posteriormodel.cpp

/*
-------------------------------------------------------------------------
   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/>.
------------------------------------------------------------------------
*/

#include "log.hpp"
#include "posteriormodel.hpp"
#include "posterior_fixed.hpp"
#include "posterior_empirical.hpp"
#include "posterior_mcmc.hpp"

namespace bayesopt
{

  PosteriorModel* PosteriorModel::create(size_t dim, Parameters params, randEngine& eng)
  {
    switch (params.l_type)
      {
      case L_FIXED: return new PosteriorFixed(dim,params,eng);
      case L_EMPIRICAL: return new EmpiricalBayes(dim,params,eng);
      case L_DISCRETE: // TODO:return new
      case L_MCMC: return new MCMCModel(dim,params,eng);
      case L_ERROR:
      default:
    throw std::invalid_argument("Learning type not supported");
      }
  };

  PosteriorModel::PosteriorModel(size_t dim, Parameters parameters, 
                 randEngine& eng):
    mParameters(parameters), mDims(dim), mMean(dim, parameters)
  {} 

  PosteriorModel::~PosteriorModel()
  { } // Default destructor


  void PosteriorModel::setSamples(const matrixd &x, const vectord &y)
  { 
    mData.setSamples(x,y);  
    mMean.setPoints(mData.mX);  //Because it expects a vecOfvec instead of a matrixd 
  }

  void PosteriorModel::setSamples(const matrixd &x)
  { 
    mData.setSamples(x);  
    mMean.setPoints(mData.mX);  //Because it expects a vecOfvec instead of a matrixd 
  }

  void PosteriorModel::setSamples(const vectord &y)
  { 
    mData.setSamples(y);  
  }


  void PosteriorModel::setSample(const vectord &x, double y)
  { 
    matrixd xx(1,x.size());  vectord yy(1);
    row(xx,0) = x;           yy(0) = y;
    mData.setSamples(xx,yy);   
    mMean.setPoints(mData.mX);  //Because it expects a vecOfvec instead of a matrixd
  }

  void PosteriorModel::addSample(const vectord &x, double y)
  {  mData.addSample(x,y); mMean.addNewPoint(x);  };


} //namespace bayesopt