// @(#)root/roostats:$Id$
// Authors: Kevin Belasco        17/06/2009
// Authors: Kyle Cranmer         17/06/2009
/*************************************************************************
 * Copyright (C) 1995-2008, Rene Brun and Fons Rademakers.               *
 * All rights reserved.                                                  *
 *                                                                       *
 * For the licensing terms see $ROOTSYS/LICENSE.                         *
 * For the list of contributors see $ROOTSYS/README/CREDITS.             *
 *************************************************************************/

#ifndef ROOSTATS_MetropolisHastings
#define ROOSTATS_MetropolisHastings

#ifndef RooStats_RooStatsUtils
#include "RooStats/RooStatsUtils.h"
#endif
#ifndef ROOT_Rtypes
#include "Rtypes.h"
#endif
#ifndef ROOT_TObject
#include "TObject.h"
#endif
#ifndef ROO_ARG_SET
#include "RooArgSet.h"
#endif
#ifndef ROOSTATS_ProposalFunction
#include "RooStats/ProposalFunction.h"
#endif
#ifndef ROOSTATS_MarkovChain
#include "RooStats/MarkovChain.h"
#endif

namespace RooStats {

/**

   \ingroup Roostats

   This class uses the Metropolis-Hastings algorithm to construct a Markov Chain
   of data points using Monte Carlo. In the main algorithm, new points in the
   parameter space are proposed and then visited based on their relative
   likelihoods.  This class can use any implementation of the ProposalFunction,
   including non-symmetric proposal functions, to propose parameter points and
   still maintain detailed balance when constructing the chain.



   The "Likelihood" function that is sampled when deciding what steps to take in
   the chain has been given a very generic implementation.  The user can create
   any RooAbsReal based on the parameters and pass it to a MetropolisHastings
   object with the method SetFunction(RooAbsReal&).  Be sure to tell
   MetropolisHastings whether your RooAbsReal is on a (+/-) regular or log scale,
   so that it knows what logic to use when sampling your RooAbsReal.  For example,
   a common use is to sample from a -log(Likelihood) distribution (NLL), for which
   the appropriate configuration calls are SetType(MetropolisHastings::kLog);
   SetSign(MetropolisHastings::kNegative);
   If you're using a traditional likelihood function:
   SetType(MetropolisHastings::kRegular);  SetSign(MetropolisHastings::kPositive);
   You must set these type and sign flags or MetropolisHastings will not construct
   a MarkovChain.

   Also note that in ConstructChain(), the values of the variables are randomized
   uniformly over their intervals before construction of the MarkovChain begins.
   
*/


   class MetropolisHastings :  public TObject {


   public:
      enum FunctionSign {kNegative, kPositive, kSignUnset};
      enum FunctionType {kRegular, kLog, kTypeUnset};

      // default constructor
      MetropolisHastings();

      // alternate constructor
      MetropolisHastings(RooAbsReal& function, const RooArgSet& paramsOfInterest,
            ProposalFunction& proposalFunction, Int_t numIters);

      virtual ~MetropolisHastings() {}

      // main purpose of MetropolisHastings - run Metropolis-Hastings
      // algorithm to generate Markov Chain of points in the parameter space
      virtual MarkovChain* ConstructChain();

      // specify the parameters to store in the chain
      // if not specified all of them will be stored
      virtual void SetChainParameters(const RooArgSet& set)
      { fChainParams.removeAll();  fChainParams.add(set);  RemoveConstantParameters(&fChainParams); }      
      // specify all the parameters of interest in the interval
      virtual void SetParameters(const RooArgSet& set)
      { fParameters.removeAll();  fParameters.add(set);  RemoveConstantParameters(&fParameters); }
      // set the proposal function for suggesting new points for the MCMC
      virtual void SetProposalFunction(ProposalFunction& proposalFunction)
      { fPropFunc = &proposalFunction; }
      // set the number of iterations to run the metropolis algorithm
      virtual void SetNumIters(Int_t numIters)
      { fNumIters = numIters; }
      // set the number of steps in the chain to discard as burn-in,
      // starting from the first
      virtual void SetNumBurnInSteps(Int_t numBurnInSteps)
      { fNumBurnInSteps = numBurnInSteps; }
      // set the (likelihood) function
      virtual void SetFunction(RooAbsReal& function) { fFunction = &function; }
      // set the sign of the function
      virtual void SetSign(enum FunctionSign sign) { fSign = sign; }
      // set the type of the function
      virtual void SetType(enum FunctionType type) { fType = type; }


   protected:
      RooAbsReal* fFunction; // function that will generate likelihood values
      RooArgSet fParameters; // RooRealVars that define all parameter space
      RooArgSet fChainParams; // RooRealVars that are stored in the chain
      ProposalFunction* fPropFunc; // Proposal function for MCMC integration
      Int_t fNumIters; // number of iterations to run metropolis algorithm
      Int_t fNumBurnInSteps; // number of iterations to discard as burn-in, starting from the first
      enum FunctionSign fSign; // whether the likelihood is negative (like NLL) or positive
      enum FunctionType fType; // whether the likelihood is on a regular, log, (or other) scale

      // whether we should take the step, based on the value of d, fSign, fType
      virtual Bool_t ShouldTakeStep(Double_t d);
      virtual Double_t CalcNLL(Double_t xL);

      ClassDef(MetropolisHastings,2) // Markov Chain Monte Carlo calculator for Bayesian credible intervals
   };
}


#endif