// of iterations or the similarity criterion is reached. //
// //
// Currently the similarity criterion is the Chi2 between the a priori //
-// and the unfolded spectrum. //
+// and the unfolded spectrum (OBSOLETE). //
+// //
+// Chi2 calculation became absolute with the correlated error //
+// calculation. //
+// Errors on the unfolded distribution are not known until the end //
+// Use the convergence criterion instead //
// //
// Currently the user has to define the max. number of iterations //
// (::SetMaxNumberOfIterations) //
-// and the chi2 below which the procedure will stop //
-// (::SetMaxChi2 or ::SetMaxChi2PerDOF) //
+// and //
+// - the chi2 below which the procedure will stop //
+// (::SetMaxChi2 or ::SetMaxChi2PerDOF) (OBSOLETE) //
+// - the convergence criterion below which the procedure will stop //
+// SetMaxConvergencePerDOF(Double_t val); //
+// //
+// Correlated error calculation can be activated by using: //
+// SetUseCorrelatedErrors(Bool_t b) in combination with convergence //
+// criterion //
+// Documentation about correlated error calculation method can be //
+// found in AliCFUnfolding::CalculateCorrelatedErrors() //
+// Author: marta.verweij@cern.ch //
// //
// An optional possibility is to smooth the unfolded spectrum at the //
// end of each iteration, either using a fit function //
#include "TH1D.h"
#include "TH2D.h"
#include "TH3D.h"
+#include "TProfile.h"
+#include "TRandom3.h"
ClassImp(AliCFUnfolding)
fPrior(0x0),
fEfficiency(0x0),
fMeasured(0x0),
- fMaxNumIterations(0),
+ fMeasuredOrig(0x0),
+ fMaxNumIterations(20),
fNVariables(0),
- fMaxChi2(0),
fUseSmoothing(kFALSE),
fSmoothFunction(0x0),
fSmoothOption(""),
+ fMaxConvergence(0),
+ fUseCorrelatedErrors(kTRUE),
+ fNRandomIterations(20),
fOriginalPrior(0x0),
fInverseResponse(0x0),
fMeasuredEstimate(0x0),
fUnfolded(0x0),
fCoordinates2N(0x0),
fCoordinatesN_M(0x0),
- fCoordinatesN_T(0x0)
+ fCoordinatesN_T(0x0),
+ fRandomizedDist(0x0),
+ fRandom3(0x0),
+ fRandomUnfolded(0x0),
+ fDeltaUnfoldedP(0x0),
+ fNCalcCorrErrors(0),
+ fRandomSeed(0)
{
//
// default constructor
fPrior(0x0),
fEfficiency((THnSparse*)efficiency->Clone()),
fMeasured((THnSparse*)measured->Clone()),
+ fMeasuredOrig((THnSparse*)measured->Clone()),
fMaxNumIterations(0),
fNVariables(nVar),
- fMaxChi2(0),
fUseSmoothing(kFALSE),
fSmoothFunction(0x0),
fSmoothOption(""),
+ fMaxConvergence(0),
+ fUseCorrelatedErrors(kTRUE),
+ fNRandomIterations(20),
fOriginalPrior(0x0),
fInverseResponse(0x0),
fMeasuredEstimate(0x0),
fUnfolded(0x0),
fCoordinates2N(0x0),
fCoordinatesN_M(0x0),
- fCoordinatesN_T(0x0)
+ fCoordinatesN_T(0x0),
+ fRandomizedDist(0x0),
+ fRandom3(0x0),
+ fRandomUnfolded(0x0),
+ fDeltaUnfoldedP(0x0),
+ fNCalcCorrErrors(0),
+ fRandomSeed(0)
{
//
// named constructor
AliInfo(Form("efficiency matrix has %d bins in dimension %d",fEfficiency->GetAxis(iVar)->GetNbins(),iVar));
