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VariationalGaussianLikelihood.cpp
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
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31 
32 #include <shogun/lib/config.h>
34 
35 namespace shogun
36 {
37 
40 {
41  init();
42 }
43 
44 void CVariationalGaussianLikelihood::init()
45 {
46  SG_ADD(&m_mu, "mu",
47  "The mean of variational normal distribution\n",
49 
50  SG_ADD(&m_s2, "sigma2",
51  "The variance of variational normal distribution\n",
53 
54  SG_ADD(&m_noise_factor, "noise_factor",
55  "Correct the variance if variance is close to zero or negative\n",
57  m_noise_factor=1e-6;
58 }
59 
61 {
62  REQUIRE(noise_factor>=0, "The noise_factor (%f) should be non negative\n", noise_factor);
63  m_noise_factor=noise_factor;
64 }
65 
67  SGVector<float64_t> s2, const CLabels* lab)
68 {
69  REQUIRE(lab, "Labels are required (lab should not be NULL)\n");
70  REQUIRE((mu.vlen==s2.vlen) && (mu.vlen==lab->get_num_labels()),
71  "Length of the vector of means (%d), length of the vector of "
72  "variances (%d) and number of labels (%d) should be the same\n",
73  mu.vlen, s2.vlen, lab->get_num_labels());
74 
75  for(index_t i = 0; i < s2.vlen; ++i)
76  {
77  if (!((s2[i]+m_noise_factor)>0.0))
78  return false;
79  if (!(s2[i]>0.0))
80  s2[i]+=m_noise_factor;
81  }
82 
83  m_mu=mu;
84  m_s2=s2;
85  return true;
86 }
87 
88 }
virtual bool set_variational_distribution(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab)
int32_t index_t
Definition: common.h:62
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual int32_t get_num_labels() const =0
#define REQUIRE(x,...)
Definition: SGIO.h:206
virtual void set_noise_factor(float64_t noise_factor)
index_t vlen
Definition: SGVector.h:494
double float64_t
Definition: common.h:50
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
The Variational Likelihood base class.
#define SG_ADD(...)
Definition: SGObject.h:81

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