testFitPerf.cxx

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#include "TH1.h"
#include "TF1.h"
#include "TF2.h"
#include "TMath.h"
#include "TSystem.h"
#include "TRandom3.h"
#include "TTree.h"
#include "TROOT.h"

#include "Fit/BinData.h"
#include "Fit/UnBinData.h"
//#include "Fit/BinPoint.h"
#include "Fit/Fitter.h"
#include "HFitInterface.h"

#include "Math/IParamFunction.h"
#include "Math/WrappedTF1.h"
#include "Math/WrappedMultiTF1.h"
#include "Math/WrappedParamFunction.h"
#include "Math/MultiDimParamFunctionAdapter.h"

#include "TGraphErrors.h"

#include "TStyle.h"

#include "TSeqCollection.h"

#include "Math/Polynomial.h"
#include "Math/DistFunc.h"

#include <string>
#include <iostream>

#include "TStopwatch.h"

#include "TVirtualFitter.h"
//#include "TFitterMinuit.h"
// #include "TFitterFumili.h"
// #include "TFumili.h"

#include "GaussFunction.h"

#include "RooDataHist.h"
#include "RooDataSet.h"
#include "RooRealVar.h"
#include "RooGaussian.h"
#include "RooMinuit.h"
#include "RooChi2Var.h"
#include "RooGlobalFunc.h"
#include "RooFitResult.h"
#include "RooProdPdf.h"

#include <cassert>

#include "MinimizerTypes.h"

//#define DEBUG

int nfit;
const int N = 20;
double iniPar[2*N];


void printData(const ROOT::Fit::UnBinData & data) {
   for (unsigned int i = 0; i < data.Size(); ++i) {
      std::cout << data.Coords(i)[0] << "\t";
   }
   std::cout << "\ndata size is " << data.Size() << std::endl;
}

void printResult(int iret) {
   std::cout << "\n************************************************************\n";
   std::cout << "Test\t\t\t\t";
   if (iret == 0) std::cout << "OK";
   else std::cout << "FAILED";
   std::cout << "\n************************************************************\n";
}

bool USE_BRANCH = false;
ROOT::Fit::UnBinData * FillUnBinData(TTree * tree, bool copyData = true, unsigned int dim = 1 ) {

   // fill the unbin data set from a TTree
   ROOT::Fit::UnBinData * d = 0;
   // for the large tree
   if (std::string(tree->GetName()) == "t2") {
      d = new ROOT::Fit::UnBinData();
      // large tree
      unsigned int n = tree->GetEntries();
#ifdef DEBUG
      std::cout << "number of unbin data is " << n << " of dim " << N << std::endl;
#endif
      d->Append(n,N);
      TBranch * bx = tree->GetBranch("x");
      double vx[N];
      bx->SetAddress(vx);
      std::vector<double>  m(N);
      for (int unsigned i = 0; i < n; ++i) {
         bx->GetEntry(i);
         d->Add(vx);
         for (int j = 0; j < N; ++j)
            m[j] += vx[j];
      }

#ifdef DEBUG
      std::cout << "average values of means :\n";
      for (int j = 0; j < N; ++j)
         std::cout << m[j]/n << "  ";
      std::cout << "\n";
#endif

      return d;
   }
   if (USE_BRANCH)
   {
      d = new ROOT::Fit::UnBinData();
      unsigned int n = tree->GetEntries();
      //std::cout << "number of unbin data is " << n << std::endl;

      if (dim == 2) {
         d->Append(n,2);
         TBranch * bx = tree->GetBranch("x");
         TBranch * by = tree->GetBranch("y");
         double v[2];
         bx->SetAddress(&v[0]);
         by->SetAddress(&v[1]);
         for (int unsigned i = 0; i < n; ++i) {
            bx->GetEntry(i);
            by->GetEntry(i);
            d->Add(v);
         }
      }
      else if (dim == 1) {
         d->Append(n,1);
         TBranch * bx = tree->GetBranch("x");
         double v[1];
         bx->SetAddress(&v[0]);
         for (int unsigned i = 0; i < n; ++i) {
            bx->GetEntry(i);
            d->Add(v);
         }
      }

      return d;

      //printData(d);
   }
   else {
      tree->SetEstimate(tree->GetEntries());

      // use TTREE::Draw
      if (dim == 2) {
         tree->Draw("x:y",0,"goff");  // goff is used to turn off the graphics
         double * x = tree->GetV1();
         double * y = tree->GetV2();

         if (x == 0 || y == 0) {
            USE_BRANCH= true;
            return FillUnBinData(tree, true, dim);
         }

