SHOGUN  6.1.3
MultiLaplaceInferenceMethod.h
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3  * Written (w) 2014 Wu Lin
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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 CMULTILAPLACEINFERENCEMETHOD_H_
43 #define CMULTILAPLACEINFERENCEMETHOD_H_
44 
45 #include <shogun/lib/config.h>
46 
48 
49 namespace shogun
50 {
51 
70 {
71 public:
74 
84  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);
85 
87 
93  virtual const char* get_name() const { return "MultiLaplaceInferenceMethod"; }
94 
95 
101 
108 
121 
128 
133  virtual bool supports_multiclass() const
134  {
135  check_members();
136  return m_model->supports_multiclass();
137  }
138 
152 
158 
163  virtual void set_newton_tolerance(float64_t tol) { m_tolerance=tol; }
164 
169  virtual int32_t get_newton_iterations() { return m_iter; }
170 
175  virtual void set_newton_iterations(int32_t iter) { m_iter=iter; }
176 
182 
188 
194 
200 
201 protected:
202 
204  virtual void check_members() const;
205 
207  virtual void update_alpha();
208 
210  virtual void update_chol();
211 
213  virtual void update_approx_cov();
214 
218  virtual void update_deriv();
219 
228  const TParameter* param);
229 
238  const TParameter* param);
239 
248  const TParameter* param);
249 
258  const TParameter* param);
259 private:
260 
261  void init();
262 
263 protected:
264 
267 
270 
279 
286  virtual void get_dpi_helper();
287 
290 
293 
296 
299 };
300 }
301 #endif /* CMULTILAPLACEINFERENCEMETHOD_H_ */
virtual bool supports_multiclass() const
virtual float64_t get_derivative_helper(SGMatrix< float64_t > dK)
int32_t index_t
Definition: common.h:72
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual void set_minimization_tolerance(float64_t tol)
The Laplace approximation inference method class for multi classification.
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)
virtual SGVector< float64_t > get_diagonal_vector()
parameter struct
virtual SGVector< float64_t > get_posterior_mean()
An abstract class of the mean function.
Definition: MeanFunction.h:49
std::enable_if<!std::is_same< T, complex128_t >::value, float64_t >::type mean(const Container< T > &a)
virtual EInferenceType get_inference_type() const
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)
double float64_t
Definition: common.h:60
EInferenceType
Definition: Inference.h:53
virtual SGVector< float64_t > get_derivative_wrt_mean(const TParameter *param)
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
The Laplace approximation inference method base class.
The Inference Method base class.
Definition: Inference.h:81
The class Features is the base class of all feature objects.
Definition: Features.h:69
The Kernel base class.
static CMultiLaplaceInferenceMethod * obtain_from_generic(CInference *inference)
T max(const Container< T > &a)
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)
CLikelihoodModel * m_model
Definition: Inference.h:470
The Likelihood model base class.

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