BayesOpt / include / bayesoptdisc.hpp

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/**  \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 <>
   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 <>.


#include "bayesoptbase.hpp"

/** \addtogroup BayesOpt */

namespace 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
     * @param iteration 
     * @param xNext 
     * @param yNext 
    void plotStepData(size_t iteration, const vectord& xNext,
		     double yNext);

     * Sample a set of points to initialize GP fit.
     * Use pure random sampling or uniform Latin Hypercube sampling
     * as appeared in Jones 
     * @return error code
    int sampleInitialPoints();

     * \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
    inline double evaluateSampleInternal( const vectord &query )
    { return evaluateSample(query); }; 

    void findOptimal(vectord &xOpt);

    vecOfvec mInputSet;               ///< List of input points


} //namespace bayesopt