AliInfo(Form("measured matrix has %d bins in dimension %d",fMeasured ->GetAxis(iVar)->GetNbins(),iVar));
}
+
Init();
}
fPrior((THnSparse*)c.fPrior->Clone()),
fEfficiency((THnSparse*)c.fEfficiency->Clone()),
fMeasured((THnSparse*)c.fMeasured->Clone()),
+ fMeasuredOrig((THnSparse*)c.fMeasuredOrig->Clone()),
fMaxNumIterations(c.fMaxNumIterations),
fNVariables(c.fNVariables),
- fMaxChi2(c.fMaxChi2),
fUseSmoothing(c.fUseSmoothing),
fSmoothFunction((TF1*)c.fSmoothFunction->Clone()),
- fSmoothOption(fSmoothOption),
+ fSmoothOption(c.fSmoothOption),
+ fMaxConvergence(c.fMaxConvergence),
+ fUseCorrelatedErrors(c.fUseCorrelatedErrors),
+ fNRandomIterations(c.fNRandomIterations),
fOriginalPrior((THnSparse*)c.fOriginalPrior->Clone()),
fInverseResponse((THnSparse*)c.fInverseResponse->Clone()),
fMeasuredEstimate((THnSparse*)fMeasuredEstimate->Clone()),
fUnfolded((THnSparse*)c.fUnfolded->Clone()),
fCoordinates2N(new Int_t(*c.fCoordinates2N)),
fCoordinatesN_M(new Int_t(*c.fCoordinatesN_M)),
- fCoordinatesN_T(new Int_t(*c.fCoordinatesN_T))
+ fCoordinatesN_T(new Int_t(*c.fCoordinatesN_T)),
+ fRandomizedDist((THnSparse*)c.fRandomizedDist->Clone()),
+ fRandom3((TRandom3*)c.fRandom3->Clone()),
+ fRandomUnfolded((THnSparse*)c.fRandomUnfolded->Clone()),
+ fDeltaUnfoldedP((TProfile*)c.fDeltaUnfoldedP),
+ fNCalcCorrErrors(c.fNCalcCorrErrors),
+ fRandomSeed(c.fRandomSeed)
{
//
// copy constructor
fPrior = (THnSparse*)c.fPrior->Clone() ;
fEfficiency = (THnSparse*)c.fEfficiency->Clone() ;
fMeasured = (THnSparse*)c.fMeasured->Clone() ;
+ fMeasuredOrig = ((THnSparse*)c.fMeasuredOrig->Clone()),
fMaxNumIterations = c.fMaxNumIterations ;
fNVariables = c.fNVariables ;
- fMaxChi2 = c.fMaxChi2 ;
+ fMaxConvergence = c.fMaxConvergence ;
fUseSmoothing = c.fUseSmoothing ;
fSmoothFunction = (TF1*)c.fSmoothFunction->Clone();
fSmoothOption = c.fSmoothOption ;
+ fUseCorrelatedErrors = c.fUseCorrelatedErrors;
+ fNRandomIterations = c.fNRandomIterations;
fOriginalPrior = (THnSparse*)c.fOriginalPrior->Clone() ;
fInverseResponse = (THnSparse*)c.fInverseResponse->Clone() ;
fMeasuredEstimate = (THnSparse*)fMeasuredEstimate->Clone() ;
fCoordinates2N = new Int_t(*c.fCoordinates2N) ;
fCoordinatesN_M = new Int_t(*c.fCoordinatesN_M) ;
fCoordinatesN_T = new Int_t(*c.fCoordinatesN_T) ;
+ fRandomizedDist = (THnSparse*)c.fRandomizedDist->Clone();
+ fRandom3 = (TRandom3*)c.fRandom3->Clone();
+ fRandomUnfolded = (THnSparse*)c.fRandomUnfolded->Clone();
+ fDeltaUnfoldedP = (TProfile*)c.fDeltaUnfoldedP;
+ fNCalcCorrErrors = c.fNCalcCorrErrors;
+ fRandomSeed = c.fRandomSeed ;
}
return *this;
}
if (fPrior) delete fPrior;
if (fEfficiency) delete fEfficiency;
if (fMeasured) delete fMeasured;
+ if (fMeasuredOrig) delete fMeasuredOrig;
if (fSmoothFunction) delete fSmoothFunction;
if (fOriginalPrior) delete fOriginalPrior;
if (fInverseResponse) delete fInverseResponse;
