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KLCholeskyInferenceMethod.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
24  * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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  * Challis, Edward, and David Barber.
36  * "Concave Gaussian variational approximations for inference in large-scale Bayesian linear models."
37  * International conference on Artificial Intelligence and Statistics. 2011.
38  *
39  */
40 
41 #ifndef _KLCHOLESKYINFERENCEMETHOD_H_
42 #define _KLCHOLESKYINFERENCEMETHOD_H_
43 
44 #include <shogun/lib/config.h>
45 
46 #ifdef HAVE_EIGEN3
48 
49 namespace shogun
50 {
51 
74 {
75 public:
78 
87  CKLCholeskyInferenceMethod(CKernel* kernel, CFeatures* features,
88  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);
89 
91 
96  virtual const char* get_name() const { return "KLCholeskyInferenceMethod"; }
97 
103 
110 
115  virtual SGVector<float64_t> get_alpha();
116 
117 protected:
119  virtual void update_alpha();
120 
127 
134 
144  virtual bool lbfgs_precompute();
145 
147  virtual void update_Sigma();
148 
150  virtual void update_InvK_Sigma();
151 private:
152  void init();
153 
156  void update_C();
157 
160  void get_lower_triangular_vector(SGMatrix<float64_t> square_matrix, SGVector<float64_t> target);
161 
166 
168  SGMatrix<float64_t> m_InvK_C;
169 
170 };
171 }
172 #endif /* HAVE_EIGEN3 */
173 #endif /* _KLCHOLESKYINFERENCEMETHOD_H_ */
The Inference Method base class.
static CKLCholeskyInferenceMethod * obtain_from_generic(CInferenceMethod *inference)
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual float64_t get_negative_log_marginal_likelihood_helper()
virtual const char * get_name() const
An abstract class of the mean function.
Definition: MeanFunction.h:49
virtual SGVector< float64_t > get_alpha()
virtual void get_gradient_of_nlml_wrt_parameters(SGVector< float64_t > gradient)
virtual EInferenceType get_inference_type() const
The KL approximation inference method class.
double float64_t
Definition: common.h:50
The KL approximation inference method class.
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
The class Features is the base class of all feature objects.
Definition: Features.h:68
The Kernel base class.
Definition: Kernel.h:158
The Likelihood model base class.

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