         // use array pre-allocated in tree->Draw . This is faster
         //assert(x != 0);
         unsigned int n = tree->GetSelectedRows();

         if (copyData) {
            d = new ROOT::Fit::UnBinData(n,2);
            double vx[2];
            for (int unsigned i = 0; i < n; ++i) {
               vx[0] = x[i];
               vx[1] = y[i];
               d->Add(vx);
            }
         }
         else  // use data pointers directly
            d = new ROOT::Fit::UnBinData(n,x,y);

      }
      else if ( dim == 1) {

            tree->Draw("x",0,"goff");  // goff is used to turn off the graphics
            double * x = tree->GetV1();

            if (x == 0) {
               USE_BRANCH= true;
               return FillUnBinData(tree, true, dim);
            }
            unsigned int n = tree->GetSelectedRows();

            if (copyData) {
               d = new ROOT::Fit::UnBinData(n,1);
               for (int unsigned i = 0; i < n; ++i) {
                  d->Add(x[i]);
               }
            }
            else
               d = new ROOT::Fit::UnBinData(n,x);
         }
      return d;
   }

   //std::copy(x,x+n, d.begin() );
   return 0;
}




// print the data
template <class T>
void printData(const T & data) {
   for (typename T::const_iterator itr = data.begin(); itr != data.end(); ++itr) {
      std::cout << itr->Coords()[0] << "   " << itr->Value() << "   " << itr->Error() << std::endl;
   }
   std::cout << "\ndata size is " << data.Size() << std::endl;
}



// fitting using new fitter
typedef ROOT::Math::IParamMultiFunction Func;
template <class MinType, class T>
int DoBinFit(T * hist, Func & func, bool debug = false, bool useGrad = false) {

   //std::cout << "Fit histogram " << std::endl;

   ROOT::Fit::BinData d;
   ROOT::Fit::FillData(d,hist);

   //printData(d);

   // create the fitter

   ROOT::Fit::Fitter fitter;
   fitter.Config().SetMinimizer(MinType::name().c_str(),MinType::name2().c_str());

   if (debug)
      fitter.Config().MinimizerOptions().SetPrintLevel(3);


   // create the function
   if (!useGrad) {

      // use simply TF1 wrapper
      //ROOT::Math::WrappedMultiTF1 f(*func);
      //ROOT::Math::WrappedTF1 f(*func);
      fitter.SetFunction(func);

   } else { // only for gaus fits
      // use function gradient
#ifdef USE_MATHMORE_FUNC
   // use mathmore for polynomial
      ROOT::Math::Polynomial pol(2);
      assert(pol.NPar() == func->GetNpar());
      pol.SetParameters(func->GetParameters() );
      ROOT::Math::WrappedParamFunction<ROOT::Math::Polynomial> f(pol,1,func->GetParameters(),func->GetParameters()+func->GetNpar() );
#endif
      GaussFunction f;
      f.SetParameters(func.Parameters());
      fitter.SetFunction(f);
   }


   bool ret = fitter.Fit(d);
   if (!ret) {
      std::cout << " Fit Failed " << std::endl;
      return -1;
   }
   if (debug)
      fitter.Result().Print(std::cout);
   return 0;
}

// unbin fit
template <class MinType, class T>
int DoUnBinFit(T * tree, Func & func, bool debug = false, bool copyData = false ) {

   ROOT::Fit::UnBinData * d  = FillUnBinData(tree, copyData, func.NDim() );
   // need to have done Tree->Draw() before fit
   //FillUnBinData(d,tree);

   //std::cout << "data size type and size  is " << typeid(*d).name() <<  "   " << d->Size() << std::endl;
   if (copyData)
      std::cout << "\tcopy data in FitData\n";
   else
      std::cout << "\tre-use original data \n";



   //printData(d);

   // create the fitter
   //std::cout << "Fit parameter 2  " << f.Parameters()[2] << std::endl;

   ROOT::Fit::Fitter fitter;
   fitter.Config().SetMinimizer(MinType::name().c_str(),MinType::name2().c_str());

   if (debug)
      fitter.Config().MinimizerOptions().SetPrintLevel(3);

   // set tolerance 1 for tree to be same as in TTTreePlayer::UnBinFIt
   fitter.Config().MinimizerOptions().SetTolerance(1);


   // create the function

   fitter.SetFunction(func);
   // need to fix param 0 , normalization in the unbinned fits
   //fitter.Config().ParSettings(0).Fix();

   bool ret = fitter.Fit(*d);
   if (!ret) {
      std::cout << " Fit Failed " << std::endl;
      return -1;
   }
   if (debug)
      fitter.Result().Print(std::cout);

   delete d;

   return 0;