if (fCoordinates2N) delete [] fCoordinates2N;
if (fCoordinatesN_M) delete [] fCoordinatesN_M;
if (fCoordinatesN_T) delete [] fCoordinatesN_T;
+ if (fRandomizedDist) delete fRandomizedDist;
+ if (fRandom3) delete fRandom3;
+ if (fRandomUnfolded) delete fRandomUnfolded;
+ if (fDeltaUnfoldedP) delete fDeltaUnfoldedP;
}
//______________________________________________________________
// initialisation function : creates internal settings
//
+ fRandom3 = new TRandom3(fRandomSeed);
+
fCoordinates2N = new Int_t[2*fNVariables];
fCoordinatesN_M = new Int_t[fNVariables];
fCoordinatesN_T = new Int_t[fNVariables];
fInverseResponse = (THnSparse*) fResponse->Clone();
// create the frame of the unfolded spectrum
fUnfolded = (THnSparse*) fPrior->Clone();
+ // create the frame of the random unfolded spectrum
+ fRandomUnfolded = (THnSparse*) fPrior->Clone();
// create the frame of the measurement estimate spectrum
fMeasuredEstimate = (THnSparse*) fMeasured->Clone();
+ // create the frame of the original measurement spectrum
+ fMeasuredOrig = (THnSparse*) fMeasured->Clone();
+
+ InitDeltaUnfoldedProfile();
+
}
//______________________________________________________________
// fill it
for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) {
Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N);
- Double_t conditionalError = fConditional->GetBinError (iBin);
GetCoordinates();
Double_t priorTimesEffValue = priorTimesEff->GetBinContent(fCoordinatesN_T);
- Double_t priorTimesEffError = priorTimesEff->GetBinError (fCoordinatesN_T);
Double_t fill = conditionalValue * priorTimesEffValue ;
if (fill>0.) {
- // error calculation : gaussian error propagation (may be overestimated...)
- Double_t err2 = TMath::Power(fMeasuredEstimate->GetBinError(fCoordinatesN_M),2) ;
- err2 += TMath::Power(conditionalValue*priorTimesEffError,2) + TMath::Power(conditionalError*priorTimesEffValue,2) ;
- Double_t err = TMath::Sqrt(err2);
- fMeasuredEstimate->SetBinError(fCoordinatesN_M,err);
-
fMeasuredEstimate->AddBinContent(fCoordinatesN_M,fill);
+ fMeasuredEstimate->SetBinError(fCoordinatesN_M,0.);
}
}
delete priorTimesEff ;
for (Long_t iBin=0; iBin<fConditional->GetNbins(); iBin++) {
Double_t conditionalValue = fConditional->GetBinContent(iBin,fCoordinates2N);
- Double_t conditionalError = fConditional->GetBinError (iBin);
GetCoordinates();
Double_t estMeasuredValue = fMeasuredEstimate->GetBinContent(fCoordinatesN_M);
- Double_t estMeasuredError = fMeasuredEstimate->GetBinError (fCoordinatesN_M);
Double_t priorTimesEffValue = priorTimesEff ->GetBinContent(fCoordinatesN_T);
- Double_t priorTimesEffError = priorTimesEff ->GetBinError (fCoordinatesN_T);
Double_t fill = (estMeasuredValue>0. ? conditionalValue * priorTimesEffValue / estMeasuredValue : 0. ) ;
- // error calculation : gaussian error propagation (may be overestimated...)
