// @(#)root/mathcore:$Id$
// Author: L. Moneta Fri Sep 22 15:06:47 2006

 *                                                                    *
 * Copyright (c) 2006  LCG ROOT Math Team, CERN/PH-SFT                *
 *                                                                    *
 *                                                                    *

// Header file for class DistSampler

#ifndef ROOT_Math_DistSampler
#define ROOT_Math_DistSampler

#ifndef ROOT_Math_IFunctionfwd
#include "Math/IFunctionfwd.h"

#ifndef ROOT_Math_WrappedFunction
#include "Math/WrappedFunction.h"

#include <vector>
#include <cassert>

class TRandom;

namespace ROOT {

   namespace Fit {

      class DataRange;
      class BinData;
      class UnBinData;

   namespace Math {

      class DistSamplerOptions;

   @defgroup Random Random number generators and generation of random number distributions
   @ingroup Random

   Classes implementing random number generators and classes implementing generation of random numbers
   according to arbitrary distributions

   Interface class for generic sampling of a distribution,
   i.e. generating random numbers according to arbitrary distributions

   @ingroup Random

class DistSampler {


   /// default constructor
   DistSampler() : fOwnFunc(false), fRange(0), fFunc(0) {}

   /// virtual destructor
   virtual ~DistSampler();

   /// set the parent function distribution to use for sampling (generic case)
   template<class Function>
   void SetFunction(Function & func, unsigned int dim) {
      WrappedMultiFunction<Function &> wf(func, dim);
      // need to clone to avoid temporary

   /// set the parent function distribution to use for random sampling (one dim case)
   virtual void SetFunction(const ROOT::Math::IGenFunction & func)  {
      SetFunction<const ROOT::Math::IGenFunction>(func, 1);

   /// set the parent function distribution to use for random sampling (multi-dim case)
   virtual void SetFunction(const ROOT::Math::IMultiGenFunction & func)  {

   /// return the dimension of the parent distribution (and the data)
   unsigned int NDim() const { return fData.size(); }

      initialize the generators with the given algorithm
      Implemented by derived classes who needs it
      (like UnuranSampler)
      If nothing is specified use default algorithm
      from DistSamplerOptions::SetDefaultAlgorithm
   virtual bool Init(const char * =""/* algorithm */) { return true;}

      initialize the generators with the given option
      which my include the algorithm but also more if
      the method is re-impelmented by derived class
      The default implementation calls the above method
      passing just the algorithm name
   virtual bool Init(const DistSamplerOptions & opt );

       Set the random engine to be used
       To be implemented by the derived classes who provides
       random sampling
   virtual void SetRandom(TRandom *  ) {}

       Set the random seed for the TRandom instances used by the sampler
       To be implemented by the derived classes who provides random sampling
   virtual void SetSeed(unsigned int /*seed*/ ) {}

      Get the random engine used by the sampler
      To be implemented by the derived classes who needs it
      Returns zero by default
   virtual TRandom * GetRandom() { return 0; }

   /// set range in a given dimension
   void SetRange(double xmin, double xmax, int icoord = 0);

   /// set range for all dimensions
   void SetRange(const double * xmin, const double * xmax);

   /// set range using DataRange class
   void SetRange(const ROOT::Fit::DataRange & range);

   /// set the mode of the distribution (could be useful to some methods)
   /// implemented by derived classes if needed
   virtual void SetMode(double  ) {}

   /// set the normalization area of distribution
   /// implemented by derived classes if needed
   virtual void SetArea(double) {}

   /// get the parent distribution function (must be called after setting the function)
   const ROOT::Math::IMultiGenFunction & ParentPdf() const {
      return *fFunc;

      sample one event in one dimension
      better implementation could be provided by the derived classes
   virtual double Sample1D() {
      return fData[0];

      sample one event and rerturning array x with coordinates
   const double *  Sample() {
      return &fData.front();

      sample one event in multi-dimension by filling the given array
      return false if sampling failed
   virtual bool Sample(double * x) = 0;

      sample one bin given an estimated of the pdf in the bin
      (this can be function value at the center or its integral in the bin
      divided by the bin width)
      By default do not do random sample, just return the function values
      Typically Poisson statistics will be used
   virtual bool SampleBin(double prob, double & value, double * error = 0) {
      value = prob;
      if (error) *error = 0;
      return true;
      sample a set of bins given a vector of probabilities
      Typically multinomial statistics will be used and the sum of the probabilities
      will be equal to the total number of events to be generated
      For sampling the bins indipendently, SampleBin should be used
   virtual bool SampleBins(unsigned int n, const double * prob, double * values, double * errors  = 0)  {
      std::copy(prob,prob+n, values);
      if (errors) std::fill(errors,errors+n,0);
      return true;

      generate a un-binned data sets (fill the given data set)
      if dataset has already data append to it
   virtual bool Generate(unsigned int nevt, ROOT::Fit::UnBinData & data);

      generate a bin data set .
      A range must have been set before (otherwise inf is returned)
      and the bins are equidinstant in the previously defined range
      bin center values must be present in given data set
      If the sampler is implemented by a random one, the entries
      will be binned according to the Poisson distribution
      It is assumed the distribution is normalized, otherwise the nevt must be scaled
      accordingly. The expected value/bin nexp  = f(x_i) * binArea/ nevt
      Extend control if use a fixed (i.e. multinomial statistics) or floating total number of events
   virtual bool Generate(unsigned int nevt, const  int * nbins, ROOT::Fit::BinData & data, bool extend = true);
      same as before but passing the range in case of 1 dim data
   bool Generate(unsigned int nevt, int nbins, double xmin, double xmax, ROOT::Fit::BinData & data, bool extend = true) {
      int nbs[1]; nbs[0] = nbins;
      return Generate(nevt, nbs, data, extend);


   // internal method to set the function
   virtual void DoSetFunction(const ROOT::Math::IMultiGenFunction & func, bool copy);
   // check if generator have been initialized correctly and one can start generating
   bool IsInitialized() ;
   /// return the data range of the Pdf . Must be called after setting the function
   const ROOT::Fit::DataRange & PdfRange() const {
      return *fRange;


   // private methods

   bool fOwnFunc;                         // flag to indicate if the function is owned
   mutable std::vector<double> fData;     // internal array used to cached the sample data
   ROOT::Fit::DataRange    *   fRange;    // data range
   const ROOT::Math::IMultiGenFunction * fFunc; // internal function (ND)


   } // end namespace Math

} // end namespace ROOT

#endif /* ROOT_Math_DistSampler */