SHOGUN  4.0.0
LaplacianInferenceBase.cpp
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
4  * Written (W) 2013 Roman Votyakov
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32 
34 
35 #ifdef HAVE_EIGEN3
36 
38 #include <shogun/lib/external/brent.h>
40 
41 using namespace shogun;
42 using namespace Eigen;
43 
44 namespace shogun
45 {
46 
48 {
49  init();
50 }
51 
53  CFeatures* feat, CMeanFunction* m, CLabels* lab, CLikelihoodModel* mod)
54  : CInferenceMethod(kern, feat, m, lab, mod)
55 {
56  init();
57 }
58 
59 void CLaplacianInferenceBase::init()
60 {
61  m_iter=20;
62  m_tolerance=1e-6;
63  m_opt_tolerance=1e-10;
64  m_opt_max=10;
65 
66  SG_ADD(&m_dlp, "dlp", "derivative of log likelihood with respect to function location", MS_NOT_AVAILABLE);
67  SG_ADD(&m_mu, "mu", "mean vector of the approximation to the posterior", MS_NOT_AVAILABLE);
68  SG_ADD(&m_Sigma, "Sigma", "covariance matrix of the approximation to the posterior", MS_NOT_AVAILABLE);
69  SG_ADD(&m_W, "W", "the noise matrix", MS_NOT_AVAILABLE);
70  SG_ADD(&m_tolerance, "tolerance", "amount of tolerance for Newton's iterations", MS_NOT_AVAILABLE);
71  SG_ADD(&m_iter, "iter", "max Newton's iterations", MS_NOT_AVAILABLE);
72  SG_ADD(&m_opt_tolerance, "opt_tolerance", "amount of tolerance for Brent's minimization method", MS_NOT_AVAILABLE);
73  SG_ADD(&m_opt_max, "opt_max", "max iterations for Brent's minimization method", MS_NOT_AVAILABLE);
74 }
75 
77 {
78 }
79 
81 {
82  SG_DEBUG("entering\n");
83 
85  update_alpha();
86  update_chol();
88  update_deriv();
90 
91  SG_DEBUG("leaving\n");
92 }
93 
94 SGVector<float64_t> CLaplacianInferenceBase::get_alpha()
95 {
97  update();
98 
100  return result;
101 
102 }
103 
105 {
107  update();
108 
109  return SGMatrix<float64_t>(m_L);
110 
111 }
112 
114 {
115 
117  update();
118 
119  return SGVector<float64_t>(m_mu);
120 }
121 
123 {
125  update();
126 
128 }
129 
130 }
131 
132 #endif /* HAVE_EIGEN3 */
virtual void update_parameter_hash()
Definition: SGObject.cpp:250
virtual void update_alpha()=0
SGVector< float64_t > m_alpha
virtual void update_approx_cov()=0
The Inference Method base class.
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
Definition: SGMatrix.h:20
An abstract class of the mean function.
Definition: MeanFunction.h:28
SGMatrix< float64_t > m_L
virtual SGMatrix< float64_t > get_cholesky()
#define SG_DEBUG(...)
Definition: SGIO.h:107
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual SGVector< float64_t > get_posterior_mean()
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual void update_chol()=0
virtual SGMatrix< float64_t > get_posterior_covariance()
The Kernel base class.
Definition: Kernel.h:153
#define SG_ADD(...)
Definition: SGObject.h:81
virtual void update_deriv()=0
virtual bool parameter_hash_changed()
Definition: SGObject.cpp:263
The Likelihood model base class.

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