- Double_t err = 0. ;
- if (estMeasuredValue>0.) {
- err = TMath::Sqrt( TMath::Power(conditionalError * priorTimesEffValue * estMeasuredValue ,2) +
- TMath::Power(conditionalValue * priorTimesEffError * estMeasuredValue ,2) +
- TMath::Power(conditionalValue * priorTimesEffValue * estMeasuredError ,2) )
- / TMath::Power(estMeasuredValue,2) ;
- }
if (fill>0. || fInverseResponse->GetBinContent(fCoordinates2N)>0.) {
fInverseResponse->SetBinContent(fCoordinates2N,fill);
- fInverseResponse->SetBinError (fCoordinates2N,err );
+ fInverseResponse->SetBinError (fCoordinates2N,0.);
}
}
delete priorTimesEff ;
void AliCFUnfolding::Unfold() {
//
// Main routine called by the user :
- // it calculates the unfolded spectrum from the response matrix and the measured spectrum
- // several iterations are performed until a reasonable chi2 is reached
+ // it calculates the unfolded spectrum from the response matrix, measured spectrum and efficiency
+ // several iterations are performed until a reasonable chi2 or convergence criterion is reached
//
- Int_t iIterBayes=0 ;
- Double_t chi2=0 ;
+ Int_t iIterBayes = 0 ;
+ Double_t convergence = 0.;
for (iIterBayes=0; iIterBayes<fMaxNumIterations; iIterBayes++) { // bayes iterations
CreateEstMeasured();
CreateInvResponse();
CreateUnfolded();
- chi2 = GetChi2();
- AliDebug(0,Form("Chi2 at iteration %d is %e",iIterBayes,chi2));
- if (fMaxChi2>0. && chi2<fMaxChi2) {
+ if (fUseCorrelatedErrors) {
+ convergence = GetConvergence();
+ AliDebug(0,Form("convergence at iteration %d is %e",iIterBayes,convergence));
+ }
+ else AliWarning("No errors will be calculated. Activate SetUseCorrelatedErrors(kTRUE)\n");
+
+ if (fMaxConvergence>0. && convergence<fMaxConvergence && fNCalcCorrErrors<1) {
+ fNRandomIterations=iIterBayes;
break;
}
+
// update the prior distribution
if (fUseSmoothing) {
if (Smooth()) {
AliError("Couldn't smooth the unfolded spectrum!!");
- AliInfo(Form("\n\n=======================\nFinished at iteration %d : Chi2 is %e and you required it to be < %e\n=======================\n\n",iIterBayes,chi2,fMaxChi2));
+ if (fUseCorrelatedErrors) {
+ if (fNCalcCorrErrors>0) {
+ AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence));
+ }
+ else {
+ AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence));
+ }
+ }
return;
}
}
- fPrior = (THnSparse*)fUnfolded->Clone() ; // this should be changed (memory)
+ if(fNCalcCorrErrors>0) {
+ if (fPrior) delete fPrior ;
+ fPrior = (THnSparse*)fRandomUnfolded->Clone() ;
+ }
+ else {
+ if (fPrior) delete fPrior ;
+ fPrior = (THnSparse*)fUnfolded->Clone() ;
+ }
+ }
+
+ if (fUseCorrelatedErrors && fNCalcCorrErrors==0) {
+ fNCalcCorrErrors=1;
+ CalculateCorrelatedErrors();
+ }
+
+ if (fUseCorrelatedErrors) {
+ if (fNCalcCorrErrors>1) {
+ AliInfo(Form("\n\n=======================\nFinished at iteration %d : convergence is %e and you required it to be < %e\n=======================\n\n",iIterBayes,convergence,fMaxConvergence));
+ }
+ else if(fNCalcCorrErrors>0) {
+ AliInfo(Form("=======================\nUnfolding of randomized distribution finished at iteration %d with convergence %e \n",iIterBayes,convergence));
+ }
}
- AliInfo(Form("\n\n=======================\nFinished at iteration %d : Chi2 is %e and you required it to be < %e\n=======================\n\n",iIterBayes,chi2,fMaxChi2));
}
//______________________________________________________________
// clear the unfolded spectrum
- fUnfolded->Reset();
+ if(fNCalcCorrErrors>0) {
+ //unfold randomized dist
+ fRandomUnfolded->Reset();
+ }
+ else {
+ //unfold measured dist
+ fUnfolded->Reset();
+ }
for (Long_t iBin=0; iBin<fInverseResponse->GetNbins(); iBin++) {
Double_t invResponseValue = fInverseResponse->GetBinContent(iBin,fCoordinates2N);
- Double_t invResponseError = fInverseResponse->GetBinError (iBin);
GetCoordinates();
Double_t effValue = fEfficiency->GetBinContent(fCoordinatesN_T);
- Double_t effError = fEfficiency->GetBinError (fCoordinatesN_T);
Double_t measuredValue = fMeasured ->GetBinContent(fCoordinatesN_M);
- Double_t measuredError = fMeasured ->GetBinError (fCoordinatesN_M);
Double_t fill = (effValue>0. ? invResponseValue * measuredValue / effValue : 0.) ;
-
+
if (fill>0.) {
- // error calculation : gaussian error propagation (may be overestimated...)
