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MultiLaplacianInferenceMethod.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
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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 
96 
102 
109 
122 
128  virtual SGVector<float64_t> get_diagonal_vector();
129 
134  virtual bool supports_multiclass() const
135  {
136  check_members();
137  return m_model->supports_multiclass();
138  }
139 
152  virtual SGVector<float64_t> get_posterior_mean();
153 protected:
154 
156  virtual void check_members() const;
157 
159  virtual void update_alpha();
160 
162  virtual void update_chol();
163 
165  virtual void update_approx_cov();
166 
170  virtual void update_deriv();
171 
179  virtual SGVector<float64_t> get_derivative_wrt_inference_method(
180  const TParameter* param);
181 
189  virtual SGVector<float64_t> get_derivative_wrt_likelihood_model(
190  const TParameter* param);
191 
199  virtual SGVector<float64_t> get_derivative_wrt_kernel(
200  const TParameter* param);
201 
209  virtual SGVector<float64_t> get_derivative_wrt_mean(
210  const TParameter* param);
211 private:
212 
213  void init();
214 
215 protected:
216 
219 
222 
231 
238  virtual void get_dpi_helper();
239 };
240 }
241 #endif /* HAVE_EIGEN3 */
242 #endif /* CMULTILAPLACIANINFERENCEMETHOD_H_ */
virtual bool supports_multiclass() const
The Inference Method base class.
virtual float64_t get_derivative_helper(SGMatrix< float64_t > dK)
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)
The Laplace approximation inference method base class.
An abstract class of the mean function.
Definition: MeanFunction.h:49
virtual EInferenceType get_inference_type() const
static CMultiLaplacianInferenceMethod * obtain_from_generic(CInferenceMethod *inference)
double float64_t
Definition: common.h:50
The Laplace approximation inference method class for multi classification.
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)
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)
The class Features is the base class of all feature objects.
Definition: Features.h:68
virtual bool supports_multiclass() const
The Kernel base class.
Definition: Kernel.h:158
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)
The Likelihood model base class.
CLikelihoodModel * m_model

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