RooNDKeysPdf.cxx

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/*****************************************************************************
 * Project: RooFit                                                           *
 * Package: RooFitModels                                                     *
 *    File: $Id: RooNDKeysPdf.cxx 31258 2009-11-17 22:41:06Z wouter $
 * Authors:                                                                  *
 *   Max Baak, CERN, mbaak@cern.ch *
 *                                                                           *
 * Redistribution and use in source and binary forms,                        *
 * with or without modification, are permitted according to the terms        *
 * listed in LICENSE (http://roofit.sourceforge.net/license.txt)             *
 *****************************************************************************/
 
/** \class RooNDKeysPdf
    \ingroup Roofit
 
Generic N-dimensional implementation of a kernel estimation p.d.f. This p.d.f. models the distribution
of an arbitrary input dataset as a superposition of Gaussian kernels, one for each data point,
each contributing 1/N to the total integral of the p.d.f.
If the 'adaptive mode' is enabled, the width of the Gaussian is adaptively calculated from the
local density of events, i.e. narrow for regions with high event density to preserve details and
wide for regions with log event density to promote smoothness. The details of the general algorithm
are described in the following paper:
Cranmer KS, Kernel Estimation in High-Energy Physics.
            Computer Physics Communications 136:198-207,2001 - e-Print Archive: hep ex/0011057
For multi-dimensional datasets, the kernels are modeled by multidimensional Gaussians. The kernels are
constructed such that they reflect the correlation coefficients between the observables
in the input dataset.
**/
 
#include <iostream>
#include <algorithm>
#include <string>
 
#include "TMath.h"
#include "TMatrixDSymEigen.h"
#include "RooNDKeysPdf.h"
#include "RooRealVar.h"
#include "RooRandom.h"
#include "RooHist.h"
#include "RooMsgService.h"
#include "RooChangeTracker.h"
 
#include "TError.h"
 
using std::string, std::vector, std::pair, std::map;
 
 
////////////////////////////////////////////////////////////////////////////////
/// Construct N-dimensional kernel estimation p.d.f. in observables 'varList'
/// from dataset 'data'. Options can be
///
///  - 'a' = Use adaptive kernels (width varies with local event density)
///  - 'm' = Mirror data points over observable boundaries. Improves modeling
///         behavior at edges for distributions that are not close to zero
///         at edge
///  - 'd' = Debug flag
///  - 'v' = Verbose flag
///
/// The parameter rho (default = 1) provides an overall scale factor that can
/// be applied to the bandwidth calculated for each kernel. The nSigma parameter
/// determines the size of the box that is used to search for contributing kernels
/// around a given point in observable space. The nSigma parameters is used
/// in case of non-adaptive bandwidths and for the 1st non-adaptive pass for
/// the calculation of adaptive keys p.d.f.s.
///
/// The optional weight arguments allows to specify an observable or function
/// expression in observables that specifies the weight of each event.
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, const RooArgList &varList, const RooDataSet &data,
                           TString options, double rho, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this), _options(options), _widthFactor(rho),
     _nSigma(nSigma), _rotate(rotate), _sortInput(sortInput), _nAdpt(1)
{
  _varList.addTyped<RooAbsReal>(varList);
 
  createPdf(true, data);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, const RooArgList &varList, const TH1 &hist,
                           TString options, double rho, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this),
     _options(options), _widthFactor(rho), _nSigma(nSigma), _rotate(rotate),
     _sortInput(sortInput), _nAdpt(1)
{
   _varList.addTyped<RooAbsReal>(varList);
 
   createPdf(true, *std::unique_ptr<RooDataSet>{createDatasetFromHist(varList, hist)});
}
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, const RooArgList &varList, const RooDataSet &data,
                           const TVectorD &rho, TString options, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this), _options(options), _widthFactor(-1.0),
     _nSigma(nSigma), _rotate(rotate), _sortInput(sortInput), _nAdpt(1)
{
  _varList.addTyped<RooAbsReal>(varList);
 
  // copy rho widths
  if(int( _varList.size()) != rho.GetNrows() ) {
     coutE(InputArguments)
        << "ERROR:  RooNDKeysPdf::RooNDKeysPdf() : The vector-size of rho is different from that of varList."
        << "Unable to create the PDF." << std::endl;
     R__ASSERT(int(_varList.size()) == rho.GetNrows());
  }
 
  // negative width factor will serve as a switch in initialize()
  // negative value means that a vector has been provided as input,
  // and that _rho has already been set ...
  _rho.resize( rho.GetNrows() );
  for (Int_t j = 0; j < rho.GetNrows(); j++) {
     _rho[j] = rho[j]; /*cout<<"RooNDKeysPdf ctor, _rho["<<j<<"]="<<_rho[j]<< std::endl;*/
  }
 
  createPdf(true, data); // calls initialize ...
}
 
////////////////////////////////////////////////////////////////////////////////
/// Backward compatibility constructor for (1-dim) RooKeysPdf. If you are a new user,
/// please use the first constructor form.
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, const RooArgList &varList, const RooDataSet &data,
                           const RooArgList &rhoList, TString options, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this), _options(options), _widthFactor(-1.0),
     _nSigma(nSigma), _rotate(rotate), _sortInput(sortInput), _nAdpt(1)
{
   _varList.addTyped<RooAbsReal>(varList);
 
   _rho.resize(rhoList.size(), 1.0);
 
   for (unsigned int i=0; i < rhoList.size(); ++i) {
      const auto rho = rhoList.at(i);
      if (!dynamic_cast<const RooAbsReal *>(rho)) {
         coutE(InputArguments) << "RooNDKeysPdf::ctor(" << GetName() << ") ERROR: parameter " << rho->GetName()
                               << " is not of type RooRealVar" << std::endl;
         assert(0);
      }
      _rhoList.add(*rho);
      _rho[i] = (dynamic_cast<RooAbsReal *>(rho))->getVal();
   }
 
   // copy rho widths
   if ((_varList.size() != _rhoList.size())) {
      coutE(InputArguments) << "ERROR:  RooNDKeysPdf::RooNDKeysPdf() : The size of rhoList is different from varList."
                            << "Unable to create the PDF." << std::endl;
      assert(_varList.size() == _rhoList.size());
   }
 
   // keep track of changes in rho parameters
   _tracker = new RooChangeTracker("tracker", "track rho parameters", _rhoList, true); // check for value updates
   (void)_tracker->hasChanged(true); // first evaluation always true for new parameters (?)
 
