SHOGUN
v3.2.0
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00001 /* 00002 * This program is free software; you can redistribute it and/or modify 00003 * it under the terms of the GNU General Public License as published by 00004 * the Free Software Foundation; either version 3 of the License, or 00005 * (at your option) any later version. 00006 * 00007 * Written (W) 2013 Roman Votyakov 00008 * Copyright (C) 2012 Jacob Walker 00009 * Copyright (C) 2013 Roman Votyakov 00010 * 00011 * Code adapted from the GPML Toolbox: 00012 * http://www.gaussianprocess.org/gpml/code/matlab/doc/ 00013 */ 00014 00015 #ifndef CSTUDENTSTLIKELIHOOD_H_ 00016 #define CSTUDENTSTLIKELIHOOD_H_ 00017 00018 #include <shogun/lib/config.h> 00019 00020 #ifdef HAVE_EIGEN3 00021 00022 #include <shogun/machine/gp/LikelihoodModel.h> 00023 00024 namespace shogun 00025 { 00026 00038 class CStudentsTLikelihood: public CLikelihoodModel 00039 { 00040 public: 00042 CStudentsTLikelihood(); 00043 00049 CStudentsTLikelihood(float64_t sigma, float64_t df); 00050 00051 virtual ~CStudentsTLikelihood(); 00052 00057 virtual const char* get_name() const { return "StudentsTLikelihood"; } 00058 00063 float64_t get_sigma() { return m_sigma; } 00064 00069 void set_sigma(float64_t sigma) 00070 { 00071 REQUIRE(sigma>0.0, "Scale parameter must be greater than zero\n") 00072 m_sigma=sigma; 00073 } 00074 00079 float64_t get_degrees_freedom() { return m_df; } 00080 00085 void set_degrees_freedom(float64_t df) 00086 { 00087 REQUIRE(df>1.0, "Number of degrees of freedom must be greater than one\n") 00088 m_df=df; 00089 } 00090 00096 static CStudentsTLikelihood* obtain_from_generic(CLikelihoodModel* likelihood); 00097 00112 virtual SGVector<float64_t> get_predictive_means(SGVector<float64_t> mu, 00113 SGVector<float64_t> s2, const CLabels* lab=NULL) const; 00114 00129 virtual SGVector<float64_t> get_predictive_variances(SGVector<float64_t> mu, 00130 SGVector<float64_t> s2, const CLabels* lab=NULL) const; 00131 00136 virtual ELikelihoodModelType get_model_type() const { return LT_STUDENTST; } 00137 00149 virtual SGVector<float64_t> get_log_probability_f(const CLabels* lab, 00150 SGVector<float64_t> func) const; 00151 00162 virtual SGVector<float64_t> get_log_probability_derivative_f( 00163 const CLabels* lab, SGVector<float64_t> func, index_t i) const; 00164 00174 virtual SGVector<float64_t> get_first_derivative(const CLabels* lab, 00175 SGVector<float64_t> func, const TParameter* param) const; 00176 00187 virtual SGVector<float64_t> get_second_derivative(const CLabels* lab, 00188 SGVector<float64_t> func, const TParameter* param) const; 00189 00200 virtual SGVector<float64_t> get_third_derivative(const CLabels* lab, 00201 SGVector<float64_t> func, const TParameter* param) const; 00202 00219 virtual SGVector<float64_t> get_log_zeroth_moments(SGVector<float64_t> mu, 00220 SGVector<float64_t> s2, const CLabels* lab) const; 00221 00236 virtual float64_t get_first_moment(SGVector<float64_t> mu, 00237 SGVector<float64_t> s2, const CLabels* lab, index_t i) const; 00238 00253 virtual float64_t get_second_moment(SGVector<float64_t> mu, 00254 SGVector<float64_t> s2, const CLabels* lab, index_t i) const; 00255 00260 virtual bool supports_regression() const { return true; } 00261 00262 private: 00264 void init(); 00265 00267 float64_t m_sigma; 00268 00270 float64_t m_df; 00271 }; 00272 } 00273 #endif /* HAVE_EIGEN3 */ 00274 #endif /* CSTUDENTSTLIKELIHOOD_H_ */