}

template <class MinType>
int DoFit(TTree * tree, Func & func, bool debug = false, bool copyData = false ) {
   return DoUnBinFit<MinType, TTree>(tree, func, debug, copyData);
}
template <class MinType>
int DoFit(TH1 * h1, Func & func, bool debug = false, bool copyData = false ) {
   return DoBinFit<MinType, TH1>(h1, func, debug, copyData);
}
template <class MinType>
int DoFit(TGraph * gr, Func & func, bool debug = false, bool copyData = false ) {
   return DoBinFit<MinType, TGraph>(gr, func, debug, copyData);
}

template <class MinType, class FitObj>
int FitUsingNewFitter(FitObj * fitobj, Func & func, bool useGrad=false) {

   std::cout << "\n************************************************************\n";
   std::cout << "\tFit using new Fit::Fitter  " << typeid(*fitobj).name() << std::endl;
   std::cout << "\tMinimizer is " << MinType::name() << "  " << MinType::name2() << " func dim = " << func.NDim() << std::endl;

   int iret = 0;
   TStopwatch w; w.Start();

#ifdef DEBUG
   // std::cout << "initial Parameters " << iniPar << "  " << *iniPar << "   " <<  *(iniPar+1) << std::endl;
   func.SetParameters(iniPar);
   iret |= DoFit<MinType>(fitobj,func,true, useGrad);
   if (iret != 0) {
      std::cout << "Fit failed " << std::endl;
   }

#else
   for (int i = 0; i < nfit; ++i) {
      func.SetParameters(iniPar);
      iret = DoFit<MinType>(fitobj,func, false, useGrad);
      if (iret != 0) {
         std::cout << "Fit failed " << std::endl;
         break;
      }
   }
#endif
   w.Stop();
   std::cout << "\nTime: \t" << w.RealTime() << " , " << w.CpuTime() << std::endl;
   std::cout << "\n************************************************************\n";

   return iret;
}


//------------old fit methods

// fit using Fit method
template <class T, class MinType>
int FitUsingTFit(T * hist, TF1 * func) {

   std::cout << "\n************************************************************\n";
   std::cout << "\tFit using " << hist->ClassName() << "::Fit\n";
   std::cout << "\tMinimizer is " << MinType::name() << std::endl;

   std::string opt = "BFQ0";
   if (MinType::name() == "Linear")
      opt = "Q0";

   // check gminuit
//    TSeqCollection * l = gROOT->GetListOfSpecials();
//    for (int i = 0; i < l->GetEntries(); ++i)  {
//       TObject * obj = l->At(i);
//       std::cout << " entries for i " << i << " of " << l->GetEntries() << " is " << obj << std::endl;
//       if (obj != 0)     std::cout << " object name is " <<  obj->GetName() << std::endl;
//    }

   int iret = 0;
   TVirtualFitter::SetDefaultFitter(MinType::name().c_str());

   TStopwatch w; w.Start();
   for (int i = 0; i < nfit; ++i) {
      func->SetParameters(iniPar);
      iret |= hist->Fit(func,opt.c_str());
      if (iret != 0) {
         std::cout << "Fit failed " << std::endl;
         return iret;
      }
   }
   // std::cout << "iret " << iret << std::endl;
#ifdef DEBUG
   func->SetParameters(iniPar);
//    if (fitter == "Minuit2") {
//       // increase print level
//       TVirtualFitter * tvf = TVirtualFitter::GetFitter();
//       TFitterMinuit * minuit2 = dynamic_cast<TFitterMinuit * >(tvf);
//       assert (minuit2 != 0);
//       minuit2->SetPrintLevel(3);
//    }
   if (MinType::name() != "Linear")
      iret |= hist->Fit(func,"BFV0");
   else
      iret |= hist->Fit(func,"V0");
   // get precice value of minimum
   int pr = std::cout.precision(18);
   std::cout << "Chi2 value = " << func->GetChisquare() << std::endl;
   std::cout.precision(pr);

#endif
   w.Stop();
   std::cout << "\nTime: \t" << w.RealTime() << " , " << w.CpuTime() << std::endl;
   std::cout << "\n************************************************************\n";

   return iret;
}


// unbinned fit using Fit of trees
template <class MinType>
int FitUsingTTreeFit(TTree * tree, TF1 * func, const std::string & vars = "x") {

   std::cout << "\n************************************************************\n";
   std::cout << "\tFit using TTree::UnbinnedFit\n";
   std::cout << "\tMinimizer is " << MinType::name() << std::endl;


   std::string sel = "";

   int iret = 0;
   TVirtualFitter::SetDefaultFitter(MinType::name().c_str());