   createPdf(true, data);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, const RooArgList &varList, const TH1 &hist,
                           const RooArgList &rhoList, TString options, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this),
     _options(options), _widthFactor(-1), _nSigma(nSigma), _rotate(rotate), _sortInput(sortInput),
     _nAdpt(1)
{
   _varList.addTyped<RooAbsReal>(varList);
 
   // copy rho widths
   _rho.resize(rhoList.size(), 1.0);
 
   for (unsigned int i=0; i < rhoList.size(); ++i) {
      const auto rho = rhoList.at(i);
      if (!dynamic_cast<RooAbsReal *>(rho)) {
         coutE(InputArguments) << "RooNDKeysPdf::ctor(" << GetName() << ") ERROR: parameter " << rho->GetName()
                               << " is not of type RooRealVar" << std::endl;
         assert(0);
      }
      _rhoList.add(*rho);
      _rho[i] = (dynamic_cast<RooAbsReal *>(rho))->getVal();
   }
 
   if ((_varList.size() != _rhoList.size())) {
      coutE(InputArguments) << "ERROR:  RooNDKeysPdf::RooNDKeysPdf() : The size of rhoList is different from varList."
                            << "Unable to create the PDF." << std::endl;
      assert(_varList.size() == _rhoList.size());
   }
 
   // keep track of changes in rho parameters
   _tracker = new RooChangeTracker("tracker", "track rho parameters", _rhoList, true); // check for value updates
   (void)_tracker->hasChanged(true); // first evaluation always true for new parameters (?)
 
   createPdf(true, *std::unique_ptr<RooDataSet>{createDatasetFromHist(varList, hist)});
}
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, RooAbsReal &x, const RooDataSet &data, Mirror mirror,
                           double rho, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this), _options("a"), _widthFactor(rho),
     _nSigma(nSigma), _rotate(rotate), _sortInput(sortInput), _nAdpt(1)
{
   _varList.add(x);
 
   if (mirror != NoMirror) {
      if (mirror != MirrorBoth) {
         coutW(InputArguments) << "RooNDKeysPdf::RooNDKeysPdf() : Warning : asymmetric mirror(s) no longer supported."
                               << std::endl;
      }
      _options = "m";
   }
 
   createPdf(true, data);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Backward compatibility constructor for Roo2DKeysPdf. If you are a new user,
/// please use the first constructor form.
 
RooNDKeysPdf::RooNDKeysPdf(const char *name, const char *title, RooAbsReal &x, RooAbsReal &y, const RooDataSet &data,
                           TString options, double rho, double nSigma, bool rotate, bool sortInput)
   : RooAbsPdf(name, title), _varList("varList", "List of variables", this),
     _rhoList("rhoList", "List of rho parameters", this), _options(options), _widthFactor(rho),
     _nSigma(nSigma), _rotate(rotate), _sortInput(sortInput), _nAdpt(1)
{
   _varList.add(RooArgSet(x, y));
 
   createPdf(true, data);
}
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor
 
RooNDKeysPdf::RooNDKeysPdf(const RooNDKeysPdf &other, const char *name)
   : RooAbsPdf(other, name),
     _varList("varList", this, other._varList),
     _rhoList("rhoList", this, other._rhoList),
     _options(other._options),
     _widthFactor(other._widthFactor),
     _nSigma(other._nSigma),
     _fixedShape(other._fixedShape),
     _mirror(other._mirror),
     _debug(other._debug),
     _verbose(other._verbose),
     _nDim(other._nDim),
     _nEvents(other._nEvents),
     _nEventsM(other._nEventsM),
     _nEventsW(other._nEventsW),
     _d(other._d),
     _n(other._n),
     _dataPts(other._dataPts),
     _dataPtsR(other._dataPtsR),
     _weights0(other._weights0),
     _weights1(other._weights1),
     _weights(_options.Contains("a") ? &_weights1 : &_weights0),
     _sortTVIdcs(other._sortTVIdcs),
     _rho(other._rho),
     _x(other._x),
     _x0(other._x0),
     _x1(other._x1),
     _x2(other._x2),
     _mean(other._mean),
     _sigma(other._sigma),
     _xDatLo(other._xDatLo),
     _xDatHi(other._xDatHi),
     _xDatLo3s(other._xDatLo3s),
     _xDatHi3s(other._xDatHi3s),
     _netFluxZ(other._netFluxZ),
     _nEventsBW(other._nEventsBW),
     _nEventsBMSW(other._nEventsBMSW),
     _xVarLo(other._xVarLo),
     _xVarHi(other._xVarHi),
     _xVarLoM3s(other._xVarLoM3s),
     _xVarLoP3s(other._xVarLoP3s),
     _xVarHiM3s(other._xVarHiM3s),
     _xVarHiP3s(other._xVarHiP3s),
     _bpsIdcs(other._bpsIdcs),
     _ibNoSort(other._ibNoSort),
     _sIdcs(other._sIdcs),
     _bIdcs(other._bIdcs),
     _bmsIdcs(other._bmsIdcs),
     _rangeBoxInfo(other._rangeBoxInfo),
     _fullBoxInfo(other._fullBoxInfo),
     _idx(other._idx),
     _minWeight(other._minWeight),
     _maxWeight(other._maxWeight),
     _wMap(other._wMap),
     _covMat(new TMatrixDSym(*other._covMat)),
     _corrMat(new TMatrixDSym(*other._corrMat)),
     _rotMat(new TMatrixD(*other._rotMat)),
     _sigmaR(new TVectorD(*other._sigmaR)),
     _dx(new TVectorD(*other._dx)),
     _sigmaAvgR(other._sigmaAvgR),
     _rotate(other._rotate),
     _sortInput(other._sortInput),
     _nAdpt(other._nAdpt),
     _tracker(other._tracker != nullptr ? new RooChangeTracker(*other._tracker) : nullptr)
{
}
 
////////////////////////////////////////////////////////////////////////////////
 
RooNDKeysPdf::~RooNDKeysPdf()
{
  if (_covMat)    delete _covMat;
  if (_corrMat)   delete _corrMat;
  if (_rotMat)    delete _rotMat;
  if (_sigmaR)    delete _sigmaR;
  if (_dx)        delete _dx;
  if (_tracker)
     delete _tracker;
 
  // delete all the boxinfos map
  while ( !_rangeBoxInfo.empty() ) {
    map<pair<string,int>,BoxInfo*>::iterator iter = _rangeBoxInfo.begin();
    BoxInfo* box= (*iter).second;
    _rangeBoxInfo.erase(iter);
    delete box;
  }
}
 
////////////////////////////////////////////////////////////////////////////////
/// evaluation order of constructor.
 
