# BayesOpt / include / bayesoptdisc.hpp

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 /** \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 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 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 . ------------------------------------------------------------------------ */ #ifndef _BAYESOPTDISC_HPP_ #define _BAYESOPTDISC_HPP_ #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 { public: /** * 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(); protected: /** * 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); /** 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); protected: 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 /**@}*/ #endif