   TStopwatch w; w.Start();
   for (int i = 0; i < nfit; ++i) {
      func->SetParameters(iniPar);
      iret |= tree->UnbinnedFit(func->GetName(),vars.c_str(),sel.c_str(),"Q");
      if (iret != 0) {
         std::cout << "Fit failed : iret = " << iret << std::endl;
         return iret;
      }
   }
   // std::cout << "iret " << iret << std::endl;
#ifdef DEBUG
   func->SetParameters(iniPar);

   iret |= tree->UnbinnedFit(func->GetName(),vars.c_str(),sel.c_str(),"V");

#endif
   w.Stop();
   std::cout << "\nTime: \t" << w.RealTime() << " , " << w.CpuTime() << std::endl;
   std::cout << "\n************************************************************\n";

   return iret;
}


// int FitUsingTVirtualFitter(TH1 * hist, TF1 * func, const std::string & fitter) {

//    TVirtualFitter::SetDefaultFitter(fitter.c_str());
//    std::cout << "Fit using TVirtualFitter and " << fitter << "\t Time: \t" << w.RealTime() << " , " << w.CpuTime() < std::endl;
// }


// ROoFit


//binned roo fit
int  FitUsingRooFit(TH1 * hist, TF1 * func) {

   int iret = 0;
   std::cout << "\n************************************************************\n";
   std::cout << "\tFit using RooFit (Chi2 Fit)\n";
   std::cout << "\twith function " << func->GetName() << "\n";


   // start counting the time
   TStopwatch w; w.Start();

   for (int i = 0; i < nfit; ++i) {

      RooRealVar x("x","x",-5,5) ;

      RooDataHist data("bindata","bin dataset with x",x,hist) ;
     // define params
      RooAbsPdf * pdf = 0;
      RooRealVar * mean = 0;
      RooRealVar * sigma = 0;

      func->SetParameters(iniPar);
      std::string fname = func->GetName();
      if (fname == "gaussian") {
         double val,pmin,pmax;
         val = func->GetParameter(1); //func->GetParLimits(1,-100,100);
         mean = new RooRealVar("mean","Mean of Gaussian",val) ;
         val = func->GetParameter(2); func->GetParLimits(1,pmin,pmax);
         sigma = new RooRealVar("sigma","Width of Gaussian",val) ;

         pdf = new RooGaussian("gauss","gauss(x,mean,sigma)",x,*mean,*sigma) ;
      }

      assert(pdf != 0);
#define USE_CHI2_FIT
#ifdef USE_CHI2_FIT
      RooChi2Var chi2("chi2","chi2",*pdf,data) ;
      RooMinuit m(chi2) ;
      m.setPrintLevel(-1);
      m.fit("mh") ;
#else
      pdf->fitTo(data);
#endif
//      if (iret != 0) return iret;
      delete pdf;
      delete mean; delete sigma;
   }

   w.Stop();
   std::cout << "\nTime: \t" << w.RealTime() << " , " << w.CpuTime() << std::endl;
   std::cout << "\n************************************************************\n";
   return iret;
}

//unbinned roo fit
int  FitUsingRooFit(TTree * tree, TF1 * func) {

   int iret = 0;
   std::cout << "\n************************************************************\n";
   std::cout << "\tFit using RooFit (Likelihood Fit)\n";
   std::cout << "\twith function " << func->GetName() << "\n";


   // start counting the time
   TStopwatch w; w.Start();

   for (int i = 0; i < nfit; ++i) {

      RooRealVar x("x","x",-100,100) ;
      RooRealVar y("y","y",-100,100);

      RooDataSet data("unbindata","unbin dataset with x",tree,RooArgSet(x,y)) ;


      RooRealVar mean("mean","Mean of Gaussian",iniPar[0], -100,100) ;
      RooRealVar sigma("sigma","Width of Gaussian",iniPar[1], -100, 100) ;

      RooGaussian pdfx("gaussx","gauss(x,mean,sigma)",x,mean,sigma);

      // for 2d data
      RooRealVar meany("meanx","Mean of Gaussian",iniPar[2], -100,100) ;
      RooRealVar sigmay("sigmay","Width of Gaussian",iniPar[3], -100, 100) ;
      RooGaussian pdfy("gaussy","gauss(y,meanx,sigmay)",y,meany,sigmay);

      RooProdPdf pdf("gausxy","gausxy",RooArgSet(pdfx,pdfy) );


#ifdef DEBUG
      int level = 3;
      std::cout << "num entries = " << data.numEntries() << std::endl;
      bool save = true;
      (pdf.getVariables())->Print("v"); // print the parameters
#else
      int level = -1;
      bool save = false;
#endif

