFunctionfwd.h" #endif #include namespace ROOT { namespace Math { class Minimizer; } /** 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. @ingroup FitMain */ class Fitter { public: typedef ROOT::Math::IParamMultiFunction IModelFunction; 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 (); /** 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 Pre-requisite on the function: */ template < class Data , class Function> bool Fit( const Data & data, const Function & func) { SetFunction(func); return Fit(data); } /** fit a binned data set (default method: use chi2) To be implemented option to do likelihood bin fit */ bool Fit(const BinData & data) { return DoLeastSquareFit(data); } /** fit an binned data set using loglikelihood method */ bool Fit(const UnBinData & data) { return DoLikelihoodFit(data); } /** Likelihood fit */ template bool LikelihoodFit(const Data & data) { return DoLikelihoodFit(data); } /** 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) { SetFunction(func); return DoLikelihoodFit(data); } /** fit using the given FCN function represented by a multi-dimensional function interface (ROOT::Math::IMultiGenFunction). Give optionally initial the parameter values and data size to have the fit Ndf correctly set in the FitResult. If the parameters values are not given (parameter pointers=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 */ bool FitFCN(const ROOT::Math::IMultiGenFunction & fcn, const double * params = 0, unsigned int dataSize = 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 other arguments same consideration as in the previous method */ bool FitFCN(const ROOT::Math::IMultiGradFunction & fcn, const double * params = 0, unsigned int dataSize = 0); /** Fit using the a generic FCN function as a C++ callable object implementing double () (const double *) The function dimension (i.e. the number of parameter) is needed in this case For the other arguments same consideration as in the previous methods */ template bool FitFCN(unsigned int npar, Function fcn, const double * params = 0, unsigned int dataSize = 0); /** fit using user provided FCN with Minuit-like interface Parameter Settings must have be created before */ typedef void (* MinuitFCN_t )(int &npar, double *gin, double &f, double *u, int flag); bool FitFCN( MinuitFCN_t fcn); /** do a linear fit on a set of bin-data */ bool LinearFit(const BinData & data) { return DoLinearFit(data); } /** Set the fitted function (model function) from a parametric function interface */ void SetFunction(const IModelFunction & func); /** Set the fitted function from a parametric 1D function interface */ void SetFunction(const IModel1DFunction & func); /** Set the fitted function (model function) from a parametric gradient function interface */ void SetFunction(const IGradModelFunction & func); /** Set the fitted function from 1D gradient parametric function interface */ void SetFunction(const IGradModel1DFunction & func); /** get fit result */ const FitResult & Result() const { assert( fResult.get() ); return *fResult; } /** 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 will be valid as far as the data, the objective function and the fitter class have not been deleted. To be used only after fitting. */ ROOT::Math::Minimizer * GetMinimizer() { 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 data and the fitter class have not been deleted. To be used after the fitting */ ROOT::Math::IMultiGenFunction * GetFCN() { return fObjFunction.get(); } protected: /// least square fit bool DoLeastSquareFit(const BinData & data); /// binned likelihood fit bool DoLikelihoodFit(const BinData & data); /// un-binned likelihood fit bool DoLikelihoodFit(const UnBinData & data); /// linear least square fit bool DoLinearFit(const BinData & data); /// do minimization template bool DoMinimization(const ObjFunc & f, unsigned int dataSize, const ROOT::Math::IMultiGenFunction * chifunc = 0); 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) IModelFunction * fFunc; // copy of the fitted function containing on output the fit result (managed by FitResult) FitConfig fConfig; // fitter configuration (options and parameter settings) std::auto_ptr fResult; //! pointer to the object containing the result of the fit std::auto_ptr fMinimizer; //! pointer to used minimizer std::auto_ptr fObjFunction; //! pointer to used objective function }; } // end namespace Fit } // end namespace ROOT // implementation of inline methods #ifndef __CINT__ #ifndef ROOT_Math_WrappedFunction #include "Math/WrappedFunction.h" #endif template bool ROOT::Fit::Fitter::FitFCN(unsigned int npar, Function f, const double * par, unsigned int datasize) { ROOT::Math::WrappedMultiFunction wf(f,npar); return FitFCN(wf,par,datasize); } #endif // endif __CINT__ #endif /* ROOT_Fit_Fitter */