1. Ruben Martinez-Cantin
  2. BayesOpt


BayesOpt / devel / bayesoptdisc.hpp

/**  \file bayesoptdisc.hpp \brief Discrete Bayesian optimization */
   This file is part of BayesOpt, an efficient C++ library for 
   Bayesian optimization.

   Copyright (C) 2011-2013 Ruben Martinez-Cantin <rmcantin@unizar.es>
   BayesOpt is free software: you can redistribute it and/or modify it 
   under the terms of the GNU General Public License as published by
   the Free Software Foundation, either version 3 of the License, or
   (at your option) any later version.

   BayesOpt is distributed in the hope that it will be useful, but 
   WITHOUT ANY WARRANTY; without even the implied warranty of
   GNU General Public License for more details.

   You should have received a copy of the GNU General Public License
   along with BayesOpt.  If not, see <http://www.gnu.org/licenses/>.


#include "bayesoptbase.hpp"

namespace bayesopt
  /** \addtogroup BayesOpt */

   * \brief Sequential Kriging Optimization using different non-parametric 
   * processes as surrogate (kriging) functions. 
  class BAYESOPT_API DiscreteModel : public BayesOptBase

     * Constructor
     * @param validSet  Set of potential inputs
    DiscreteModel(const vecOfvec &validSet );

     * Constructor
     * @param validSet  Set of potential inputs
     * @param params set of parameters (see parameters.h)
    DiscreteModel(const vecOfvec &validSet, bopt_params params);
    /** Default destructor  */
    virtual ~DiscreteModel();

    /** Initialize the optimization process. */
    void initializeOptimization();

    /** Once the optimization has been perfomed, return the optimal point. */
    vectord getFinalResult();

    /** Print data for every step according to the verbose level */
    void plotStepData(size_t iteration, const vectord& xNext,
		     double yNext);

    /** Selects the initial set of points to build the surrogate model. */
    void sampleInitialPoints();

    /** Sample a single point in the input space. Used for epsilon greedy exploration. */
    vectord samplePoint();

     * \brief Wrapper for the target function normalize in the hypercube
     * [0,1]
     * @param query point to evaluate in [0,1] hypercube
     * @return actual return value of the target function
    double evaluateSampleInternal( const vectord &query ); 

    void findOptimal(vectord &xOpt);

    vecOfvec mInputSet;               ///< List of input points

  inline vectord DiscreteModel::samplePoint()
    randInt sample(mEngine, intUniformDist(0,mInputSet.size()-1));
    return mInputSet[sample()];

  inline double DiscreteModel::evaluateSampleInternal( const vectord &query )
  { return evaluateSample(query); }; 

} //namespace bayesopt