- Double_t err2 = TMath::Power(fUnfolded->GetBinError(fCoordinatesN_T),2) ;
- err2 += TMath::Power(invResponseError * measuredValue * effValue,2) / TMath::Power(effValue,4) ;
- err2 += TMath::Power(invResponseValue * measuredError * effValue,2) / TMath::Power(effValue,4) ;
- err2 += TMath::Power(invResponseValue * measuredValue * effError,2) / TMath::Power(effValue,4) ;
- Double_t err = TMath::Sqrt(err2);
- fUnfolded->SetBinError(fCoordinatesN_T,err);
-
- fUnfolded->AddBinContent(fCoordinatesN_T,fill);
+
+ Double_t err = 0.;
+
+ if(fNCalcCorrErrors>0) {
+ fRandomUnfolded->SetBinError(fCoordinatesN_T,err);
+ fRandomUnfolded->AddBinContent(fCoordinatesN_T,fill);
+ }
+ else {
+ fUnfolded->SetBinError(fCoordinatesN_T,err);
+ fUnfolded->AddBinContent(fCoordinatesN_T,fill);
+ }
}
}
}
-
+
+//______________________________________________________________
+
+void AliCFUnfolding::CalculateCorrelatedErrors() {
+
+ fRandomizedDist = (THnSparse*) fMeasuredOrig->Clone();
+ fPrior = (THnSparse*) fOriginalPrior->Clone();
+
+ // Step 1: Create randomized distribution (fRandomizedDist) of each bin of the measured spectrum to calculate correlated errors. Poisson statistics: mean = measured value of bin
+ // Step 2: Unfold randomized distribution
+ // Step 3: Store difference of unfolded spectrum from measured distribution and unfolded distribution from randomized distribution -> fDeltaUnfoldedP (TProfile with option "S")
+ // Step 4: Repeat Step 1-3 several times (fNRandomIterations)
+ // Step 5: The spread of fDeltaUnfoldedP for each bin is the error on the unfolded spectrum of that specific bin
+
+ //Do fNRandomIterations = bayes iterations performed
+ for(int i=0; i<fNRandomIterations; i++) {
+ if (fPrior) delete fPrior ;
+ if (fRandomizedDist) delete fRandomizedDist ;
+ fPrior = (THnSparse*) fOriginalPrior->Clone();
+ fRandomizedDist = (THnSparse*) fMeasuredOrig->Clone();
+ CreateRandomizedDist();
+ if (fMeasured) delete fMeasured ;
+ fMeasured = (THnSparse*) fRandomizedDist->Clone();
+ //Unfold fRandomizedDist
+ Unfold();
+ FillDeltaUnfoldedProfile();
+ }
+
+ // Get statistical errors for final unfolded spectrum
+ // ie. spread of each pt bin in fDeltaUnfoldedP
+ Double_t sigma = 0.;
+ Double_t dummy = 0.;
+ for (Long_t iBin=0; iBin<fRandomUnfolded->GetNbins(); iBin++) {
+ dummy = fUnfolded->GetBinContent(iBin,fCoordinatesN_M);
+ sigma = fDeltaUnfoldedP->GetBinError(fCoordinatesN_M[0]);
+ fUnfolded->SetBinError(fCoordinatesN_M,sigma);
+ fNCalcCorrErrors = 2;
+ }
+
+}
+
+//______________________________________________________________
+void AliCFUnfolding::InitDeltaUnfoldedProfile() {
+ //
+ //Initialize the fDeltaUnfoldedP profile
+ //Errors will be filled with spread between unfolded measured and unfolded randomized spectra
+ //
+
+ Int_t nbinsx = fResponse->GetAxis(0)->GetNbins();
+ Double_t xbins[nbinsx];
+ for(int ix=0; ix<nbinsx; ix++) {
+ xbins[ix] = fResponse->GetAxis(0)->GetBinLowEdge(ix+1);
+ }
+ xbins[nbinsx] = fResponse->GetAxis(0)->GetBinUpEdge(nbinsx);
+ fDeltaUnfoldedP = new TProfile("fDeltaUnfoldedP","Profile of pTUnfolded with spread in error",nbinsx,xbins,"S");
