bayesopt.hpp

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/*
-------------------------------------------------------------------------
   This file is part of BayesOpt, an efficient C++ library for 
   Bayesian optimization.

   Copyright (C) 2011-2015 Ruben Martinez-Cantin <rmcantin@unizar.es>
 
   BayesOpt is free software: you can redistribute it and/or modify it 
   under the terms of the GNU Affero 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
   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   GNU Affero General Public License for more details.

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

#ifndef  _BAYESOPTAPI_HPP_
#define  _BAYESOPTAPI_HPP_

#include "bayesoptbase.hpp"

namespace bayesopt  {

  // Forward declarations
  namespace utils 
  {  
    template <class V> 
    class BoundingBox;
  }
  class NLOPT_Optimization;
  class CritCallback;


  
  class BAYESOPT_API ContinuousModel: public BayesOptBase
  {
  public:
    ContinuousModel(size_t dim, Parameters params);

    virtual ~ContinuousModel();

    void setBoundingBox(const vectord &lowerBound,
            const vectord &upperBound);


  protected:
    vectord samplePoint();

    void findOptimal(vectord &xOpt);

    vectord remapPoint(const vectord& x);

    void generateInitialPoints(matrixd& xPoints);

  private:
    boost::scoped_ptr<utils::BoundingBox<vectord> > mBB;      
    boost::scoped_ptr<NLOPT_Optimization> cOptimizer;
    boost::scoped_ptr<CritCallback> mCallback;

  private:
    ContinuousModel();                       
  };
  

  class BAYESOPT_API DiscreteModel : public BayesOptBase
  {
  public:
    DiscreteModel(const vecOfvec &validSet, Parameters params);

    DiscreteModel(const vectori &categories, Parameters params);
    
    virtual ~DiscreteModel();
    
  protected:
    vectord samplePoint();

    void findOptimal(vectord &xOpt);

    vectord remapPoint(const vectord& x);

    void generateInitialPoints(matrixd& xPoints);

  private:
    vecOfvec mInputSet;               

    DiscreteModel();         
  };


}  //namespace bayesopt


#endif