SHOGUN  4.0.0
MultiLaplacianInferenceMethod.h
Go to the documentation of this file.
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  * https://gist.github.com/yorkerlin/14ace49f2278f3859614
32  * Gaussian Process Machine Learning Toolbox
33  * http://www.gaussianprocess.org/gpml/code/matlab/doc/
34  * and
35  * GPstuff - Gaussian process models for Bayesian analysis
36  * http://becs.aalto.fi/en/research/bayes/gpstuff/
37  *
38  * The reference pseudo code is the algorithm 3.3 of the GPML textbook
39  *
40  */
41 
42 #ifndef CMULTILAPLACIANINFERENCEMETHOD_H_
43 #define CMULTILAPLACIANINFERENCEMETHOD_H_
44 
45 #include <shogun/lib/config.h>
46 
47 #ifdef HAVE_EIGEN3
49 
50 namespace shogun
51 {
52 
71 {
72 public:
75 
85  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);
86 
88 
94  virtual const char* get_name() const { return "MultiLaplacianInferenceMethod"; }
95 
108 
114  virtual SGVector<float64_t> get_diagonal_vector();
115 
120  virtual bool supports_multiclass() const
121  {
122  check_members();
123  return m_model->supports_multiclass();
124  }
125 
126 protected:
127 
129  virtual void check_members() const;
130 
132  virtual void update_alpha();
133 
135  virtual void update_chol();
136 
138  virtual void update_approx_cov();
139 
143  virtual void update_deriv();
144 
152  virtual SGVector<float64_t> get_derivative_wrt_inference_method(
153  const TParameter* param);
154 
162  virtual SGVector<float64_t> get_derivative_wrt_likelihood_model(
163  const TParameter* param);
164 
172  virtual SGVector<float64_t> get_derivative_wrt_kernel(
173  const TParameter* param);
174 
182  virtual SGVector<float64_t> get_derivative_wrt_mean(
183  const TParameter* param);
184 private:
185 
186  void init();
187 
188 protected:
189 
191  SGMatrix<float64_t> m_U;
192 
194  float64_t m_nlz;
195 
203  virtual float64_t get_derivative_helper(SGMatrix<float64_t> dK);
204 
211  virtual void get_dpi_helper();
212 };
213 }
214 #endif /* HAVE_EIGEN3 */
215 #endif /* CMULTILAPLACIANINFERENCEMETHOD_H_ */
virtual bool supports_multiclass() const
virtual void update_alpha()=0
virtual void update_approx_cov()=0
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
The Laplace approximation inference method base class.
An abstract class of the mean function.
Definition: MeanFunction.h:28
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)=0
double float64_t
Definition: common.h:50
The Laplace approximation inference method class for multi classification.
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)=0
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)=0
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
virtual SGVector< float64_t > get_derivative_wrt_mean(const TParameter *param)=0
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual void update_chol()=0
virtual bool supports_multiclass() const
virtual void check_members() const
The Kernel base class.
Definition: Kernel.h:153
virtual void update_deriv()=0
The Likelihood model base class.
CLikelihoodModel * m_model
static void * get_derivative_helper(void *p)

SHOGUN Machine Learning Toolbox - Documentation