nonparametricprocess.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 __BAYESIANREGRESSOR_HPP__
#define __BAYESIANREGRESSOR_HPP__

#include "dataset.hpp"
#include "prob_distribution.hpp"
#include "mean_functors.hpp"
#include "optimizable.hpp"

namespace bayesopt
{

  class NonParametricProcess: public RBOptimizable
  {
  public:
    NonParametricProcess(size_t dim, Parameters parameters, 
             const Dataset& data, 
             MeanModel& mean,
             randEngine& eng);

    virtual ~NonParametricProcess();

    static NonParametricProcess* create(size_t dim, Parameters parameters,
                    const Dataset& data,             
                    MeanModel& mean, randEngine& eng);

    virtual ProbabilityDistribution* prediction(const vectord &query) = 0;
                 
    virtual void fitSurrogateModel() = 0;

    virtual void updateSurrogateModel() = 0;


    // Getters and setters
    double getValueAtMinimum();
    const Dataset* getData();
    double getSignalVariance();
    
    virtual size_t nHyperParameters() = 0;
    virtual vectord getHyperParameters() = 0;
    virtual void setHyperParameters(const vectord& theta) = 0;


  protected:
    const Dataset& mData;  
    double mSigma;                                   
    size_t dim_;
    MeanModel& mMean;
  };


  inline double NonParametricProcess::getValueAtMinimum() 
  { return mData.getValueAtMinimum(); };


  inline const Dataset* NonParametricProcess::getData()
  {return &mData;}

  inline double NonParametricProcess::getSignalVariance() 
  { return mSigma; };

} //namespace bayesopt

#endif