RooPoisson.cxx

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 /*****************************************************************************
  * Project: RooFit                                                           *
  *                                                                           *
  * Simple Poisson PDF
  * author: Kyle Cranmer <cranmer@cern.ch>
  *                                                                           *
  *****************************************************************************/
 
/** \class RooPoisson
    \ingroup Roofit
 
Poisson pdf
**/
 
#include "RooPoisson.h"
#include "RooRandom.h"
#include "RooMath.h"
#include "RooNaNPacker.h"
#include "RooBatchCompute.h"
 
#include "Math/ProbFuncMathCore.h"
 
ClassImp(RooPoisson);
 
////////////////////////////////////////////////////////////////////////////////
/// Constructor
 
RooPoisson::RooPoisson(const char *name, const char *title,
             RooAbsReal& _x,
             RooAbsReal& _mean,
             bool noRounding) :
  RooAbsPdf(name,title),
  x("x","x",this,_x),
  mean("mean","mean",this,_mean),
  _noRounding(noRounding)
{
}
 
////////////////////////////////////////////////////////////////////////////////
/// Copy constructor
 
 RooPoisson::RooPoisson(const RooPoisson& other, const char* name) :
   RooAbsPdf(other,name),
   x("x",this,other.x),
   mean("mean",this,other.mean),
   _noRounding(other._noRounding),
   _protectNegative(other._protectNegative)
{
}
 
////////////////////////////////////////////////////////////////////////////////
/// Implementation in terms of the TMath::Poisson() function.
 
double RooPoisson::evaluate() const
{
  double k = _noRounding ? x : floor(x);
  if(_protectNegative && mean<0) {
    RooNaNPacker np;
    np.setPayload(-mean);
    return np._payload;
  }
  return TMath::Poisson(k,mean) ;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Compute multiple values of the Poisson distribution.
void RooPoisson::computeBatch(cudaStream_t *stream, double *output, size_t nEvents,
                              RooFit::Detail::DataMap const &dataMap) const
{
   auto dispatch = stream ? RooBatchCompute::dispatchCUDA : RooBatchCompute::dispatchCPU;
   RooBatchCompute::ArgVector extraArgs{static_cast<double>(_protectNegative), static_cast<double>(_noRounding)};
   dispatch->compute(stream, RooBatchCompute::Poisson, output, nEvents, {dataMap.at(x), dataMap.at(mean)}, extraArgs);
}
 
////////////////////////////////////////////////////////////////////////////////
 
Int_t RooPoisson::getAnalyticalIntegral(RooArgSet& allVars, RooArgSet& analVars, const char* /*rangeName*/) const
{
  if (matchArgs(allVars,analVars,x)) return 1 ;
  if (matchArgs(allVars, analVars, mean)) return 2;
  return 0 ;
}
 
////////////////////////////////////////////////////////////////////////////////
 
double RooPoisson::analyticalIntegral(Int_t code, const char* rangeName) const
{
  R__ASSERT(code == 1 || code == 2) ;
 
  const double mu = mean; // evaluating the proxy once for less overhead
 
  if(_protectNegative && mu < 0.0) {
    return std::exp(-2.0 * mu); // make it fall quickly
  }
 
  if (code == 1) {
    // Implement integral over x as summation. Add special handling in case
    // range boundaries are not on integer values of x
    const double xmin = std::max(0., x.min(rangeName));
    const double xmax = x.max(rangeName);
 
    if (xmax < 0. || xmax < xmin) {
      return 0.;
    }
    const double delta = 100.0 * std::sqrt(mu);
    // If the limits are more than many standard deviations away from the mean,
    // we might as well return the integral of the full Poisson distribution to
    // save computing time.
    if (xmin < std::max(mu - delta, 0.0) && xmax > mu + delta) {
      return 1.;
    }
 
    // The range as integers. ixmin is included, ixmax outside.
    const unsigned int ixmin = xmin;
    const unsigned int ixmax = std::min(xmax + 1.,
                                        (double)std::numeric_limits<unsigned int>::max());
 
    // Sum from 0 to just before the bin outside of the range.
    if (ixmin == 0) {
      return ROOT::Math::poisson_cdf(ixmax - 1, mu);
    }
    else {
      // If necessary, subtract from 0 to the beginning of the range
      if (ixmin <= mu) {
        return ROOT::Math::poisson_cdf(ixmax - 1, mu) - ROOT::Math::poisson_cdf(ixmin - 1, mu);
      }
      else {
        //Avoid catastrophic cancellation in the high tails:
        return ROOT::Math::poisson_cdf_c(ixmin - 1, mu) - ROOT::Math::poisson_cdf_c(ixmax - 1, mu);
      }
 
    }
 
  } else if(code == 2) {
 
    // the integral with respect to the mean is the integral of a gamma distribution
 
    // negative ix does not need protection (gamma returns 0.0)
    const double ix = _noRounding ? x + 1 : int(TMath::Floor(x)) + 1.0;
 
    using ROOT::Math::gamma_cdf;
    return gamma_cdf(mean.max(rangeName), ix, 1.0) - gamma_cdf(mean.min(rangeName), ix, 1.0);
  }
 
  return 0.0;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Advertise internal generator in x
 
Int_t RooPoisson::getGenerator(const RooArgSet& directVars, RooArgSet &generateVars, bool /*staticInitOK*/) const
{
  if (matchArgs(directVars,generateVars,x)) return 1 ;
  return 0 ;
}
 
////////////////////////////////////////////////////////////////////////////////
/// Implement internal generator using TRandom::Poisson
 
void RooPoisson::generateEvent(Int_t code)
{
  R__ASSERT(code==1) ;
  double xgen ;
  while(1) {
    xgen = RooRandom::randomGenerator()->Poisson(mean);
    if (xgen<=x.max() && xgen>=x.min()) {
      x = xgen ;
      break;
    }
  }
  return;
}