From 3c79f94ebf395d1b1074823b2e4420380aba252d Mon Sep 17 00:00:00 2001
From: Lorenzo Moneta <Lorenzo.Moneta@cern.ch>
Date: Thu, 11 Nov 2010 10:17:52 +0000
Subject: [PATCH] add an option flag in Fitter::FitFCN to indicate when is a
chi2 fit so it can be propagated to the FitResult
git-svn-id: http://root.cern.ch/svn/root/trunk@36590 27541ba8-7e3a-0410-8455-c3a389f83636
---
math/mathcore/inc/Fit/Fitter.h | 29 +++++++++++++++--------------
math/mathcore/src/FitResult.cxx | 2 +-
math/mathcore/src/Fitter.cxx | 31 ++++++++++++++++++++-----------
3 files changed, 36 insertions(+), 26 deletions(-)
diff --git a/math/mathcore/inc/Fit/Fitter.h b/math/mathcore/inc/Fit/Fitter.h
index 0be5a5be2da..ab97baafb7f 100644
--- a/math/mathcore/inc/Fit/Fitter.h
+++ b/math/mathcore/inc/Fit/Fitter.h
@@ -153,39 +153,40 @@ public:
/**
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
+ 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 <class Function>
- bool FitFCN(unsigned int npar, Function & fcn, const double * params = 0, unsigned int dataSize = 0);
+ bool FitFCN(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
+ 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
+ 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 (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 );
+ bool FitFCN(const ROOT::Math::IMultiGenFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
/**
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
+ 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 FitFCN(const ROOT::Math::IMultiGradFunction & fcn, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
/**
fit using user provided FCN with Minuit-like interface
- Parameter Settings must have be created before
+ 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);
+ bool FitFCN( MinuitFCN_t fcn, int npar = 0, const double * params = 0, unsigned int dataSize = 0, bool chi2fit = false);
/**
do a linear fit on a set of bin-data
@@ -326,9 +327,9 @@ private:
#endif
template<class Function>
-bool ROOT::Fit::Fitter::FitFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize) {
+bool ROOT::Fit::Fitter::FitFCN(unsigned int npar, Function & f, const double * par, unsigned int datasize,bool chi2fit) {
ROOT::Math::WrappedMultiFunction<Function &> wf(f,npar);
- return FitFCN(wf,par,datasize);
+ return FitFCN(wf,par,datasize,chi2fit);
}
#endif // endif __CINT__
diff --git a/math/mathcore/src/FitResult.cxx b/math/mathcore/src/FitResult.cxx
index d679aa05a27..94cc32a565b 100644
--- a/math/mathcore/src/FitResult.cxx
+++ b/math/mathcore/src/FitResult.cxx
@@ -365,7 +365,7 @@ void FitResult::Print(std::ostream & os, bool doCovMatrix) const {
const std::ios_base::fmtflags prFmt = os.setf(std::ios::left,std::ios::adjustfield); // set left alignment
if (fVal != fChi2 || fChi2 < 0)
- os << std::left << std::setw(nw) << "LogLikelihood" << " = " << std::right << std::setw(nn) << fVal << std::endl;
+ os << std::left << std::setw(nw) << "MinFCN" << " = " << std::right << std::setw(nn) << fVal << std::endl;
if (fChi2 >= 0)
os << std::left << std::setw(nw) << "Chi2" << " = " << std::right << std::setw(nn) << fChi2 << std::endl;
os << std::left << std::setw(nw) << "NDf" << " = " << std::right << std::setw(nn) << fNdf << std::endl;
diff --git a/math/mathcore/src/Fitter.cxx b/math/mathcore/src/Fitter.cxx
index e84c9579e20..c23e02c53bd 100644
--- a/math/mathcore/src/Fitter.cxx
+++ b/math/mathcore/src/Fitter.cxx
@@ -128,10 +128,14 @@ void Fitter::SetFunction(const IGradModel1DFunction & func)
}
-bool Fitter::FitFCN(const BaseFunc & fcn, const double * params, unsigned int dataSize) {
+bool Fitter::FitFCN(const BaseFunc & fcn, const double * params, unsigned int dataSize, bool chi2fit) {
// fit a user provided FCN function
// create fit parameter settings
unsigned int npar = fcn.NDim();
+ if (npar == 0) {
+ MATH_ERROR_MSG("Fitter::FitFCN","FCN function has zero parameters ");
+ return false;
+ }
if (params != 0 )
fConfig.SetParamsSettings(npar, params);
else {
@@ -140,7 +144,7 @@ bool Fitter::FitFCN(const BaseFunc & fcn, const double * params, unsigned int da
return false;
}
}
- fBinFit = false;
+ fBinFit = chi2fit;
// create Minimizer
fMinimizer = std::auto_ptr<ROOT::Math::Minimizer> ( fConfig.CreateMinimizer() );
@@ -152,9 +156,13 @@ bool Fitter::FitFCN(const BaseFunc & fcn, const double * params, unsigned int da
return DoMinimization<BaseFunc> (fcn, dataSize);
}
-bool Fitter::FitFCN(const BaseGradFunc & fcn, const double * params, unsigned int dataSize) {
+bool Fitter::FitFCN(const BaseGradFunc & fcn, const double * params, unsigned int dataSize, bool chi2fit) {
// fit a user provided FCN gradient function
unsigned int npar = fcn.NDim();
+ if (npar == 0) {
+ MATH_ERROR_MSG("Fitter::FitFCN","FCN function has zero parameters ");
+ return false;
+ }
if (params != 0 )
fConfig.SetParamsSettings(npar, params);
else {
@@ -163,7 +171,7 @@ bool Fitter::FitFCN(const BaseGradFunc & fcn, const double * params, unsigned in
return false;
}
}
- fBinFit = false;
+ fBinFit = chi2fit;
// create Minimizer (need to be done afterwards)
fMinimizer = std::auto_ptr<ROOT::Math::Minimizer> ( fConfig.CreateMinimizer() );
@@ -176,18 +184,19 @@ bool Fitter::FitFCN(const BaseGradFunc & fcn, const double * params, unsigned in
return DoMinimization<BaseGradFunc> (fcn, dataSize);
}
-bool Fitter::FitFCN(MinuitFCN_t fcn ) {
+bool Fitter::FitFCN(MinuitFCN_t fcn, int npar, const double * params , unsigned int dataSize , bool chi2fit ) {
// fit using Minuit style FCN type (global function pointer)
// create corresponfing objective function from that function
- int npar = fConfig.ParamsSettings().size();
- if (npar == 0) {
- MATH_ERROR_MSG("Fitter::FitFCN","wrong fit parameter settings - npar = 0 ");
- return false;
+ if (npar == 0) {
+ npar = fConfig.ParamsSettings().size();
+ if (npar == 0) {
+ MATH_ERROR_MSG("Fitter::FitFCN","Fit Parameter settings have not been created ");
+ return false;
+ }
}
- fBinFit = false;
ROOT::Fit::FcnAdapter newFcn(fcn,npar);
- return FitFCN(newFcn);
+ return FitFCN(newFcn,params,dataSize,chi2fit);
}
bool Fitter::DoLeastSquareFit(const BinData & data) {
--
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