void RooNDKeysPdf::createPdf(bool firstCall, RooDataSet const& data)
{
  if (firstCall) {
    // set options
    setOptions();
    // initialization
    initialize(data);
  }
 
 
  // copy dataset, calculate sigma_i's, determine min and max event weight
  loadDataSet(firstCall, data);
 
  // mirror dataset around dataset boundaries -- does not depend on event weights
  if (_mirror) mirrorDataSet();
 
  // store indices and weights of events with high enough weights
  loadWeightSet(data);
 
  // store indices of events in variable boundaries and box shell.
//calculateShell(&_fullBoxInfo);
  // calculate normalization needed in analyticalIntegral()
//calculatePreNorm(&_fullBoxInfo);
 
  // lookup table for determining which events contribute to a certain coordinate
  const_cast<RooNDKeysPdf*>(this)->sortDataIndices();
 
  // determine static and/or adaptive bandwidth
  calculateBandWidth();
}
 
////////////////////////////////////////////////////////////////////////////////
/// set the configuration
 
void RooNDKeysPdf::setOptions()
{
  _options.ToLower();
 
  if( _options.Contains("a") ) { _weights = &_weights1; }
  else                         { _weights = &_weights0; }
  if( _options.Contains("m") )   _mirror = true;
  else                           _mirror = false;
  if( _options.Contains("d") )   _debug  = true;
  else                           _debug  = false;
  if( _options.Contains("v") ) { _debug = true;  _verbose = true; }
  else                         { _debug = false; _verbose = false; }
 
  cxcoutD(InputArguments) << "RooNDKeysPdf::setOptions()    options = " << _options
         << "\n\tbandWidthType    = " << _options.Contains("a")
         << "\n\tmirror           = " << _mirror
         << "\n\tdebug            = " << _debug
         << "\n\tverbose          = " << _verbose
         << std::endl;
 
  if (_nSigma<2.0) {
    coutW(InputArguments) << "RooNDKeysPdf::setOptions() : Warning : nSigma = " << _nSigma << " < 2.0. "
           << "Calculated normalization could be too large."
           << std::endl;
  }
 
  // number of adaptive width iterations. Default is 1.
  if (_options.Contains("a")) {
     if (!sscanf(_options.Data(), "%d%*s", &_nAdpt)) {
        _nAdpt = 1;
     }
  }
}
 
////////////////////////////////////////////////////////////////////////////////
/// initialization
 
void RooNDKeysPdf::initialize(RooDataSet const& data)
{
  _nDim      = _varList.size();
  _nEvents   = (Int_t)data.numEntries();
  _nEventsM  = _nEvents;
  _fixedShape= false;
 
  _netFluxZ = false;
  _nEventsBW = 0;
  _nEventsBMSW = 0;
 
  if(_nDim==0) {
    coutE(InputArguments) << "ERROR:  RooNDKeysPdf::initialize() : The observable list is empty. "
           << "Unable to begin generating the PDF." << std::endl;
    R__ASSERT (_nDim!=0);
  }
 
  if(_nEvents==0) {
    coutE(InputArguments) << "ERROR:  RooNDKeysPdf::initialize() : The input data set is empty. "
           << "Unable to begin generating the PDF." << std::endl;
    R__ASSERT (_nEvents!=0);
  }
 
  _d         = static_cast<double>(_nDim);
 
  std::vector<double> dummy(_nDim,0.);
  _dataPts.resize(_nEvents,dummy);
  _weights0.resize(_nEvents,dummy);
 
  //rdh _rho.resize(_nDim,_widthFactor);
 
  if (_widthFactor>0) { _rho.resize(_nDim,_widthFactor); }
  // else: _rho has been provided as external input
 
  _x.resize(_nDim,0.);
  _x0.resize(_nDim,0.);
  _x1.resize(_nDim,0.);
  _x2.resize(_nDim,0.);
 
  _mean.resize(_nDim,0.);
  _sigma.resize(_nDim,0.);
 
  _xDatLo.resize(_nDim,0.);
  _xDatHi.resize(_nDim,0.);
  _xDatLo3s.resize(_nDim,0.);
  _xDatHi3s.resize(_nDim,0.);
 
  boxInfoInit(&_fullBoxInfo,nullptr,0xFFFF);
 
  _minWeight=0;
  _maxWeight=0;
  _wMap.clear();
 
  _covMat = nullptr;
  _corrMat= nullptr;
  _rotMat = nullptr;
  _sigmaR = nullptr;
  _dx = new TVectorD(_nDim); _dx->Zero();
  _dataPtsR.resize(_nEvents,*_dx);
 
  for(unsigned int j=0; j < _varList.size(); ++j) {
    auto& var = static_cast<const RooRealVar&>(_varList[j]);
    _xDatLo[j] = var.getMin();
    _xDatHi[j] = var.getMax();
  }
}
 
////////////////////////////////////////////////////////////////////////////////
/// copy the dataset and calculate some useful variables
 
void RooNDKeysPdf::loadDataSet(bool firstCall, RooDataSet const& data)
{
  // first some initialization
  _nEventsW=0.;
 
  TMatrixD mat(_nDim,_nDim);
  if (!_covMat)  _covMat = new TMatrixDSym(_nDim);
  if (!_corrMat) _corrMat= new TMatrixDSym(_nDim);
  if (!_rotMat)  _rotMat = new TMatrixD(_nDim,_nDim);
  if (!_sigmaR)  _sigmaR = new TVectorD(_nDim);
 
  mat.Zero();
  _covMat->Zero();
  _corrMat->Zero();
  _rotMat->Zero();
  _sigmaR->Zero();
 
  const RooArgSet* values= data.get();
  vector<RooRealVar*> dVars(_nDim);
  for  (Int_t j=0; j<_nDim; j++) {
    dVars[j] = static_cast<RooRealVar*>(values->find(_varList[j].GetName()));
    _x0[j]=_x1[j]=_x2[j]=0.;
  }
 
  _idx.clear();
  for (Int_t i=0; i<_nEvents; i++) {
 
    data.get(i); // fills dVars
    _idx.push_back(i);
    std::vector<double>& point  = _dataPts[i];
    TVectorD& pointV = _dataPtsR[i];
 
    double myweight = data.weight(); // default is one?
    if ( std::abs(myweight)>_maxWeight ) { _maxWeight = std::abs(myweight); }
    _nEventsW += myweight;
 
    for (Int_t j=0; j<_nDim; j++) {
      for (Int_t k=0; k<_nDim; k++) {
   mat(j,k) += dVars[j]->getVal() * dVars[k]->getVal() * myweight;
      }
      // only need to do once
      if (firstCall)
   point[j] = pointV[j] = dVars[j]->getVal();
 