//#ifndef _WIN32 // until a bug 30762 is fixed
      RooFitResult * result = pdf.fitTo(data, RooFit::Minos(0), RooFit::Hesse(1) , RooFit::PrintLevel(level), RooFit::Save(save) );
// #else
//       RooFitResult * result = pdf.fitTo(data );
// #endif

#ifdef DEBUG
      mean.Print();
      sigma.Print();
      assert(result != 0);
      std::cout << " Roofit status " << result->status() << std::endl;
      result->Print();
#endif
      if (save) iret |= (result == 0);

      if (iret != 0) {
         std::cout << "Fit failed " << std::endl;
         return iret;
      }

   }

   w.Stop();
   std::cout << "\nTime: \t" << w.RealTime() << " , " << w.CpuTime() << std::endl;
   std::cout << "\n************************************************************\n";
   return iret;
}

//unbinned roo fit (large tree)
int  FitUsingRooFit2(TTree * tree) {

   int iret = 0;
   std::cout << "\n************************************************************\n";
   std::cout << "\tFit using RooFit (Likelihood Fit)\n";


   // start counting the time
   TStopwatch w; w.Start();

   for (int i = 0; i < nfit; ++i) {

      RooArgSet xvars;
      std::vector<RooRealVar *> x(N);
      std::vector<RooRealVar *> m(N);
      std::vector<RooRealVar *> s(N);

      std::vector<RooGaussian *> g(N);
      std::vector<RooProdPdf *> pdf(N);

      for (int j = 0; j < N; ++j) {
         std::string xname = "x_" + ROOT::Math::Util::ToString(j);
         x[j] = new RooRealVar(xname.c_str(),xname.c_str(),-10000,10000) ;
         xvars.add( *x[j] );
      }


      RooDataSet data("unbindata","unbin dataset with x",tree,xvars) ;

      // create the gaussians
      for (int j = 0; j < N; ++j) {
         std::string mname = "m_" + ROOT::Math::Util::ToString(j);
         std::string sname = "s_" + ROOT::Math::Util::ToString(j);


         m[j] = new RooRealVar(mname.c_str(),mname.c_str(),iniPar[2*j],-100,100) ;
         s[j] = new RooRealVar(sname.c_str(),sname.c_str(),iniPar[2*j+1],-100,100) ;

         std::string gname = "g_" + ROOT::Math::Util::ToString(j);
         g[j] = new RooGaussian(gname.c_str(),"gauss(x,mean,sigma)",*x[j],*m[j],*s[j]);

         std::string pname = "prod_" + ROOT::Math::Util::ToString(j);
         if (j == 1) {
            pdf[1] = new RooProdPdf(pname.c_str(),pname.c_str(),RooArgSet(*g[1],*g[0]) );
         }
         else if (j > 1) {
            pdf[j] = new RooProdPdf(pname.c_str(),pname.c_str(),RooArgSet(*g[j],*pdf[j-1]) );
         }
//          else
//             pdf[0] = g[0];

      }




#ifdef DEBUG
      int level = 3;
      std::cout << "num entries = " << data.numEntries() << std::endl;
      bool save = true;
      (pdf[N-1]->getVariables())->Print("v"); // print the parameters
      std::cout << "\n\nDo the fit now \n\n";
#else
      int level = -1;
      bool save = false;
#endif


#ifndef _WIN32 // until a bug 30762 is fixed
      RooFitResult * result = pdf[N-1]->fitTo(data, RooFit::Minos(0), RooFit::Hesse(1) , RooFit::PrintLevel(level), RooFit::Save(save) );
#else
      RooFitResult * result = pdf[N-1]->fitTo(data);
#endif

#ifdef DEBUG
      assert(result != 0);
      std::cout << " Roofit status " << result->status() << std::endl;
      result->Print();
#endif


      iret |= (result != 0);

      // free
      for (int j = 0; j < N; ++j) {
         delete x[j];
         delete m[j];
         delete s[j];
         delete g[j];
         if (j> 0) delete pdf[j];
      }

      if (iret != 0) return iret;
      //assert(iret == 0);


   }

   w.Stop();
   std::cout << "\nTime: \t" << w.RealTime() << " , " << w.CpuTime() << std::endl;
   std::cout << "\n************************************************************\n";
   return iret;
}


double poly2(const double *x, const double *p) {
   return p[0] + (p[1]+p[2]*x[0] ) * x[0];
}

int testPolyFit() {

   int iret = 0;


   std::cout << "\n\n************************************************************\n";
   std::cout << "\t POLYNOMIAL FIT\n";
   std::cout << "************************************************************\n";

   std::string fname("pol2");
   //TF1 * func = (TF1*)gROOT->GetFunction(fname.c_str());
   TF1 * f1 = new TF1("pol2",fname.c_str(),-5,5.);

   f1->SetParameter(0,1);
   f1->SetParameter(1,0.0);
   f1->SetParameter(2,1.0);


