TGo4FitAmplEstimation.cxx

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// $Id$
//-----------------------------------------------------------------------
//       The GSI Online Offline Object Oriented (Go4) Project
//         Experiment Data Processing at EE department, GSI
//-----------------------------------------------------------------------
// Copyright (C) 2000- GSI Helmholtzzentrum fuer Schwerionenforschung GmbH
//                     Planckstr. 1, 64291 Darmstadt, Germany
// Contact:            http://go4.gsi.de
//-----------------------------------------------------------------------
// This software can be used under the license agreements as stated
// in Go4License.txt file which is part of the distribution.
//-----------------------------------------------------------------------

#include "TGo4FitAmplEstimation.h"

#include <iostream>

#include "TMath.h"
#include "TMatrixD.h"
#include "TVectorD.h"
#include "TArrayC.h"
#include "TArrayD.h"

#include "TGo4Fitter.h"
#include "TGo4FitModel.h"
#include "TGo4FitData.h"
#include "TGo4FitParameter.h"

TGo4FitAmplEstimation::TGo4FitAmplEstimation() : TGo4FitterAction(), fiNumIters(1) {}

TGo4FitAmplEstimation::TGo4FitAmplEstimation(const char *Name, Int_t NumIters)
   : TGo4FitterAction(Name, "Estimate amplitude action"), fiNumIters(NumIters)
{
}

TGo4FitAmplEstimation::~TGo4FitAmplEstimation() {}

void TGo4FitAmplEstimation::DoAction(TGo4FitterAbstract *Fitter)
{
   auto f = dynamic_cast<TGo4Fitter *>(Fitter);
   if (!f)
      return;
   if (!CalculateWithBuffers(f))
      CalculateWithIterators(f);
}

Double_t
TGo4FitAmplEstimation::PointWeight(Int_t niter, Int_t FFtype, Double_t value, Double_t modelvalue, Double_t standdev)
{
   switch (FFtype) {
   case TGo4Fitter::ff_least_squares: return 1.;
   case TGo4Fitter::ff_chi_square: return (standdev <= 0.) ? 0. : 1. / standdev;
   case TGo4Fitter::ff_chi_Pearson:
   case TGo4Fitter::ff_ML_Poisson:
      if (niter == 0)
         return (value < 0.) ? 0. : 1. / (value + 1.);
      else
         return (modelvalue <= 0.) ? 0. : 1. / modelvalue;
   case TGo4Fitter::ff_chi_Neyman: return (value < 1.) ? 1. : 1. / value;
   case TGo4Fitter::ff_chi_gamma: return (value < 0.) ? 0. : 1. / (value + 1.);
   case TGo4Fitter::ff_user: return 1.;
   }
   return 1.;
}

Bool_t TGo4FitAmplEstimation::CalculateWithBuffers(TGo4Fitter *fitter)
{
   if (!fitter->IsInitialized())
      return kFALSE;

   for (Int_t n = 0; n < fitter->GetNumData(); n++)
      if (!fitter->DataBuffersAllocated(fitter->GetData(n)))
         return kFALSE;

   for (Int_t n = 0; n < fitter->GetNumModel(); n++)
      if (!fitter->ModelBuffersAllocated(fitter->GetModel(n)))
         return kFALSE;

   TObjArray comps, fixedcomps;

   for (Int_t n = 0; n < fitter->GetNumModel(); n++) {
      TGo4FitModel *fModel = fitter->GetModel(n);

      Bool_t assigned = kFALSE;
      for (Int_t na = 0; na < fModel->NumAssigments(); na++)
         if (fitter->FindData(fModel->AssignmentName(na))) {
            assigned = kTRUE;
            break;
         }
      if (!assigned)
         continue;

      if (fModel->GetAmplPar() && !fModel->GetAmplPar()->GetFixed())
         comps.Add(fModel);
      else
         fixedcomps.Add(fModel);
   }

   Int_t ncomp = comps.GetLast() + 1;
   if (ncomp <= 0)
      return kFALSE;

   TMatrixD matr(ncomp, ncomp);
   TVectorD vector(ncomp);

   Double_t **DopData = new Double_t *[fitter->GetNumData()];
   for (Int_t n = 0; n < fitter->GetNumData(); n++)
      DopData[n] = new Double_t[fitter->GetDataBinsSize(fitter->GetData(n))];

   Double_t **Weights = new Double_t *[fitter->GetNumData()];
   for (Int_t n = 0; n < fitter->GetNumData(); n++)
      Weights[n] = new Double_t[fitter->GetDataBinsSize(fitter->GetData(n))];

   Int_t niter = 0;
   Int_t FFtype = fitter->GetFitFunctionType();
   Bool_t changed = kFALSE;

   do {

      fitter->RebuildAll();

      for (Int_t ndata = 0; ndata < fitter->GetNumData(); ndata++) {
         TGo4FitData *data = fitter->GetData(ndata);