+}
+//______________________________________________________________
+void AliCFUnfolding::CreateRandomizedDist() {
+ //
+ // Create randomized dist from measured distribution
+ //
+
+ Double_t random = 0.;
+ Double_t measuredValue = 0.;
+ Double_t measuredError = 0.;
+ for (Long_t iBin=0; iBin<fRandomizedDist->GetNbins(); iBin++) {
+ measuredValue = fMeasuredOrig->GetBinContent(iBin,fCoordinatesN_M); //used as mean
+ measuredError = fMeasuredOrig->GetBinError(fCoordinatesN_M); //used as sigma
+ // random = fRandom3->PoissonD(measuredValue); //doesn't work for normalized spectra, use Gaus (assuming raw counts in bin is large >10)
+ random = fRandom3->Gaus(measuredValue,measuredError);
+ fRandomizedDist->SetBinContent(iBin,random);
+ }
+}
+
+//______________________________________________________________
+void AliCFUnfolding::FillDeltaUnfoldedProfile() {
+ //
+ // Store difference of unfolded spectrum from measured distribution and unfolded distribution from randomized distribution
+ //
+
+ for (Long_t iBin2=0; iBin2<fRandomUnfolded->GetNbins(); iBin2++) {
+ Double_t delta = fUnfolded->GetBinContent(iBin2,fCoordinatesN_M)-fRandomUnfolded->GetBinContent(iBin2,fCoordinatesN_M);
+ fDeltaUnfoldedP->Fill(fDeltaUnfoldedP->GetBinCenter(fCoordinatesN_M[0]),delta);
+ }
+}
+
//______________________________________________________________
void AliCFUnfolding::GetCoordinates() {
for (Int_t iDim=0; iDim<fNVariables; iDim++) dim[iDim] = fNVariables+iDim ; //dimensions corresponding to TRUE values (i.e. from N to 2N-1)
fProjResponseInT = fConditional->Projection(fNVariables,dim,"E"); //project
delete [] dim;
-
+
// fill the conditional probability matrix
for (Long_t iBin=0; iBin<fResponse->GetNbins(); iBin++) {
Double_t responseValue = fResponse->GetBinContent(iBin,fCoordinates2N);
- Double_t responseError = fResponse->GetBinError (iBin);
GetCoordinates();
Double_t projValue = fProjResponseInT->GetBinContent(fCoordinatesN_T);
- Double_t projError = fProjResponseInT->GetBinError (fCoordinatesN_T);
-
+
Double_t fill = responseValue / projValue ;
if (fill>0. || fConditional->GetBinContent(fCoordinates2N)>0.) {
fConditional->SetBinContent(fCoordinates2N,fill);
- // gaussian error for the moment
- Double_t err2 = TMath::Power(responseError*projValue,2) + TMath::Power(responseValue*projError,2) ;
- Double_t err = TMath::Sqrt(err2);
- err /= TMath::Power(projValue,2) ;
+ Double_t err = 0.;
fConditional->SetBinError (fCoordinates2N,err);
}
}
}
+//______________________________________________________________
+
+Int_t AliCFUnfolding::GetDOF() {
+ //
+ // number of dof = number of bins
+ //
+
+ Int_t nDOF = 1 ;
+ for (Int_t iDim=0; iDim<fNVariables; iDim++) {
+ nDOF *= fPrior->GetAxis(iDim)->GetNbins();
+ }
+ AliDebug(0,Form("Number of degrees of freedom = %d",nDOF));
+ return nDOF;
+}
//______________________________________________________________
Double_t AliCFUnfolding::GetChi2() {
//
// Returns the chi2 between unfolded and a priori spectrum
+ // This function became absolute with the correlated error calculation.
