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KLCovarianceInferenceMethod.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 Gaussian Process Machine Learning Toolbox
33  * http://www.gaussianprocess.org/gpml/code/matlab/doc/
34  * and the reference paper is
35  * Nickisch, Hannes, and Carl Edward Rasmussen.
36  * "Approximations for Binary Gaussian Process Classification."
37  * Journal of Machine Learning Research 9.10 (2008).
38  *
39  */
40 
41 #ifndef _KLCOVARIANCEINFERENCEMETHOD_H_
42 #define _KLCOVARIANCEINFERENCEMETHOD_H_
43 
44 #include <shogun/lib/config.h>
45 
46 #ifdef HAVE_EIGEN3
49 
50 namespace shogun
51 {
52 
75 {
76 public:
79 
89  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);
90 
92 
97  virtual const char* get_name() const { return "KLCovarianceInferenceMethod"; }
98 
109  virtual SGVector<float64_t> get_alpha();
110 
123 
124 protected:
126  virtual void update_approx_cov();
127 
129  virtual void update_alpha();
130 
132  virtual void update_chol();
133 
137  virtual void update_deriv();
138 
145 
152 
160  virtual void lbfgs_precompute();
161 
172  virtual float64_t get_derivative_related_cov(Eigen::MatrixXd eigen_dK);
173 private:
174  void init();
175 
177  SGVector<float64_t> m_sW;
178 
181 
187 
192 
194  SGVector<float64_t> m_dv;
195 
197  SGVector<float64_t> m_df;
198 
199 };
200 }
201 #endif /* HAVE_EIGEN3 */
202 #endif /* _KLCOVARIANCEINFERENCEMETHOD_H_ */
virtual void get_gradient_of_nlml_wrt_parameters(SGVector< float64_t > gradient)
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
An abstract class of the mean function.
Definition: MeanFunction.h:28
virtual float64_t get_derivative_related_cov(Eigen::MatrixXd eigen_dK)
double float64_t
Definition: common.h:50
The KL approximation inference method class.
The KL approximation inference method class.
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual SGVector< float64_t > get_diagonal_vector()
The Kernel base class.
Definition: Kernel.h:153
The Likelihood model base class.

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