diff --git a/tutorials/fit/NumericalMinimization.C b/tutorials/fit/NumericalMinimization.C
index 252a6464381ee81062ee69f2e5b226ea61e4184f..7ec522367f9c675eb3dafc988f52313741614e3b 100644
--- a/tutorials/fit/NumericalMinimization.C
+++ b/tutorials/fit/NumericalMinimization.C
@@ -1,10 +1,12 @@
-// tutorial showing how to use the Minimizer class in ROOT
-// and all the possible minimizer
+// Example on how to use the new Minimizer class in ROOT
+// Show usage with all the possible minimizers.
// Minimize the Rosenbrock function (a 2D -function)
// This example is described also in
// http://root.cern.ch/drupal/content/numerical-minimization#multidim_minim
// input : minimizer name + algorithm name
// randomSeed: = <0 : fixed value: 0 random with seed 0; >0 random with given seed
+//
+//Author: L. Moneta Dec 2010
#include "Math/Minimizer.h"
#include "Math/Factory.h"
@@ -22,19 +24,24 @@ double RosenBrock(const double *xx )
return 100*tmp1*tmp1+tmp2*tmp2;
}
-int NumericalMinimization(const char * minName = "Minuit2",const char *algoName = "" , int randomSeed = -1)
+int NumericalMinimization(const char * minName = "Minuit2",
+ const char *algoName = "" ,
+ int randomSeed = -1)
{
- // create minimizer giving a name and a name (optionally) for the specific algorithm
+ // create minimizer giving a name and a name (optionally) for the specific
+ // algorithm
// possible choices are:
// minName algoName
// Minuit /Minuit2 Migrad, Simplex,Combined,Scan (default is Migrad)
// Minuit2 Fumili2
// Fumili
- // GSLMultiMin ConjugateFR, ConjugatePR, BFGS, BFGS2, SteepestDescent
+ // GSLMultiMin ConjugateFR, ConjugatePR, BFGS,
+ // BFGS2, SteepestDescent
// GSLMultiFit
// GSLSimAn
// Genetic
- ROOT::Math::Minimizer* min = ROOT::Math::Factory::CreateMinimizer(minName, algoName);
+ ROOT::Math::Minimizer* min =
+ ROOT::Math::Factory::CreateMinimizer(minName, algoName);
// set tolerance , etc...
min->SetMaxFunctionCalls(1000000); // for Minuit/Minuit2
@@ -70,9 +77,11 @@ int NumericalMinimization(const char * minName = "Minuit2",const char *algoName
// expected minimum is 0
if ( min->MinValue() < 1.E-4 && f(xs) < 1.E-4)
- std::cout << "Minimizer " << minName << " - " << algoName << " converged to the right minimum" << std::endl;
+ std::cout << "Minimizer " << minName << " - " << algoName
+ << " converged to the right minimum" << std::endl;
else {
- std::cout << "Minimizer " << minName << " - " << algoName << " failed to converge !!!" << std::endl;
+ std::cout << "Minimizer " << minName << " - " << algoName
+ << " failed to converge !!!" << std::endl;
Error("NumericalMinimization","fail to converge");
}
diff --git a/tutorials/fit/combinedFit.C b/tutorials/fit/combinedFit.C
new file mode 100644
index 0000000000000000000000000000000000000000..d8570950d0b93b61d0dbb50f5370879631f8a80b
--- /dev/null
+++ b/tutorials/fit/combinedFit.C
@@ -0,0 +1,139 @@
+//+ Combined (simultaneous) fit of two histogram with separate functions
+// and some common parameters
+//
+// See http://root.cern.ch/phpBB3//viewtopic.php?f=3&t=11740#p50908
+// for a modified version working with Fumili or GSLMultiFit
+//
+// N.B. this macro must be compiled with ACliC
+//
+//Author: L. Moneta - Dec 2010
+
+#include "Fit/Fitter.h"
+#include "Fit/BinData.h"
+#include "Fit/Chi2FCN.h"
+#include "TH1.h"
+#include "TList.h"
+#include "Math/WrappedMultiTF1.h"
