// @(#)root/minuit2:$Id$
// Authors: M. Winkler, F. James, L. Moneta, A. Zsenei   2003-2005

/**********************************************************************
 *                                                                    *
 * Copyright (c) 2005 LCG ROOT Math team,  CERN/PH-SFT                *
 *                                                                    *
 **********************************************************************/

#include "Minuit2/ModularFunctionMinimizer.h"
#include "Minuit2/MinimumSeedGenerator.h"
#include "Minuit2/AnalyticalGradientCalculator.h"
#include "Minuit2/Numerical2PGradientCalculator.h"
#include "Minuit2/MinimumBuilder.h"
#include "Minuit2/MinimumSeed.h"
#include "Minuit2/FunctionMinimum.h"
#include "Minuit2/MnUserParameterState.h"
#include "Minuit2/MnUserParameters.h"
#include "Minuit2/MnUserCovariance.h"
#include "Minuit2/MnUserTransformation.h"
#include "Minuit2/MnUserFcn.h"
#include "Minuit2/FCNBase.h"
#include "Minuit2/FCNGradientBase.h"
#include "Minuit2/MnStrategy.h"
#include "Minuit2/MnHesse.h"
#include "Minuit2/MnLineSearch.h"
#include "Minuit2/MnParabolaPoint.h"

#if defined(DEBUG) || defined(WARNINGMSG)
#include "Minuit2/MnPrint.h"
#endif


namespace ROOT {

   namespace Minuit2 {


// #include "Minuit2/MnUserParametersPrint.h"

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNBase& fcn, const std::vector<double>& par, const std::vector<double>& err, unsigned int stra, unsigned int maxfcn, double toler) const {
   // minimize from FCNBase and std::vector of double's for parameter values and errors (step sizes)
   MnUserParameterState st(par, err);
   MnStrategy strategy(stra);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNGradientBase& fcn, const std::vector<double>& par, const std::vector<double>& err, unsigned int stra, unsigned int maxfcn, double toler) const {
   // minimize from FCNGradientBase (use analytical gradient provided in FCN)
   // and std::vector of double's for parameter values and errors (step sizes)
   MnUserParameterState st(par, err);
   MnStrategy strategy(stra);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

// move nrow before cov to avoid ambiguities when using default parameters
FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNBase& fcn, const std::vector<double>& par, unsigned int nrow, const std::vector<double>& cov, unsigned int stra, unsigned int maxfcn, double toler) const {
   // minimize from FCNBase using std::vector for parameter error and
   // an std::vector of size n*(n+1)/2 for the covariance matrix  and n (rank of cov matrix)

   MnUserParameterState st(par, cov, nrow);
   MnStrategy strategy(stra);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNGradientBase& fcn, const std::vector<double>& par, unsigned int nrow, const std::vector<double>& cov, unsigned int stra, unsigned int maxfcn, double toler) const {
   // minimize from FCNGradientBase (use analytical gradient provided in FCN)
   // using std::vector for parameter error and
   // an std::vector of size n*(n+1)/2 for the covariance matrix  and n (rank of cov matrix)

   MnUserParameterState st(par, cov, nrow);
   MnStrategy strategy(stra);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNBase& fcn, const MnUserParameters& upar, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // minimize from FCNBase and MnUserParameters object

   MnUserParameterState st(upar);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNGradientBase& fcn, const MnUserParameters& upar, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // minimize from FCNGradientBase (use analytical gradient provided in FCN)  and MnUserParameters object

   MnUserParameterState st(upar);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNBase& fcn, const MnUserParameters& upar, const MnUserCovariance& cov, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // minimize from FCNBase and MnUserParameters and MnUserCovariance objects

   MnUserParameterState st(upar, cov);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}

FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNGradientBase& fcn, const MnUserParameters& upar, const MnUserCovariance& cov, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // minimize from FCNGradientBase (use analytical gradient provided in FCN)  and
   // MnUserParameters MnUserCovariance objects

   MnUserParameterState st(upar, cov);
   return Minimize(fcn, st, strategy, maxfcn, toler);
}



FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNBase& fcn, const MnUserParameterState& st, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // minimize from a FCNBase and a MnUserparameterState - interface used by all the previous ones
   // based on FCNBase. Create in this case a NumericalGradient calculator
   // Create the minuit FCN wrapper (MnUserFcn) containing the trasformation (int<->ext)

   // neeed MnUsserFcn for difference int-ext parameters
   MnUserFcn mfcn(fcn, st.Trafo() );
   Numerical2PGradientCalculator gc(mfcn, st.Trafo(), strategy);

   unsigned int npar = st.VariableParameters();
   if(maxfcn == 0) maxfcn = 200 + 100*npar + 5*npar*npar;
   MinimumSeed mnseeds = SeedGenerator()(mfcn, gc, st, strategy);

   return Minimize(mfcn, gc, mnseeds, strategy, maxfcn, toler);
}


// use Gradient here
FunctionMinimum ModularFunctionMinimizer::Minimize(const FCNGradientBase& fcn, const MnUserParameterState& st, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // minimize from a FCNGradientBase and a MnUserparameterState - interface used by all the previous ones
   // based on FCNGradientBase.
   // Create in this acase an AnalyticalGradient calculator
   // Create the minuit FCN wrapper (MnUserFcn) containing the trasformation (int<->ext)

   MnUserFcn mfcn(fcn, st.Trafo());
   AnalyticalGradientCalculator gc(fcn, st.Trafo());

   unsigned int npar = st.VariableParameters();
   if(maxfcn == 0) maxfcn = 200 + 100*npar + 5*npar*npar;

   MinimumSeed mnseeds = SeedGenerator()(mfcn, gc, st, strategy);

   return Minimize(mfcn, gc, mnseeds, strategy, maxfcn, toler);
}


FunctionMinimum ModularFunctionMinimizer::Minimize(const MnFcn& mfcn, const GradientCalculator& gc, const MinimumSeed& seed, const MnStrategy& strategy, unsigned int maxfcn, double toler) const {
   // Interface used by all the others for the minimization using the base MinimumBuilder class
   // According to the contained type of MinimumBuilder the right type will be used

   const MinimumBuilder & mb = Builder();
   //std::cout << typeid(&mb).Name() << std::endl;
   double effective_toler = toler * mfcn.Up();   // scale tolerance with Up()
   // avoid tolerance too smalls (than limits)
   double eps = MnMachinePrecision().Eps2();
   if (effective_toler < eps) effective_toler = eps;

   // check if maxfcn is already exhausted
   // case already reached call limit
   if(mfcn.NumOfCalls() >= maxfcn) {
#ifdef WARNINGMSG
      MN_INFO_MSG("ModularFunctionMinimizer: Stop before iterating - call limit already exceeded");
#endif
      return FunctionMinimum(seed, std::vector<MinimumState>(1, seed.State()), mfcn.Up(), FunctionMinimum::MnReachedCallLimit());
   }




   return mb.Minimum(mfcn, gc, seed, strategy, maxfcn, effective_toler);
}




   }  // namespace Minuit2

}  // namespace ROOT
