BayesOpt / include / gaussian_process.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 /** \file gaussian_process.hpp \brief Standard zero mean gaussian process with noisy observations */ /* ------------------------------------------------------------------------- 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 _GAUSSIAN_PROCESS_HPP_ #define _GAUSSIAN_PROCESS_HPP_ #include "gauss_distribution.hpp" #include "empiricalbayesprocess.hpp" namespace bayesopt { /** \addtogroup NonParametricProcesses */ /**@{*/ /** * \brief Standard zero mean gaussian process with noisy observations. */ class GaussianProcess: public ConditionalBayesProcess { public: GaussianProcess(size_t dim, bopt_params params, const Dataset& data); virtual ~GaussianProcess(); /** * \brief Function that returns the prediction of the GP for a query point * in the hypercube [0,1]. * * @param query in the hypercube [0,1] to evaluate the Gaussian process * @return pointer to the probability distribution. */ ProbabilityDistribution* prediction(const vectord &query); private: /** * \brief Computes the negative log likelihood of the data for all * the parameters. * @return value negative log likelihood */ double negativeTotalLogLikelihood(); /** * \brief Computes the negative log likelihood of the data. * * \f[ \log p(y|x,\theta,f) \propto y^T (K+\sigma I)^{-1} y + * \log|K+\sigma I| * \f] * * @return value negative log likelihood */ double negativeLogLikelihood(); /** Precompute some values of the prediction that do not depends * on the query */ void precomputePrediction(); private: vectord mAlphaV; ///< Precomputed L\y GaussianDistribution* d_; ///< Pointer to distribution function }; /**@}*/ } //namespace bayesopt #endif