# BayesOpt / include / student_t_distribution.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``` ```/** \file student_t_distribution.hpp \brief Student's t probability distribution */ /* ------------------------------------------------------------------------- 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 __STUDENT_T_DISTRIBUTION_HPP__ #define __STUDENT_T_DISTRIBUTION_HPP__ #include // for student t distribution #include "prob_distribution.hpp" class StudentTDistribution: public ProbabilityDistribution { public: StudentTDistribution(); virtual ~StudentTDistribution(); /** * \brief Sets the mean and std of the distribution */ void setMeanAndStd(double mean, double std) { mean_ = mean; std_ = std; }; /** * \brief Sets the degrees of freedom (dof) the distribution */ void setDof(size_t dof) { dof_ = dof; boost::math::students_t new_d(dof); d_ = new_d; }; /** * \brief Probability density function * @param x query point * @return probability */ double pdf(double x) { x = (x - mean_) / std_; return boost::math::pdf(d_,x); }; /** * \brief Expected Improvement algorithm for minimization * @param min minimum value found * @param g exponent (used for annealing) * * @return negative value of the expected improvement */ double negativeExpectedImprovement(double min, size_t g); /** * \brief Lower confindence bound. Can be seen as the inverse of the Upper * confidence bound * @param beta std coefficient (used for annealing) * @return value of the lower confidence bound */ double lowerConfidenceBound(double beta); /** * Probability of improvement algorithm for minimization * @param min minimum value found * @param epsilon minimum improvement margin * * @return negative value of the probability of improvement */ double negativeProbabilityOfImprovement(double min, double epsilon); /** * Sample outcome acording to the marginal distribution at the query point. * @param eng boost.random engine * * @return outcome */ double sample_query(randEngine& eng); double getMean() { return mean_; }; double getStd() { return std_; }; private: boost::math::students_t d_; double mean_; double std_; size_t dof_; }; #endif ```