      _x0[j] += 1. * myweight;
      _x1[j] += point[j] * myweight ;
      _x2[j] += point[j] * point[j] * myweight ;
      if (_x2[j]!=_x2[j]) exit(3);
 
      // only need to do once
      if (firstCall) {
   if (point[j]<_xDatLo[j]) { _xDatLo[j]=point[j]; }
   if (point[j]>_xDatHi[j]) { _xDatHi[j]=point[j]; }
      }
    }
  }
 
  _n = std::pow(4./(_nEventsW*(_d+2.)), 1./(_d+4.)) ;
  // = (4/[n(dim(R) + 2)])^1/(dim(R)+4); dim(R) = 2
  _minWeight = (0.5 - std::erf(_nSigma/sqrt(2.))/2.) * _maxWeight;
 
  for (Int_t j=0; j<_nDim; j++) {
    _mean[j]  = _x1[j]/_x0[j];
    _sigma[j] = sqrt(_x2[j]/_x0[j]-_mean[j]*_mean[j]);
  }
 
  for (Int_t j=0; j<_nDim; j++) {
    for (Int_t k=0; k<_nDim; k++) {
      (*_covMat)(j,k) = mat(j,k)/_x0[j] - _mean[j]*_mean[k];
    }
  }
 
  for (Int_t j=0; j<_nDim; j++) {
    for (Int_t k=0; k<_nDim; k++)
      (*_corrMat)(j,k) = (*_covMat)(j,k)/(_sigma[j]*_sigma[k]) ;
  }
 
  // use raw sigmas (without rho) for sigmaAvgR
  TMatrixDSymEigen evCalculator(*_covMat);
  TVectorD sigmaRraw = evCalculator.GetEigenValues();
  for (Int_t j=0; j<_nDim; j++) { sigmaRraw[j] = sqrt(sigmaRraw[j]); }
 
  _sigmaAvgR=1.;
  for (Int_t j=0; j<_nDim; j++) { _sigmaAvgR *= sigmaRraw[j]; }
  _sigmaAvgR = std::pow(_sigmaAvgR, 1./_d) ;
 
  // find decorrelation matrix and eigenvalues (R)
  if (_nDim > 1 && _rotate) {
     // new: rotation matrix now independent of rho evaluation
     *_rotMat = evCalculator.GetEigenVectors();
     *_rotMat = _rotMat->T(); // transpose
  } else {
     TMatrixD haar(_nDim, _nDim);
     TMatrixD unit(TMatrixD::kUnit, haar);
     *_rotMat = unit;
  }
 
  // update sigmas (rho dependent)
  updateRho();
 
  //// rho no longer used after this.
  //// Now set rho = 1 because sigmaR now contains rho
  // for (Int_t j=0; j<_nDim; j++) { _rho[j] = 1.; }  // reset: important!
 
  if (_verbose) {
     //_covMat->Print();
     _rotMat->Print();
     _corrMat->Print();
     _sigmaR->Print();
  }
 
  if (_nDim > 1 && _rotate) {
     // apply rotation
     for (Int_t i = 0; i < _nEvents; i++) {
        TVectorD &pointR = _dataPtsR[i];
        pointR *= *_rotMat;
     }
  }
 
  coutI(Contents) << "RooNDKeysPdf::loadDataSet(" << this << ")"
                  << "\n Number of events in dataset: " << _nEvents
                  << "\n Weighted number of events in dataset: " << _nEventsW << std::endl;
}
 
////////////////////////////////////////////////////////////////////////////////
/// determine mirror dataset.
/// mirror points are added around the physical boundaries of the dataset
/// Two steps:
/// 1. For each entry, determine if it should be mirrored (the mirror configuration).
/// 2. For each mirror configuration, make the mirror points.
 
void RooNDKeysPdf::mirrorDataSet()
{
  for (Int_t j=0; j<_nDim; j++) {
     _xDatLo3s[j] = _xDatLo[j] + _nSigma * (_n * _sigma[j]);
     _xDatHi3s[j] = _xDatHi[j] - _nSigma * (_n * _sigma[j]);
 
     // std::cout<<"xDatLo3s["<<j<<"]="<<_xDatLo3s[j]<< std::endl;
     // std::cout<<"xDatHi3s["<<j<<"]="<<_xDatHi3s[j]<< std::endl;
  }
 
  vector<double> dummy(_nDim,0.);
 
  // 1.
  for (Int_t i=0; i<_nEvents; i++) {
    vector<double>& x = _dataPts[i];
 
    Int_t size = 1;
    vector<vector<double> > mpoints(size,dummy);
    vector<vector<Int_t> > mjdcs(size);
 
    // create all mirror configurations for event i
    for (Int_t j=0; j<_nDim; j++) {
 
      vector<Int_t>& mjdxK = mjdcs[0];
      vector<double>& mpointK = mpoints[0];
 
      // single mirror *at physical boundaries*
      if ((x[j]>_xDatLo[j] && x[j]<_xDatLo3s[j]) && x[j]<(_xDatLo[j]+_xDatHi[j])/2.) {
   mpointK[j] = 2.*_xDatLo[j]-x[j];
   mjdxK.push_back(j);
      } else if ((x[j]<_xDatHi[j] && x[j]>_xDatHi3s[j]) && x[j]>(_xDatLo[j]+_xDatHi[j])/2.) {
   mpointK[j] = 2.*_xDatHi[j]-x[j];
   mjdxK.push_back(j);
      }
    }
 
    vector<Int_t>& mjdx0 = mjdcs[0];
    // no mirror point(s) for this event
    if (size==1 && mjdx0.empty()) continue;
 
    // 2.
    // generate all mirror points for event i
    vector<Int_t>& mjdx = mjdcs[0];
    vector<double>& mpoint = mpoints[0];
 
    // number of mirror points for this mirror configuration
    Int_t eMir = 1 << mjdx.size();
    vector<vector<double> > epoints(eMir,x);
 
    for (Int_t m=0; m<Int_t(mjdx.size()); m++) {
      Int_t size1 = 1 << m;
      Int_t size2 = 1 << (m+1);
      // copy all previous mirror points
      for (Int_t l=size1; l<size2; ++l) {
   epoints[l] = epoints[l-size1];
   // fill high mirror points
   vector<double>& epoint = epoints[l];
   epoint[mjdx[Int_t(mjdx.size()-1)-m]] = mpoint[mjdx[Int_t(mjdx.size()-1)-m]];
      }
    }
 