         Int_t size = fitter->GetDataBinsSize(data);
         Double_t ampl = data->GetAmplValue();

         Double_t *values = fitter->GetDataBinsValues(data);
         Double_t *result = fitter->GetDataBinsResult(data);
         Double_t *devs = fitter->GetDataBinsDevs(data);
         Double_t *weight = Weights[ndata];

         for (Int_t nbin = 0; nbin < size; nbin++)
            weight[nbin] = PointWeight(niter, FFtype, ampl * values[nbin], result[nbin], ampl * ampl * devs[nbin]);
      }

      for (Int_t n1 = 0; n1 < ncomp; n1++)
         for (Int_t n2 = n1; n2 < ncomp; n2++) {
            Double_t fSum = 0.;
            for (Int_t ndata = 0; ndata < fitter->GetNumData(); ndata++) {
               Double_t *bins1 = fitter->GetModelBinsValues((TGo4FitModel *)comps[n1], fitter->GetDataName(ndata));
               Double_t *bins2 = fitter->GetModelBinsValues((TGo4FitModel *)comps[n2], fitter->GetDataName(ndata));
               if (bins1 && bins2) {
                  Int_t size = fitter->GetDataBinsSize(fitter->GetData(ndata));
                  Double_t *weight = Weights[ndata];
                  for (Int_t nbin = 0; nbin < size; nbin++)
                     fSum += bins1[nbin] * bins2[nbin] * weight[nbin];
               }
            }
            matr(n1, n2) = fSum;
            matr(n2, n1) = fSum;
         }

      // calculating difference between data and fixed components
      for (Int_t ndata = 0; ndata < fitter->GetNumData(); ndata++) {
         TGo4FitData *data = fitter->GetData(ndata);
         Int_t size = fitter->GetDataBinsSize(data);
         Double_t dampl = data->GetAmplValue();
         Double_t *values = fitter->GetDataBinsValues(data);
         Double_t *bins = DopData[ndata];

         for (Int_t nbin = 0; nbin < size; nbin++)
            bins[nbin] = dampl * values[nbin];

         for (Int_t n = 0; n <= fixedcomps.GetLast(); n++) {
            TGo4FitModel *model = (TGo4FitModel *)fixedcomps[n];
            Double_t *mbins = fitter->GetModelBinsValues(model, data->GetName());
            if (mbins) {
               Double_t mampl = model->GetAmplValue();
               for (Int_t nbin = 0; nbin < size; nbin++)
                  bins[nbin] -= mampl * mbins[nbin];
            }
         }
      }

      // filling vector

      for (Int_t n = 0; n < ncomp; n++) {
         Double_t fSum = 0.;
         for (Int_t ndata = 0; ndata < fitter->GetNumData(); ndata++) {
            Double_t *bins1 = fitter->GetModelBinsValues((TGo4FitModel *)comps[n], fitter->GetDataName(ndata));
            if (!bins1)
               continue;
            Int_t size = fitter->GetDataBinsSize(fitter->GetData(ndata));
            Double_t *bins = DopData[ndata];
            Double_t *weight = Weights[ndata];
            for (Int_t nbin = 0; nbin < size; nbin++)
               fSum += bins[nbin] * bins1[nbin] * weight[nbin];
         }
         vector(n) = fSum;
      }

      if (matr.Determinant() == 0.) {
         std::cerr << "  Amplitude estimation algorithm failed. " << std::endl;
         std::cerr
            << "  Determinant of matrix == 0.; This may be due to equivalent model components or zero model at all"
            << std::endl;
         break;
      }

      matr.Invert();
      vector *= matr;

      for (Int_t n = 0; n < ncomp; n++) {
         TGo4FitModel *model = (TGo4FitModel *)comps[n];
         model->SetAmplValue(vector(n));
         if (matr(n, n) > 0.)
            model->SetAmplError(TMath::Sqrt(matr(n, n)));
      }

      changed = kFALSE;
      niter++;
      if (niter < fiNumIters)
         if ((FFtype == TGo4Fitter::ff_chi_Pearson) || (FFtype == TGo4Fitter::ff_ML_Poisson))
            changed = kTRUE;

   } while (changed);

   for (Int_t n = 0; n < fitter->GetNumData(); n++)
      delete[] Weights[n];
   delete[] Weights;

   for (Int_t n = 0; n < fitter->GetNumData(); n++)
      delete[] DopData[n];
   delete[] DopData;

   return kTRUE;
}

Bool_t TGo4FitAmplEstimation::CalculateWithIterators(TGo4Fitter *fitter)
{
   if (!fitter)
      return kFALSE;

