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NumericalVGLikelihood.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
4  * All rights reserved.
5  *
6  * Redistribution and use in source and binary forms, with or without
7  * modification, are permitted provided that the following conditions are met:
8  *
9  * 1. Redistributions of source code must retain the above copyright notice, this
10  * list of conditions and the following disclaimer.
11  * 2. Redistributions in binary form must reproduce the above copyright notice,
12  * this list of conditions and the following disclaimer in the documentation
13  * and/or other materials provided with the distribution.
14  *
15  * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
16  * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
17  * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
18  * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
19  * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
20  * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
21  * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
22  * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
23  * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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25  *
26  * The views and conclusions contained in the software and documentation are those
27  * of the authors and should not be interpreted as representing official policies,
28  * either expressed or implied, of the Shogun Development Team.
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 
38 #ifndef _NUMERICALVGLIKELIHOOD_H_
39 #define _NUMERICALVGLIKELIHOOD_H_
40 
41 #include <shogun/lib/config.h>
42 
43 #ifdef HAVE_EIGEN3
48 
49 
50 namespace shogun
51 {
52 template<class C> class SGMatrix;
53 
62 {
63 public:
65 
66  virtual ~CNumericalVGLikelihood();
67 
72  virtual const char* get_name() const { return "NumericalVGLikelihood"; }
73 
83  SGVector<float64_t> s2, const CLabels* lab);
84 
96 
109 
119 
126  virtual void set_GHQ_number(index_t n);
127 
128 protected:
129 
133  virtual void init_likelihood()=0;
134 
135 private:
136 
138  index_t m_GHQ_N;
139 
141  bool m_is_init_GHQ;
142 
144  void init();
145 
150  void precompute();
151 
153  SGVector<float64_t> m_xgh;
154 
156  SGVector<float64_t> m_wgh;
157 
159  SGMatrix<float64_t> m_log_lam;
160 };
161 }
162 #endif /* HAVE_EIGEN3 */
163 #endif /* _NUMERICALVGLIKELIHOOD_H_ */
virtual const char * get_name() const
int32_t index_t
Definition: common.h:62
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
The variational Gaussian Likelihood base class. The variational distribution is Gaussian.
parameter struct
virtual bool set_variational_distribution(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab)
virtual void set_GHQ_number(index_t n)
virtual SGVector< float64_t > get_variational_expection()
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual void init_likelihood()=0
virtual SGVector< float64_t > get_first_derivative_wrt_hyperparameter(const TParameter *param) const
virtual SGVector< float64_t > get_variational_first_derivative(const TParameter *param) const
Class that models likelihood and uses numerical integration to approximate the following variational ...

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