    // remove duplicate mirror points
    // note that: first epoint == x
    epoints.erase(epoints.begin());
 
    // add mirror points of event i to total dataset
    TVectorD pointR(_nDim);
 
    for (Int_t m=0; m<Int_t(epoints.size()); m++) {
      _idx.push_back(i);
      _dataPts.push_back(epoints[m]);
      //_weights0.push_back(_weights0[i]);
      for (Int_t j=0; j<_nDim; j++) { pointR[j] = (epoints[m])[j]; }
      if (_nDim > 1 && _rotate) {
         pointR *= *_rotMat;
      }
      _dataPtsR.push_back(pointR);
    }
 
    epoints.clear();
    mpoints.clear();
    mjdcs.clear();
  } // end of event loop
 
  _nEventsM = Int_t(_dataPts.size());
}
 
////////////////////////////////////////////////////////////////////////////////
 
void RooNDKeysPdf::loadWeightSet(RooDataSet const& data)
{
  _wMap.clear();
 
  for (Int_t i=0; i<_nEventsM; i++) {
    data.get(_idx[i]);
    double myweight = data.weight();
    //if ( std::abs(myweight)>_minWeight ) {
      _wMap[i] = myweight;
    //}
  }
 
  coutI(Contents) << "RooNDKeysPdf::loadWeightSet(" << this << ") : Number of weighted events : " << _wMap.size() << std::endl;
}
 
////////////////////////////////////////////////////////////////////////////////
/// determine points in +/- nSigma shell around the box determined by the variable
/// ranges. These points are needed in the normalization, to determine probability
/// leakage in and out of the box.
 
void RooNDKeysPdf::calculateShell(BoxInfo* bi) const
{
  for (Int_t j=0; j<_nDim; j++) {
    if (bi->xVarLo[j]==_xDatLo[j] && bi->xVarHi[j]==_xDatHi[j]) {
      bi->netFluxZ = bi->netFluxZ && true;
    } else { bi->netFluxZ = false; }
 
    bi->xVarLoM3s[j] = bi->xVarLo[j] - _nSigma * (_n * _sigma[j]);
    bi->xVarLoP3s[j] = bi->xVarLo[j] + _nSigma * (_n * _sigma[j]);
    bi->xVarHiM3s[j] = bi->xVarHi[j] - _nSigma * (_n * _sigma[j]);
    bi->xVarHiP3s[j] = bi->xVarHi[j] + _nSigma * (_n * _sigma[j]);
 
    //cout<<"bi->xVarLoM3s["<<j<<"]="<<bi->xVarLoM3s[j]<< std::endl;
    //cout<<"bi->xVarLoP3s["<<j<<"]="<<bi->xVarLoP3s[j]<< std::endl;
    //cout<<"bi->xVarHiM3s["<<j<<"]="<<bi->xVarHiM3s[j]<< std::endl;
    //cout<<"bi->xVarHiM3s["<<j<<"]="<<bi->xVarHiM3s[j]<< std::endl;
  }
 
  //for (Int_t i=0; i<_nEventsM; i++) {
  for (const auto& wMapItr : _wMap) {
    Int_t i = wMapItr.first;
 
    const vector<double>& x = _dataPts[i];
    bool inVarRange(true);
    bool inVarRangePlusShell(true);
 
    for (Int_t j=0; j<_nDim; j++) {
 
      if (x[j]>bi->xVarLo[j] && x[j]<bi->xVarHi[j]) {
        inVarRange = inVarRange && true;
      } else { inVarRange = inVarRange && false; }
 
      if (x[j]>bi->xVarLoM3s[j] && x[j]<bi->xVarHiP3s[j]) {
        inVarRangePlusShell = inVarRangePlusShell && true;
      } else { inVarRangePlusShell = inVarRangePlusShell && false; }
    }
 
    // event in range?
    if (inVarRange) {
      bi->bIdcs.push_back(i);
    }
 
    // event in shell?
    if (inVarRangePlusShell) {
      bi->bpsIdcs[i] = true;
      bool inShell(false);
      for (Int_t j=0; j<_nDim; j++) {
        if ((x[j]>bi->xVarLoM3s[j] && x[j]<bi->xVarLoP3s[j]) && x[j]<(bi->xVarLo[j]+bi->xVarHi[j])/2.) {
          inShell = true;
        } else if ((x[j]>bi->xVarHiM3s[j] && x[j]<bi->xVarHiP3s[j]) && x[j]>(bi->xVarLo[j]+bi->xVarHi[j])/2.) {
          inShell = true;
        }
      }
      if (inShell) bi->sIdcs.push_back(i); // needed for normalization
      else {
        bi->bmsIdcs.push_back(i);          // idem
      }
    }
  }
 
  coutI(Contents) << "RooNDKeysPdf::calculateShell() : "
        << "\n Events in shell " << bi->sIdcs.size()
        << "\n Events in box " << bi->bIdcs.size()
        << "\n Events in box and shell " << bi->bpsIdcs.size()
        << std::endl;
}
 
////////////////////////////////////////////////////////////////////////////////
///bi->nEventsBMSW=0.;
///bi->nEventsBW=0.;
 
void RooNDKeysPdf::calculatePreNorm(BoxInfo* bi) const
{
  // box minus shell
  for (Int_t i=0; i<Int_t(bi->bmsIdcs.size()); i++)
    bi->nEventsBMSW += _wMap.at(bi->bmsIdcs[i]);
 
  // box
  for (Int_t i=0; i<Int_t(bi->bIdcs.size()); i++)
    bi->nEventsBW += _wMap.at(bi->bIdcs[i]);
 
  cxcoutD(Eval) << "RooNDKeysPdf::calculatePreNorm() : "
         << "\n nEventsBMSW " << bi->nEventsBMSW
         << "\n nEventsBW " << bi->nEventsBW
         << std::endl;
}
 
////////////////////////////////////////////////////////////////////////////////
/// sort entries, as needed for loopRange()
 
void RooNDKeysPdf::sortDataIndices(BoxInfo* bi)
{
  // will loop over all events by default
  if (!_sortInput) {
    _ibNoSort.clear();
    for (unsigned int i = 0; i < _dataPtsR.size(); ++i) {
      _ibNoSort[i] = true;
    }
    return;
  }
 
  itVec itrVecR;
  vector<TVectorD>::iterator dpRItr = _dataPtsR.begin();
  for (Int_t i = 0; dpRItr != _dataPtsR.end(); ++dpRItr, ++i) {
    if (bi) {
      if (bi->bpsIdcs.find(i) != bi->bpsIdcs.end()) {
        // if (_wMap.find(i)!=_wMap.end())
        itrVecR.push_back(itPair(i, dpRItr));
      }
    } else
      itrVecR.push_back(itPair(i, dpRItr));
  }
 