   Int_t nmodel = fitter->GetNumModel();
   TArrayI refindex(fitter->GetNumModel());
   refindex.Reset(-1);
   TArrayI fixedindex(fitter->GetNumModel());
   fixedindex.Reset(-1);

   Int_t ncomp = 0;
   Int_t nfixed = 0;

   for (Int_t n = 0; n < nmodel; n++) {
      TGo4FitModel *model = fitter->GetModel(n);
      Bool_t assigned = kFALSE;
      for (Int_t na = 0; na < model->NumAssigments(); na++)
         if (fitter->FindData(model->AssignmentName(na))) {
            assigned = kTRUE;
            break;
         }
      if (!assigned)
         continue;

      if (model->GetAmplPar() && !model->GetAmplPar()->GetFixed())
         refindex[ncomp++] = n;
      else
         fixedindex[nfixed++] = n;
   }

   if (ncomp <= 0)
      return kFALSE;

   TMatrixD matr(ncomp, ncomp);
   TVectorD vector(ncomp);

   TArrayC Usage(nmodel);
   TArrayD ModelValues(nmodel);
   TArrayD ModelAmpls(nmodel);

   Int_t niter = 0;
   Int_t FFtype = fitter->GetFitFunctionType();
   Bool_t changed = kFALSE;

   do {
      matr = 0.;
      vector = 0.;

      for (Int_t ndata = 0; ndata < fitter->GetNumData(); ndata++) {
         TGo4FitData *data = fitter->GetData(ndata);
         auto iter = data->MakeIter();
         if (!iter)
            return kFALSE;

         Double_t dampl = data->GetAmplValue();

         if (iter->Reset()) {
            Usage.Reset(0);
            ModelAmpls.Reset(0.);
            ModelValues.Reset(0.);
            for (Int_t n = 0; n < nmodel; n++) {
               TGo4FitModel *model = fitter->GetModel(n);
               if (model->IsAssignTo(data->GetName())) {
                  Usage[n] = 1;
                  ModelAmpls[n] = model->GetAmplValue() * model->GetRatioValueFor(data->GetName());
                  model->BeforeEval(iter->IndexesSize());
               }
            }

            do {
               Double_t result = 0.;
               for (Int_t n = 0; n < nmodel; n++)
                  if (Usage[n]) {
                     ModelValues[n] = fitter->GetModel(n)->EvaluateAtPoint(iter);
                     result += ModelAmpls[n] * ModelValues[n];
                  }
               Double_t weight =
                  PointWeight(niter, FFtype, dampl * iter->Value(), result, dampl * dampl * iter->StandardDeviation());
               if (weight <= 0.)
                  continue;

               for (Int_t i1 = 0; i1 < ncomp; i1++)
                  for (Int_t i2 = i1; i2 < ncomp; i2++) {
                     Double_t zn = matr(i1, i2);
                     zn += ModelValues[refindex[i1]] * ModelValues[refindex[i2]] * weight;
                     matr(i1, i2) = zn;
                     if (i1 != i2)
                        matr(i2, i1) = zn;
                  }

               Double_t dvalue = dampl * iter->Value();
               for (Int_t nf = 0; nf < nfixed; nf++)
                  dvalue -= ModelAmpls[fixedindex[nf]] * ModelValues[fixedindex[nf]];

               for (Int_t i1 = 0; i1 < ncomp; i1++) {
                  Double_t zn = vector(i1);
                  zn += ModelValues[refindex[i1]] * dvalue * weight;
                  vector(i1) = zn;
               }

            } while (iter->Next());

            for (Int_t n = 0; n < nmodel; n++)
               if (Usage[n])
                  fitter->GetModel(n)->AfterEval();
         }
      }

      if (matr.Determinant() == 0.) {
         std::cerr << "  Amplitude estimation algorithm failed. " << std::endl;
         std::cerr
            << "  Determinant of matrix == 0.; This may be due to equivalent model components or zero model at all"
            << std::endl;
         return kFALSE;
      }

      matr.Invert();
      vector *= matr;

      for (Int_t n = 0; n < ncomp; n++) {
         TGo4FitModel *model = fitter->GetModel(refindex[n]);
         model->SetAmplValue(vector(n));
         if (matr(n, n) > 0.)
            model->SetAmplError(TMath::Sqrt(matr(n, n)));
      }

      changed = kFALSE;
      niter++;
      if (niter < fiNumIters)
         if ((FFtype == TGo4Fitter::ff_chi_Pearson) || (FFtype == TGo4Fitter::ff_ML_Poisson))
            changed = kTRUE;

   } while (changed);

   return kTRUE;
}

void TGo4FitAmplEstimation::Print(Option_t *option) const
{
   TGo4FitterAction::Print(option);
   std::cout << "  number of iterations " << fiNumIters << std::endl;
}