+#include "HFitInterface.h"
+#include "TCanvas.h"
+#include "TStyle.h"
+
+
+// definition of shared parameter
+// background function
+int iparB[2] = { 0, // exp amplitude in B histo
+ 2 // exp common parameter
+};
+
+// signal + background function
+int iparSB[5] = { 1, // exp amplitude in S+B histo
+ 2, // exp common parameter
+ 3, // gaussian amplitude
+ 4, // gaussian mean
+ 5 // gaussian sigma
+};
+
+struct GlobalChi2 {
+ GlobalChi2( ROOT::Math::IMultiGenFunction & f1,
+ ROOT::Math::IMultiGenFunction & f2) :
+ fChi2_1(&f1), fChi2_2(&f2) {}
+
+ // parameter vector is first background (in common 1 and 2)
+ // and then is signal (only in 2)
+ double operator() (const double *par) const {
+ double p1[2];
+ for (int i = 0; i < 2; ++i) p1[i] = par[iparB[i] ];
+
+ double p2[5];
+ for (int i = 0; i < 5; ++i) p2[i] = par[iparSB[i] ];
+
+ return (*fChi2_1)(p1) + (*fChi2_2)(p2);
+ }
+
+ const ROOT::Math::IMultiGenFunction * fChi2_1;
+ const ROOT::Math::IMultiGenFunction * fChi2_2;
+};
+
+void combinedFit() {
+
+ TH1D * hB = new TH1D("hB","histo B",100,0,100);
+ TH1D * hSB = new TH1D("hSB","histo S+B",100, 0,100);
+
+ TF1 * fB = new TF1("fB","expo",0,100);
+ fB->SetParameters(1,-0.05);
+ hB->FillRandom("fB");
+
+ TF1 * fS = new TF1("fS","gaus",0,100);
+ fS->SetParameters(1,30,5);
+
+ hSB->FillRandom("fB",2000);
+ hSB->FillRandom("fS",1000);
+
+ // perform now global fit
+
+ TF1 * fSB = new TF1("fSB","expo + gaus(2)",0,100);
+
+ ROOT::Math::WrappedMultiTF1 wfB(*fB,1);
+ ROOT::Math::WrappedMultiTF1 wfSB(*fSB,1);
+
+ ROOT::Fit::DataOptions opt;
+ ROOT::Fit::DataRange rangeB;
+ // set the data range
+ rangeB.SetRange(10,90);
+ ROOT::Fit::BinData dataB(opt,rangeB);
+ ROOT::Fit::FillData(dataB, hB);
+
+ ROOT::Fit::DataRange rangeSB;
+ rangeSB.SetRange(10,50);
+ ROOT::Fit::BinData dataSB(opt,rangeSB);
+ ROOT::Fit::FillData(dataSB, hSB);
+
+ ROOT::Fit::Chi2Function chi2_B(dataB, wfB);
+ ROOT::Fit::Chi2Function chi2_SB(dataSB, wfSB);
+
+ GlobalChi2 globalChi2(chi2_B, chi2_SB);
+
+ ROOT::Fit::Fitter fitter;
+
+ const int Npar = 6;
+ double par0[Npar] = { 5,5,-0.1,100, 30,10};
+
+ // create before the parameter settings in order to fix or set range on them
+ fitter.Config().SetParamsSettings(6,par0);
+ // fix 5-th parameter
+ fitter.Config().ParSettings(4).Fix();
+ // set limits on the third and 4-th parameter
+ fitter.Config().ParSettings(2).SetLimits(-10,-1.E-4);
+ fitter.Config().ParSettings(3).SetLimits(0,10000);
+ fitter.Config().ParSettings(3).SetStepSize(5);
+
+ fitter.Config().MinimizerOptions().SetPrintLevel(0);
+ fitter.Config().SetMinimizer("Minuit2","Migrad");
+
+ // fit FCN function directly
+ // (specify optionally data size and flag to indicate that is a chi2 fit)
+ fitter.FitFCN(6,globalChi2,0,dataB.Size()+dataSB.Size(),true);
+ ROOT::Fit::FitResult result = fitter.Result();
+ result.Print(std::cout);
+
+ TCanvas * c1 = new TCanvas("Simfit","Simultaneous fit of two histograms",
+ 10,10,700,700);
+ c1->Divide(1,2);
+ c1->cd(1);
+ gStyle->SetOptFit(1111);
+
+ fB->SetFitResult( result, iparB);
+ fB->SetRange(rangeB().first, rangeB().second);
+ fB->SetLineColor(kBlue);
+ hB->GetListOfFunctions()->Add(fB);
+ hB->Draw();
+
+ c1->cd(2);
+ fSB->SetFitResult( result, iparSB);
+ fSB->SetRange(rangeSB().first, rangeSB().second);
+ fSB->SetLineColor(kRed);
+ hSB->GetListOfFunctions()->Add(fSB);
+ hSB->Draw();
+
+
+}