  _sortTVIdcs.resize(_nDim);
  for (Int_t j=0; j<_nDim; j++) {
    _sortTVIdcs[j].clear();
    sort(itrVecR.begin(), itrVecR.end(), [=](const itPair& a, const itPair& b) {
      return (*a.second)[j] < (*b.second)[j];
    });
    _sortTVIdcs[j] = itrVecR;
  }
 
  for (Int_t j=0; j<_nDim; j++) {
    cxcoutD(Eval) << "RooNDKeysPdf::sortDataIndices() : Number of sorted events : " << _sortTVIdcs[j].size() << std::endl;
  }
}
 
////////////////////////////////////////////////////////////////////////////////
 
void RooNDKeysPdf::calculateBandWidth()
{
  cxcoutD(Eval) << "RooNDKeysPdf::calculateBandWidth()" << std::endl;
 
  const bool adaptive = _options.Contains("a");
  if (_weights != &_weights1 || _weights != &_weights0) {
    _weights = adaptive ? &_weights1 : &_weights0;
  }
 
  // non-adaptive bandwidth
  // (default, and needed to calculate adaptive bandwidth)
 
  if(!adaptive) {
      cxcoutD(Eval) << "RooNDKeysPdf::calculateBandWidth() Using static bandwidth." << std::endl;
  }
 
  // fixed width approximation
  for (Int_t i=0; i<_nEvents; i++) {
    vector<double>& weight = _weights0[i];
    for (Int_t j = 0; j < _nDim; j++) {
       weight[j] = _n * (*_sigmaR)[j];
       // std::cout<<"j: "<<j<<", _n: "<<_n<<", sigmaR="<<(*_sigmaR)[j]<<", weight="<<weight[j]<< std::endl;
    }
  }
 
  // adaptive width
  if (adaptive) {
     cxcoutD(Eval) << "RooNDKeysPdf::calculateBandWidth() Using adaptive bandwidth." << std::endl;
 
     double sqrt12 = sqrt(12.);
     double sqrtSigmaAvgR = sqrt(_sigmaAvgR);
 
     vector<double> dummy(_nDim, 0.);
     _weights1.resize(_nEvents, dummy);
 
     std::vector<std::vector<double>> *weights_prev(nullptr);
     std::vector<std::vector<double>> *weights_new(nullptr);
 
     // std::cout << "Number of adaptive iterations: " << _nAdpt << std::endl;
 
     for (Int_t k = 1; k <= _nAdpt; ++k) {
 
        // std::cout << "  Cycle: " << k << std::endl;
 
        // if multiple adaptive iterations, need to swap weight sets
        if (k % 2) {
           weights_prev = &_weights0;
           weights_new = &_weights1;
        } else {
           weights_prev = &_weights1;
           weights_new = &_weights0;
        }
 
        for (Int_t i = 0; i < _nEvents; ++i) {
           vector<double> &x = _dataPts[i];
           double f = std::pow(gauss(x, *weights_prev) / _nEventsW, -1. / (2. * _d));
 
           vector<double> &weight = (*weights_new)[i];
           for (Int_t j = 0; j < _nDim; j++) {
              double norm = (_n * (*_sigmaR)[j]) / sqrtSigmaAvgR;
              weight[j] = norm * f / sqrt12; //  note additional factor of sqrt(12) compared with HEP-EX/0011057
           }
        }
     }
     // this is the latest updated weights set
     _weights = weights_new;
  }
}
 
////////////////////////////////////////////////////////////////////////////////
/// loop over all closest point to x, as determined by loopRange()
 
double RooNDKeysPdf::gauss(vector<double>& x, vector<vector<double> >& weights) const
{
  if(_nEvents==0) return 0.;
 
  const double sqrt2pi = std::sqrt(TMath::TwoPi());
 
  double z=0.;
  std::vector<int> indices;
 
  // determine input loop range for event x
  if (_sortInput) {
    if (_sortTVIdcs.empty())
      const_cast<RooNDKeysPdf*>(this)->sortDataIndices();
 
    loopRange(x, indices);
  } else {
    indices.reserve(_ibNoSort.size());
    for (const auto& ibMapItr : _ibNoSort) {
       indices.push_back(ibMapItr.first);
    }
  }
 
  for (const auto& i : indices) {
     double g(1.);
 
     if (i >= (Int_t)_idx.size()) {
        continue;
     } //---> 1.myline
 
     const vector<double> &point = _dataPts[i];
     const vector<double> &weight = weights[_idx[i]];
 
     for (Int_t j = 0; j < _nDim; j++) {
        (*_dx)[j] = x[j] - point[j];
     }
 
     if (_nDim > 1 && _rotate) {
        *_dx *= *_rotMat; // rotate to decorrelated frame!
     }
 
     for (Int_t j = 0; j < _nDim; j++) {
        double r = (*_dx)[j]; // x[j] - point[j];
        double c = 1. / (2. * weight[j] * weight[j]);
 
        // std::cout << "j = " << j << " x[j] = " << point[j] << " w = " << weight[j] << std::endl;
 
        g *= exp(-c * r * r);
        g *= 1. / (sqrt2pi * weight[j]);
     }
     z += (g * _wMap.at(_idx[i]));
  }
  return z;
}
 
////////////////////////////////////////////////////////////////////////////////
/// determine closest points to x, to loop over in evaluate()
 
void RooNDKeysPdf::loopRange(vector<double>& x, std::vector<Int_t>& ibMap) const
{
   ibMap.clear();
   TVectorD xRm(_nDim);
   TVectorD xRp(_nDim);
 
   for (Int_t j = 0; j < _nDim; j++) {
      xRm[j] = xRp[j] = x[j];
   }
 
   if (_nDim > 1 && _rotate) {
      xRm *= *_rotMat;
      xRp *= *_rotMat;
   }
   for (Int_t j = 0; j < _nDim; j++) {
      xRm[j] -= _nSigma * (_n * (*_sigmaR)[j]);
      xRp[j] += _nSigma * (_n * (*_sigmaR)[j]);
  }
 
  std::vector<TVectorD> xvecRm(1,xRm);
  std::vector<TVectorD> xvecRp(1,xRp);
 
  // To remember the indices that were selected in the previous dimensions
  std::vector<Int_t> ibMapRT;
 
  for (Int_t j=0; j<_nDim; j++) {
    ibMap.clear();
    auto comp = [=](const itPair& a, const itPair& b) {
      return (*a.second)[j] < (*b.second)[j];
    };
 