+ // Errors on the unfolded distribution are not known until the end
+ // Use the convergence criterion instead
//
- Double_t chi2 = 0. ;
+ Double_t chi2 = 0. ;
+ Double_t error_unf = 0.;
for (Long_t iBin=0; iBin<fPrior->GetNbins(); iBin++) {
Double_t priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T);
- Double_t error_unf = fUnfolded->GetBinError(fCoordinatesN_T);
+ error_unf = fUnfolded->GetBinError(fCoordinatesN_T);
chi2 += (error_unf > 0. ? TMath::Power((fUnfolded->GetBinContent(fCoordinatesN_T) - priorValue)/error_unf,2) / priorValue : 0.) ;
}
return chi2;
//______________________________________________________________
-void AliCFUnfolding::SetMaxChi2PerDOF(Double_t val) {
+Double_t AliCFUnfolding::GetConvergence() {
+ //
+ // Returns convergence criterion = \sum_t ((U_t^{n-1}-U_t^n)/U_t^{n-1})^2
+ // U is unfolded spectrum, t is the bin, n = current, n-1 = previous
+ //
+ Double_t convergence = 0.;
+ Double_t priorValue = 0.;
+ Double_t currentValue = 0.;
+ for (Long_t iBin=0; iBin < fPrior->GetNbins(); iBin++) {
+ priorValue = fPrior->GetBinContent(iBin,fCoordinatesN_T);
+ if (fNCalcCorrErrors > 0)
+ currentValue = fRandomUnfolded->GetBinContent(fCoordinatesN_T);
+ else
+ currentValue = fUnfolded->GetBinContent(fCoordinatesN_T);
+
+ if (priorValue > 0.)
+ convergence += ((priorValue-currentValue)/priorValue)*((priorValue-currentValue)/priorValue);
+ else {
+ AliWarning(Form("priorValue = %f. Adding 0 to convergence criterion.",priorValue));
+ convergence += 0.;
+ }
+ }
+ return convergence;
+}
+
+//______________________________________________________________
+
+void AliCFUnfolding::SetMaxConvergencePerDOF(Double_t val) {
//
- // Max. chi2 per degree of freedom : user setting
+ // Max. convergence criterion per degree of freedom : user setting
+ // convergence criterion = DOF*val; DOF = number of bins
+ // In Jan-Fiete's multiplicity note: Convergence criterion = DOF*0.001^2
//
- Int_t nDOF = 1 ;
- for (Int_t iDim=0; iDim<fNVariables; iDim++) {
- nDOF *= fPrior->GetAxis(iDim)->GetNbins();
- }
- AliInfo(Form("Number of degrees of freedom = %d",nDOF));
- fMaxChi2 = val * nDOF ;
+ fUseCorrelatedErrors = kTRUE;
+ Int_t nDOF = GetDOF() ;
+ fMaxConvergence = val * nDOF ;
+ AliInfo(Form("MaxConvergence = %e. Number of degrees of freedom = %d",fMaxConvergence,nDOF));
}
//______________________________________________________________
Double_t content = copy->GetBinContent(iBin,coordinates);
Double_t error2 = TMath::Power(copy->GetBinError(iBin),2);
- printf("coord = [%d,%d]\n",coordinates[0],coordinates[1]);
-
// skip the under/overflow bins...
Bool_t isOutside = kFALSE ;
for (Int_t iVar=0; iVar<nDimensions; iVar++) {
fUnfolded->SetBinError (bin,fUnfolded->GetBinError(bin)*functionValue/fUnfolded->GetBinContent(bin));
fUnfolded->SetBinContent(bin,functionValue);
}
+ delete [] bins;
+ delete [] bin ;
return 0;
}
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff