GSLSimAnMinimizer.cxx

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// @(#)root/mathmore:$Id$
// Author: L. Moneta Wed Dec 20 17:16:32 2006
 
/**********************************************************************
 *                                                                    *
 * Copyright (c) 2006  LCG ROOT Math Team, CERN/PH-SFT                *
 *                                                                    *
 *                                                                    *
 **********************************************************************/
 
// Implementation file for class GSLSimAnMinimizer
 
#include "Math/GSLSimAnMinimizer.h"
#include "Math/WrappedParamFunction.h"
#include "Math/Error.h"
 
#include "Math/MinimTransformFunction.h"
#include "Math/MultiNumGradFunction.h"   // needed to use transformation function
#include "Math/FitMethodFunction.h"
 
#include <iostream>
#include <cassert>
 
namespace ROOT {
 
   namespace Math {
 
 
 
// GSLSimAnMinimizer implementation
 
GSLSimAnMinimizer::GSLSimAnMinimizer( int /* ROOT::Math::EGSLSimAnMinimizerType type */ ) :
   BasicMinimizer()
{
   // Constructor implementation : create GSLMultiFit wrapper object
 
   SetMaxIterations(100);
   SetPrintLevel(0);
}
 
GSLSimAnMinimizer::~GSLSimAnMinimizer () {
}
 
 
bool GSLSimAnMinimizer::Minimize() {
   // set initial parameters of the minimizer
   int debugLevel = PrintLevel();
 
   if (debugLevel >=1 ) std::cout <<"Minimize using GSLSimAnMinimizer " << std::endl;
 
   const ROOT::Math::IMultiGenFunction * function = ObjFunction();
   if (function == 0) {
      MATH_ERROR_MSG("GSLSimAnMinimizer::Minimize","Function has not been set");
      return false;
   }
 
   // vector of internal values (copied by default)
   unsigned int npar = NPar();
   std::vector<double> xvar;
   std::vector<double> steps(StepSizes(),StepSizes()+npar);
 
   // needed for the transformation
   MultiNumGradFunction * gradFunc = new MultiNumGradFunction( *function );
   gradFunc->SetOwnership();
 
   MinimTransformFunction * trFunc  = CreateTransformation(xvar, gradFunc );
   // ObjFunction() will return now the new transformed function
 
   if (trFunc) {
      // transform also the step sizes
      trFunc->InvStepTransformation(X(), StepSizes(), &steps[0]);
      steps.resize( trFunc->NDim() );
   }
 
   assert (xvar.size() == steps.size() );
 
 
#ifdef DEBUG
   for (unsigned int i = 0; i < npar ; ++i) {
      std::cout << "x  = " << xvar[i] << " steps " << steps[i] << "  x " << X()[i] << std::endl;
   }
   std::cout << "f(x) = " <<  (*ObjFunction())(&xvar.front() ) << std::endl;
   std::cout << "f(x) not transf = " <<  (*function)( X() ) << std::endl;
   if (trFunc) std::cout << "ftrans(x) = " <<  (*trFunc) (&xvar.front() ) << std::endl;
#endif
 
   // output vector
   std::vector<double> xmin(xvar.size() );
 
 
   int iret = fSolver.Solve(*ObjFunction(), &xvar.front(), &steps.front(), &xmin[0], (debugLevel > 1) );
 
   SetMinValue( (*ObjFunction())(&xmin.front() ) );
 
   SetFinalValues(&xmin.front());
 
 
   if (debugLevel >=1 ) {
      if (iret == 0)
         std::cout << "GSLSimAnMinimizer: Minimum Found" << std::endl;
      else
         std::cout << "GSLSimAnMinimizer: Error in solving" << std::endl;
 
      int pr = std::cout.precision(18);
      std::cout << "FVAL         = " << MinValue() << std::endl;
      std::cout.precision(pr);
      for (unsigned int i = 0; i < NDim(); ++i)
         std::cout << VariableName(i) << "\t  = " << X()[i] << std::endl;
   }
 
 
   return ( iret == 0) ? true : false;
}
 
 
unsigned int GSLSimAnMinimizer::NCalls() const {
   // return number of function calls
   const ROOT::Math::MinimTransformFunction * tfunc = dynamic_cast<const ROOT::Math::MinimTransformFunction *>(ObjFunction());
   const ROOT::Math::MultiNumGradFunction * f = 0;
   if (tfunc) f = dynamic_cast<const ROOT::Math::MultiNumGradFunction *>(tfunc->OriginalFunction());
   else
      f = dynamic_cast<const ROOT::Math::MultiNumGradFunction *>(ObjFunction());
   if (f) return f->NCalls();
   return 0;
}
 
 
   } // end namespace Math
 
} // end namespace ROOT