    // Figure out which elements are to be selected in the current dimension
    auto lo = std::lower_bound(_sortTVIdcs[j].begin(), _sortTVIdcs[j].end(), itPair(0, xvecRm.begin()), comp);
    auto hi = std::upper_bound(_sortTVIdcs[j].begin(), _sortTVIdcs[j].end(), itPair(0, xvecRp.begin()), comp);
    auto it = lo;
 
    if (j==0) {
      // In the first dimension, we can skip straight to filling the remembered
      // indices for the previous dimension, or in the 1-d case, just fill the
      // output map.
      auto& m = _nDim==1 ? ibMap : ibMapRT;
      m.reserve(std::distance(lo, hi));
      for (it=lo; it!=hi; ++it) {
        m.push_back(it->first);
      }
      // Sort by indices so we can use binary search
      std::sort(m.begin(), m.end());
      continue;
    }
 
    // Iterate over elements in current dimension and add them to the list if
    // they were also selected in the previous dimension.
    for (it=lo; it!=hi; ++it) {
      // Look up in ibMapRT with binary search
      auto found = std::lower_bound(ibMapRT.begin(), ibMapRT.end(), it->first);
      if (found != ibMapRT.end() && !(it->first < *found)) {
          ibMap.push_back(it->first);
      }
    }
    // Sort by indices so we can use binary search
    std::sort(ibMap.begin(), ibMap.end());
 
    ibMapRT.clear();
    if (j!=_nDim-1) { ibMapRT = std::move(ibMap); }
  }
}
 
////////////////////////////////////////////////////////////////////////////////
 
void RooNDKeysPdf::boxInfoInit(BoxInfo* bi, const char* rangeName, Int_t /*code*/) const
{
  vector<bool> doInt(_nDim,true);
 
  bi->filled = false;
 
  bi->xVarLo.resize(_nDim,0.);
  bi->xVarHi.resize(_nDim,0.);
  bi->xVarLoM3s.resize(_nDim,0.);
  bi->xVarLoP3s.resize(_nDim,0.);
  bi->xVarHiM3s.resize(_nDim,0.);
  bi->xVarHiP3s.resize(_nDim,0.);
 
  bi->netFluxZ = true;
  bi->bpsIdcs.clear();
  bi->bIdcs.clear();
  bi->sIdcs.clear();
  bi->bmsIdcs.clear();
 
  bi->nEventsBMSW=0.;
  bi->nEventsBW=0.;
 
  for (unsigned int j=0; j < _varList.size(); ++j) {
    auto var = static_cast<const RooAbsRealLValue*>(_varList.at(j));
    if (doInt[j]) {
      bi->xVarLo[j] = var->getMin(rangeName);
      bi->xVarHi[j] = var->getMax(rangeName);
    } else {
      bi->xVarLo[j] = var->getVal() ;
      bi->xVarHi[j] = var->getVal() ;
    }
  }
}
 
////////////////////////////////////////////////////////////////////////////////
 
double RooNDKeysPdf::evaluate() const
{
   if ( (_tracker && _tracker->hasChanged(true)) || (_weights != &_weights0 && _weights != &_weights1) ) {
      updateRho(); // update internal rho parameters
      // redetermine static and/or adaptive bandwidth
      const_cast<RooNDKeysPdf*>(this)->calculateBandWidth();
   }
 
   const RooArgSet *nset = _varList.nset();
   for (unsigned int j=0; j < _varList.size(); ++j) {
     auto var = static_cast<const RooAbsReal*>(_varList.at(j));
     _x[j] = var->getVal(nset);
   }
 
  double val = gauss(_x,*_weights);
  //cout<<"returning "<<val<< std::endl;
 
  if (val >= 1E-20) {
     return val;
  } else {
     return (1E-20);
  }
}
 
////////////////////////////////////////////////////////////////////////////////
 
Int_t RooNDKeysPdf::getAnalyticalIntegral(RooArgSet& allVars, RooArgSet& analVars, const char* rangeName) const
{
  if (rangeName) return 0 ;
 
  Int_t code=0;
  if (matchArgs(allVars,analVars,RooArgSet(_varList))) { code=1; }
 
  return code;
 
}
 
////////////////////////////////////////////////////////////////////////////////
 
double RooNDKeysPdf::analyticalIntegral(Int_t code, const char* rangeName) const
{
  checkInitWeights();
 
  cxcoutD(Eval) << "Calling RooNDKeysPdf::analyticalIntegral(" << GetName() << ") with code " << code
         << " and rangeName " << (rangeName?rangeName:"<none>") << std::endl;
 
  // determine which observables need to be integrated over ...
  Int_t nComb = 1 << (_nDim);
  R__ASSERT(code>=1 && code<nComb) ;
 
  vector<bool> doInt(_nDim,true);
 
  // get BoxInfo
  BoxInfo* bi(nullptr);
 
  if (rangeName) {
    string rangeNameStr(rangeName) ;
    bi = _rangeBoxInfo[make_pair(rangeNameStr,code)] ;
    if (!bi) {
      bi = new BoxInfo ;
      _rangeBoxInfo[make_pair(rangeNameStr,code)] = bi ;
      boxInfoInit(bi,rangeName,code);
    }
  } else bi= &_fullBoxInfo ;
 
  // have boundaries changed?
  bool newBounds(false);
  for (unsigned int j=0; j < _varList.size(); ++j) {
    auto var = static_cast<const RooAbsRealLValue*>(_varList.at(j));
    if ((var->getMin(rangeName)-bi->xVarLo[j]!=0) ||
        (var->getMax(rangeName)-bi->xVarHi[j]!=0)) {
      newBounds = true;
    }
  }
 
  // reset
  if (newBounds) {
    cxcoutD(Eval) << "RooNDKeysPdf::analyticalIntegral() : Found new boundaries ... " << (rangeName?rangeName:"<none>") << std::endl;
    boxInfoInit(bi,rangeName,code);
  }
 
  // recalculates netFluxZero and nEventsIR
  if (!bi->filled || newBounds) {
    // Fill box info with contents
    calculateShell(bi);
    calculatePreNorm(bi);
    bi->filled = true;
    const_cast<RooNDKeysPdf*>(this)->sortDataIndices(bi);
  }
 