   // fill an histogram
   TH1D * h1 = new TH1D("h1","h1",20,-5.,5.);
//      h1->FillRandom(fname.c_str(),100);
   for (int i = 0; i <1000; ++i)
      h1->Fill( f1->GetRandom() );

   //h1->Print();
   //h1->Draw();
   iniPar[0] = 2.; iniPar[1] = 2.; iniPar[2] = 2.;


   iret |= FitUsingTFit<TH1,TMINUIT>(h1,f1);
   iret |= FitUsingTFit<TH1,MINUIT2>(h1,f1);
   // dummy for testing
   //iret |= FitUsingNewFitter<DUMMY>(h1,f1);

   // use simply TF1 wrapper
   //ROOT::Math::WrappedMultiTF1 f2(*f1);
   ROOT::Math::WrappedParamFunction<> f2(&poly2,1,iniPar,iniPar+3);


   // if Minuit2 is later than TMinuit on Interl is much slower , why ??
   iret |= FitUsingNewFitter<MINUIT2>(h1,f2);
   iret |= FitUsingNewFitter<TMINUIT>(h1,f2);

   // test with linear fitter
   // for this test need to pass a multi-dim function
   ROOT::Math::WrappedTF1 wf(*f1);
   ROOT::Math::MultiDimParamGradFunctionAdapter lfunc(wf);
   iret |= FitUsingNewFitter<LINEAR>(h1,lfunc,true);
   iret |= FitUsingTFit<TH1,LINEAR>(h1,f1);

   // test with a graph

   gStyle->SetErrorX(0.); // to seto zero error on X
   TGraphErrors * gr = new TGraphErrors(h1);
   iret |= FitUsingTFit<TGraph,TMINUIT>(gr,f1);

   iret |= FitUsingTFit<TGraph,MINUIT2>(gr,f1);

   iret |= FitUsingNewFitter<MINUIT2>(gr,f2);


   std::cout << "\n-----> test now TGraphErrors with errors in X coordinates\n\n";
   // try with error in X
   gStyle->SetErrorX(0.5); // to set zero error on X
   TGraphErrors * gr2 = new TGraphErrors(h1);
   iret |= FitUsingTFit<TGraph,TMINUIT>(gr2,f1);
   iret |= FitUsingTFit<TGraph,MINUIT2>(gr2,f1);

   iret |= FitUsingNewFitter<MINUIT2>(gr2,f2);

   printResult(iret);

   return iret;
}

double gaussian(const double *x, const double *p) {
   //return p[0]*TMath::Gaus(x[0],p[1],p[2]);
   double tmp = (x[0]-p[1])/p[2];
   return p[0] * std::exp(-tmp*tmp/2);
}

double gausnorm(const double *x, const double *p) {
   //return p[0]*TMath::Gaus(x[0],p[1],p[2]);
   double invsig = 1./p[1];
   double tmp = (x[0]-p[0]) * invsig;
   const double sqrt_2pi = 1./std::sqrt(2.* 3.14159 );
   return std::exp(-0.5 * tmp*tmp ) * sqrt_2pi * invsig;
}
double gausnorm2D(const double *x, const double *p) {
   //return p[0]*TMath::Gaus(x[0],p[1],p[2]);
   return gausnorm(x,p)*gausnorm(x+1,p+2);
}
double gausnormN(const double *x, const double *p) {
   //return p[0]*TMath::Gaus(x[0],p[1],p[2]);
   double f = 1;
   for (int i = 0; i < N; ++i)
      f *= gausnorm(x+i,p+2*i);

   return f;
}

int testGausFit() {

   int iret = 0;

   std::cout << "\n\n************************************************************\n";
   std::cout << "\t GAUSSIAN FIT\n";
   std::cout << "************************************************************\n";



   //std::string fname = std::string("gaus");
   //TF1 * func = (TF1*)gROOT->GetFunction(fname.c_str());
   //TF1 * f1 = new TF1("gaus",fname.c_str(),-5,5.);
   TF1 * f1 = new TF1("gaussian",gaussian,-5,5.,3);
   //f2->SetParameters(0,1,1);

   // fill an histogram
   int nbin = 10000;
   TH1D * h2 = new TH1D("h2","h2",nbin,-5.,5.);
//      h1->FillRandom(fname.c_str(),100);
   for (int i = 0; i < 10000000; ++i)
      h2->Fill( gRandom->Gaus(0,10) );

   iniPar[0] = 100.; iniPar[1] = 2.; iniPar[2] = 2.;


