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StudentsTLikelihood.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (W) 2013 Roman Votyakov
4  * Written (W) 2012 Jacob Walker
5  * All rights reserved.
6  *
7  * Redistribution and use in source and binary forms, with or without
8  * modification, are permitted provided that the following conditions are met:
9  *
10  * 1. Redistributions of source code must retain the above copyright notice, this
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12  * 2. Redistributions in binary form must reproduce the above copyright notice,
13  * this list of conditions and the following disclaimer in the documentation
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15  *
16  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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30  *
31  * Code adapted from the GPML Toolbox:
32  * http://www.gaussianprocess.org/gpml/code/matlab/doc/
33  */
34 
35 #ifndef CSTUDENTSTLIKELIHOOD_H_
36 #define CSTUDENTSTLIKELIHOOD_H_
37 
38 #include <shogun/lib/config.h>
39 
40 
42 
43 namespace shogun
44 {
45 
58 {
59 public:
62 
69 
70  virtual ~CStudentsTLikelihood();
71 
76  virtual const char* get_name() const { return "StudentsTLikelihood"; }
77 
82  float64_t get_sigma() const { return CMath::exp(m_log_sigma); }
83 
88  void set_sigma(float64_t sigma)
89  {
90  REQUIRE(sigma>0.0, "Scale parameter (%f) must be greater than zero\n", sigma);
91  m_log_sigma=CMath::log(sigma);
92  }
93 
98  float64_t get_degrees_freedom() const { return CMath::exp(m_log_df)+1; }
99 
105  {
106  REQUIRE(df>1.0, "Number of degrees (%f) of freedom must be greater than one\n", df)
107  m_log_df=CMath::log(df-1);
108  }
109 
116 
132  SGVector<float64_t> s2, const CLabels* lab=NULL) const;
133 
149  SGVector<float64_t> s2, const CLabels* lab=NULL) const;
150 
156 
169  SGVector<float64_t> func) const;
170 
182  const CLabels* lab, SGVector<float64_t> func, index_t i) const;
183 
194  SGVector<float64_t> func, const TParameter* param) const;
195 
207  SGVector<float64_t> func, const TParameter* param) const;
208 
220  SGVector<float64_t> func, const TParameter* param) const;
221 
239  SGVector<float64_t> s2, const CLabels* lab) const;
240 
256  SGVector<float64_t> s2, const CLabels* lab, index_t i) const;
257 
273  SGVector<float64_t> s2, const CLabels* lab, index_t i) const;
274 
279  virtual bool supports_regression() const { return true; }
280 
281 private:
283  void init();
284 
286  float64_t m_log_sigma;
287 
289  float64_t m_log_df;
290 };
291 }
292 #endif /* CSTUDENTSTLIKELIHOOD_H_ */
virtual SGVector< float64_t > get_predictive_variances(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
ELikelihoodModelType
virtual float64_t get_second_moment(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const
int32_t index_t
Definition: common.h:62
virtual const char * get_name() const
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual SGVector< float64_t > get_second_derivative(const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
virtual SGVector< float64_t > get_first_derivative(const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
parameter struct
#define REQUIRE(x,...)
Definition: SGIO.h:206
virtual SGVector< float64_t > get_predictive_means(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
virtual SGVector< float64_t > get_log_probability_f(const CLabels *lab, SGVector< float64_t > func) const
float64_t get_degrees_freedom() const
virtual float64_t get_first_moment(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const
virtual SGVector< float64_t > get_log_probability_derivative_f(const CLabels *lab, SGVector< float64_t > func, index_t i) const
virtual bool supports_regression() const
double float64_t
Definition: common.h:50
void set_sigma(float64_t sigma)
virtual ELikelihoodModelType get_model_type() const
Class that models a Student's-t likelihood.
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
void set_degrees_freedom(float64_t df)
static float64_t exp(float64_t x)
Definition: Math.h:621
static float64_t log(float64_t v)
Definition: Math.h:922
static CStudentsTLikelihood * obtain_from_generic(CLikelihoodModel *likelihood)
virtual SGVector< float64_t > get_third_derivative(const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
The Likelihood model base class.
virtual SGVector< float64_t > get_log_zeroth_moments(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const

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