  // first guess
  double norm=bi->nEventsBW;
 
  if (_mirror && bi->netFluxZ) {
    // KEYS expression is self-normalized
    cxcoutD(Eval) << "RooNDKeysPdf::analyticalIntegral() : Using mirrored normalization : " << bi->nEventsBW << std::endl;
    return bi->nEventsBW;
  }
  // calculate leakage in and out of variable range box
  else
  {
    norm = bi->nEventsBMSW;
    if (norm<0.) norm=0.;
 
    for (Int_t i=0; i<Int_t(bi->sIdcs.size()); ++i) {
      double prob=1.;
      const vector<double>& x = _dataPts[bi->sIdcs[i]];
      const vector<double>& weight = (*_weights)[_idx[bi->sIdcs[i]]];
 
      vector<double> chi(_nDim,100.);
 
      for (Int_t j=0; j<_nDim; j++) {
   if(!doInt[j]) continue;
 
   if ((x[j] > bi->xVarLoM3s[j] && x[j] < bi->xVarLoP3s[j]) && x[j] < (bi->xVarLo[j] + bi->xVarHi[j]) / 2.) {
              chi[j] = (x[j] - bi->xVarLo[j]) / weight[j];
   } else if ((x[j] > bi->xVarHiM3s[j] && x[j] < bi->xVarHiP3s[j]) && x[j] > (bi->xVarLo[j] + bi->xVarHi[j]) / 2.) {
              chi[j] = (bi->xVarHi[j] - x[j]) / weight[j];
   }
 
   if (chi[j] > 0) { // inVarRange
              prob *= (0.5 + std::erf(std::abs(chi[j]) / sqrt(2.)) / 2.);
   } else { // outside Var range
              prob *= (0.5 - std::erf(std::abs(chi[j]) / sqrt(2.)) / 2.);
   }
      }
 
      norm += prob * _wMap.at(_idx[bi->sIdcs[i]]);
    }
 
    cxcoutD(Eval) << "RooNDKeysPdf::analyticalIntegral() : Final normalization : " << norm << " " << bi->nEventsBW << std::endl;
    return norm;
  }
}
 
 
////////////////////////////////////////////////////////////////////////////////
 
RooDataSet* RooNDKeysPdf::createDatasetFromHist(const RooArgList &varList, const TH1 &hist) const
{
   RooArgSet varSet{varList};
   std::vector<RooRealVar *> varVec;
 
   for (const auto var : varList) {
      if (!dynamic_cast<RooRealVar *>(var)) {
         coutE(InputArguments) << "RooNDKeysPdf::createDatasetFromHist(" << GetName() << ") WARNING: variable "
                               << var->GetName() << " is not of type RooRealVar. Skip." << std::endl;
         continue;
      }
      varVec.push_back(static_cast<RooRealVar *>(var)); // used for setting the variables.
   }
 
   /// determine histogram dimensionality
   unsigned int histndim(0);
   std::string classname = hist.ClassName();
   if (classname.find("TH1") == 0) {
      histndim = 1;
   } else if (classname.find("TH2") == 0) {
      histndim = 2;
   } else if (classname.find("TH3") == 0) {
      histndim = 3;
   }
   assert(histndim == varVec.size());
 
   if (histndim > 3 || histndim <= 0) {
      coutE(InputArguments) << "RooNDKeysPdf::createDatasetFromHist(" << GetName()
                            << ") ERROR: input histogram dimension not between [1-3]: " << histndim << std::endl;
      assert(0);
   }
 
   /// dataset creation
   RooDataSet *dataFromHist = new RooDataSet("datasetFromHist", "datasetFromHist", varSet, RooFit::WeightVar());
 
   /// dataset filling
   for (int i = 1; i <= hist.GetXaxis()->GetNbins(); ++i) {
      // 1 or more dimension
 
      double xval = hist.GetXaxis()->GetBinCenter(i);
      varVec[0]->setVal(xval);
 
      if (varVec.size() == 1) {
         double fval = hist.GetBinContent(i);
         dataFromHist->add(varSet, fval);
      } else { // 2 or more dimensions
 
         for (int j = 1; j <= hist.GetYaxis()->GetNbins(); ++j) {
            double yval = hist.GetYaxis()->GetBinCenter(j);
            varVec[1]->setVal(yval);
 
            if (varVec.size() == 2) {
               double fval = hist.GetBinContent(i, j);
               dataFromHist->add(varSet, fval);
            } else { // 3 dimensions
 
               for (int k = 1; k <= hist.GetZaxis()->GetNbins(); ++k) {
                  double zval = hist.GetZaxis()->GetBinCenter(k);
                  varVec[2]->setVal(zval);
 
                  double fval = hist.GetBinContent(i, j, k);
                  dataFromHist->add(varSet, fval);
               }
            }
         }
      }
   }
 
   return dataFromHist;
}
 
 
////////////////////////////////////////////////////////////////////////////////
/// Return evaluated weights
TMatrixD RooNDKeysPdf::getWeights(const int &k) const
{
  checkInitWeights();
 
  TMatrixD mref(_nEvents, _nDim + 1);
 
   cxcoutD(Eval) << "RooNDKeysPdf::getWeights() Return evaluated weights." << std::endl;
 
   for (Int_t i = 0; i < _nEvents; ++i) {
      const vector<double>& x = _dataPts[i];
      for (Int_t j = 0; j < _nDim; j++) {
         mref(i, j) = x[j];
      }
 
      const vector<double>& weight = (*_weights)[i];
      mref(i, _nDim) = weight[k];
   }
 
   return mref;
}
 
   ////////////////////////////////////////////////////////////////////////////////
void RooNDKeysPdf::updateRho() const
{
   for (unsigned int j = 0; j < _rhoList.size(); ++j) {
     auto rho = static_cast<const RooAbsReal*>(_rhoList.at(j));
     _rho[j] = rho->getVal();
   }
 
   if (_nDim > 1 && _rotate) {
      TMatrixDSym covMatRho(_nDim); // covariance matrix times rho parameters
      for (Int_t j = 0; j < _nDim; j++) {
         for (Int_t k = 0; k < _nDim; k++) {
            covMatRho(j, k) = (*_covMat)(j, k) * _rho[j] * _rho[k];
         }
      }
      // find decorrelation matrix and eigenvalues (R)
      TMatrixDSymEigen evCalculatorRho(covMatRho);
      *_sigmaR = evCalculatorRho.GetEigenValues();
      for (Int_t j = 0; j < _nDim; j++) {
         (*_sigmaR)[j] = sqrt((*_sigmaR)[j]);
      }
   } else {
      for (Int_t j = 0; j < _nDim; j++) {
         (*_sigmaR)[j] = (_sigma[j] * _rho[j]);
      } // * rho
   }
}