   // use simply TF1 wrapper
   //ROOT::Math::WrappedMultiTF1 f2(*f1);
   ROOT::Math::WrappedParamFunction<> f2(&gaussian,1,iniPar,iniPar+3);


   iret |= FitUsingNewFitter<MINUIT2>(h2,f2);
   iret |= FitUsingNewFitter<TMINUIT>(h2,f2);

//    iret |= FitUsingNewFitter<GSL_PR>(h2,f2);


   iret |= FitUsingTFit<TH1,TMINUIT>(h2,f1);
   iret |= FitUsingTFit<TH1,MINUIT2>(h2,f1);


    iret |= FitUsingNewFitter<GSL_FR>(h2,f2);
    iret |= FitUsingNewFitter<GSL_PR>(h2,f2);
    iret |= FitUsingNewFitter<GSL_BFGS>(h2,f2);
    iret |= FitUsingNewFitter<GSL_BFGS2>(h2,f2);


   // test also fitting a TGraphErrors with histogram data
   gStyle->SetErrorX(0.); // to seto zero error on X
   TGraphErrors * gr = new TGraphErrors(h2);

   iret |= FitUsingTFit<TGraph,TMINUIT>(gr,f1);
   iret |= FitUsingTFit<TGraph,MINUIT2>(gr,f1);

   iret |= FitUsingNewFitter<MINUIT2>(gr,f2);

   // try with error in X
   gStyle->SetErrorX(0.5); // to seto zero error on X
   TGraphErrors * gr2 = new TGraphErrors(h2);
   iret |= FitUsingTFit<TGraph,TMINUIT>(gr2,f1);

   iret |= FitUsingNewFitter<MINUIT2>(gr2,f2);



//#ifdef LATER
   // test using grad function
   std::cout << "\n\nTest Using pre-calculated gradients\n\n";
   bool useGrad=true;
   iret |= FitUsingNewFitter<MINUIT2>(h2,f2,useGrad);
   iret |= FitUsingNewFitter<TMINUIT>(h2,f2,useGrad);
   iret |= FitUsingNewFitter<GSL_FR>(h2,f2,useGrad);
   iret |= FitUsingNewFitter<GSL_PR>(h2,f2,useGrad);
   iret |= FitUsingNewFitter<GSL_BFGS>(h2,f2,useGrad);
   iret |= FitUsingNewFitter<GSL_BFGS2>(h2,f2,useGrad);


   // test LS algorithm
   std::cout << "\n\nTest Least Square algorithms\n\n";
   iret |= FitUsingNewFitter<GSL_NLS>(h2,f2);
   iret |= FitUsingNewFitter<FUMILI2>(h2,f2);
   iret |= FitUsingNewFitter<TFUMILI>(h2,f2);

//    iret |= FitUsingTFit<TH1,FUMILI2>(h2,f1);
//    iret |= FitUsingTFit<TH1,TFUMILI>(h2,f1);
//#endif

   //iret |= FitUsingRooFit(h2,f1);

   printResult(iret);

   return iret;
}

int testTreeFit() {

   std::cout << "\n\n************************************************************\n";
   std::cout << "\t UNBINNED TREE (GAUSSIAN)  FIT\n";
   std::cout << "************************************************************\n";


   TTree t1("t1","a simple Tree with simple variables");
   double  x, y;
   Int_t ev;
   t1.Branch("x",&x,"x/D");
   t1.Branch("y",&y,"y/D");
//          t1.Branch("pz",&pz,"pz/F");
//          t1.Branch("random",&random,"random/D");
   t1.Branch("ev",&ev,"ev/I");

   //fill the tree
   int nrows = 10000;
#ifdef TREE_FIT2D
   nrows = 10000;
#endif
   for (Int_t i=0;i<nrows;i++) {
      gRandom->Rannor(x,y);
      x *= 2; x += 1.;
      y *= 3; y -= 2;

      ev = i;
      t1.Fill();

   }
   //t1.Draw("x"); // to select fit variable

   TF1 * f1 = new TF1("gausnorm", gausnorm, -10,10, 2);
   TF2 * f2 = new TF2("gausnorm2D", gausnorm2D, -10,10, -10,10, 4);

   ROOT::Math::WrappedParamFunction<> wf1(&gausnorm,1,iniPar,iniPar+2);
   ROOT::Math::WrappedParamFunction<> wf2(&gausnorm2D,2,iniPar,iniPar+4);


   iniPar[0] = 0;
   iniPar[1] = 1;
   iniPar[2] = 0;
   iniPar[3] = 1;

   // use simply TF1 wrapper
   //ROOT::Math::WrappedMultiTF1 f2(*f1);

   int iret = 0;

