SHOGUN  6.1.3
StudentsTVGLikelihood.cpp
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3  * Written (w) 2014 Wu Lin
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29  *
30  * Code adapted from
31  * http://hannes.nickisch.org/code/approxXX.tar.gz
32  * and the reference paper is
33  * Nickisch, Hannes, and Carl Edward Rasmussen.
34  * "Approximations for Binary Gaussian Process Classification."
35  * Journal of Machine Learning Research 9.10 (2008).
36  *
37  * This code specifically adapted from function in approxKL.m
38  */
39 
41 
43 
44 using namespace Eigen;
45 
46 namespace shogun
47 {
48 
49 CStudentsTVGLikelihood::CStudentsTVGLikelihood()
51 {
52  m_log_sigma = 0.0;
53  m_log_df = CMath::log(2.0);
54  init();
55 }
56 
59 {
60  REQUIRE(sigma>0.0, "Scale parameter (%f) must be greater than zero\n", sigma);
61  REQUIRE(df>1.0, "Number of degrees of freedom (%f) must be greater than one\n", df);
62 
63  m_log_sigma=CMath::log(sigma);
64  m_log_df=CMath::log(df-1.0);
65  init();
66 }
67 
69 {
70 }
71 
73 {
74  set_likelihood(new CStudentsTLikelihood(CMath::exp(m_log_sigma), CMath::exp(m_log_df)+1.0));
75 }
76 
77 void CStudentsTVGLikelihood::init()
78 {
80  SG_ADD(&m_log_df, "log_df", "Degrees of freedom in log domain", MS_AVAILABLE, GRADIENT_AVAILABLE);
81  SG_ADD(&m_log_sigma, "log_sigma", "Scale parameter in log domain", MS_AVAILABLE, GRADIENT_AVAILABLE);
82 }
83 
84 } /* namespace shogun */
85 
Definition: SGMatrix.h:25
#define REQUIRE(x,...)
Definition: SGIO.h:181
virtual void set_likelihood(CLikelihoodModel *lik)
double float64_t
Definition: common.h:60
Class that models a Student's-t likelihood.
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
static float64_t exp(float64_t x)
Definition: Math.h:551
static float64_t log(float64_t v)
Definition: Math.h:714
#define SG_ADD(...)
Definition: SGObject.h:93
Class that models likelihood and uses numerical integration to approximate the following variational ...

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