   // fit 1D first


   iret |= FitUsingTTreeFit<MINUIT2>(&t1,f1,"x");
   iret |= FitUsingTTreeFit<MINUIT2>(&t1,f1,"x");

   iret |= FitUsingTTreeFit<TMINUIT>(&t1,f1,"x");

   iret |= FitUsingNewFitter<MINUIT2>(&t1,wf1,false); // not copying the data
   iret |= FitUsingNewFitter<TMINUIT>(&t1,wf1,false); // not copying the data
   iret |= FitUsingNewFitter<MINUIT2>(&t1,wf1,true); // copying the data
   iret |= FitUsingNewFitter<TMINUIT>(&t1,wf1,true); // copying the data

   // fit 2D



   iret |= FitUsingTTreeFit<MINUIT2>(&t1,f2,"x:y");
   iret |= FitUsingTTreeFit<TMINUIT>(&t1,f2,"x:y");

   iret |= FitUsingNewFitter<MINUIT2>(&t1,wf2, true);
   iret |= FitUsingNewFitter<MINUIT2>(&t1,wf2, false);



   iret |= FitUsingRooFit(&t1,f1);

   printResult(iret);
   return iret;

}

int testLargeTreeFit(int nevt = 1000) {

   std::cout << "\n\n************************************************************\n";
   std::cout << "\t UNBINNED TREE (GAUSSIAN MULTI-DIM)  FIT\n";
   std::cout << "************************************************************\n";

   TTree t1("t2","a large Tree with simple variables");
   double  x[N];
   Int_t ev;
   t1.Branch("x",x,"x[20]/D");
   t1.Branch("ev",&ev,"ev/I");

   //fill the tree
   TRandom3 r;
   for (Int_t i=0;i<nevt;i++) {
      for (int j = 0;  j < N; ++j) {
         double mu = double(j)/10.;
         double s  = 1.0 + double(j)/10.;
         x[j] = r.Gaus(mu,s);
      }

      ev = i;
      t1.Fill();

   }
   //t1.Draw("x"); // to select fit variable


   for (int i = 0; i <N; ++i) {
      iniPar[2*i] = 0;
      iniPar[2*i+1] = 1;
   }

   // use simply TF1 wrapper
   //ROOT::Math::WrappedMultiTF1 f2(*f1);
   ROOT::Math::WrappedParamFunction<> f2(&gausnormN,N,2*N,iniPar);

   int iret = 0;
   iret |= FitUsingNewFitter<MINUIT2>(&t1,f2);
   iret |= FitUsingNewFitter<TMINUIT>(&t1,f2);
   iret |= FitUsingNewFitter<GSL_BFGS2>(&t1,f2);



   printResult(iret);
   return iret;

}
int testLargeTreeRooFit(int nevt = 1000) {

   int iret = 0;

   TTree t2("t2b","a large Tree with simple variables");
   double  x[N];
   Int_t ev;
   for (int j = 0; j < N; ++j) {
      std::string xname = "x_" + ROOT::Math::Util::ToString(j);
      std::string xname2 = "x_" + ROOT::Math::Util::ToString(j) + "/D";
      t2.Branch(xname.c_str(),&x[j],xname2.c_str());
   }
   t2.Branch("ev",&ev,"ev/I");
   //fill the tree
   TRandom3 r;
   for (Int_t i=0;i<nevt;i++) {
      for (int j = 0;  j < N; ++j) {
         double mu = double(j)/10.;
         double s  = 1.0 + double(j)/10.;
         x[j] = r.Gaus(mu,s);
      }

      ev = i;
      t2.Fill();
   }


   for (int i = 0; i <N; ++i) {
      iniPar[2*i] = 0;
      iniPar[2*i+1] = 1;
   }


   //TF1 * f = new TF1("gausnormN", gausnormN, -100,100, 2*N);

   iret |= FitUsingRooFit2(&t2);


   printResult(iret);

   return iret;

}

int testFitPerf() {

   int iret = 0;



#ifndef DEBUG
   nfit = 10;
#endif
  iret |= testGausFit();


#ifdef DEBUG
   nfit = 1;
#else
   nfit = 1;
#endif
  iret |= testTreeFit();



 //return iret;

#ifndef DEBUG
   nfit = 1000;
#endif
   iret |= testPolyFit();




  nfit = 1;
 iret |= testLargeTreeRooFit(500);
 iret |= testLargeTreeFit(500);


   if (iret != 0)
      std::cerr << "testFitPerf :\t FAILED " << std::endl;
   else
      std::cerr << "testFitPerf :\t OK " << std::endl;
   return iret;
}

int main() {
   return testFitPerf();
}