SHOGUN  6.1.3
VarDTCInferenceMethod.h
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30  * Code adapted from
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33 
34 #ifndef CVARDTCINFERENCEMETHOD_H
35 #define CVARDTCINFERENCEMETHOD_H
36 
37 
38 #include <shogun/lib/config.h>
40 
41 namespace shogun
42 {
53 {
54 public:
57 
67  CVarDTCInferenceMethod(CKernel* kernel, CFeatures* features,
68  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model,
69  CFeatures* inducing_features);
70 
71  virtual ~CVarDTCInferenceMethod();
72 
77  virtual const char* get_name() const { return "VarDTCInferenceMethod"; }
78 
84 
91 
104 
105 
118 
123  virtual bool supports_regression() const
124  {
125  check_members();
126  return m_model->supports_regression();
127  }
128 
145 
162 
164  virtual void update();
165 
170  virtual void register_minimizer(Minimizer* minimizer);
171 
172 protected:
174  virtual void check_members() const;
175 
177  virtual void update_alpha();
178 
180  virtual void update_chol();
181 
185  virtual void update_deriv();
186 
195  const TParameter* param);
196 
207  const TParameter* param);
208 
216  const TParameter* param);
217 
226  const TParameter* param);
227 
243 
245  virtual void compute_gradient();
246 protected:
265 private:
267  void init();
268 };
269 }
270 #endif /* CVARDTCINFERENCEMETHOD_H */
virtual SGVector< float64_t > get_derivative_wrt_inducing_noise(const TParameter *param)
virtual SGVector< float64_t > get_posterior_mean()
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
The inference method class based on the Titsias&#39; variational bound. For more details, see Titsias, Michalis K. "Variational learning of inducing variables in sparse Gaussian processes." International Conference on Artificial Intelligence and Statistics. 2009.
virtual float64_t get_derivative_related_cov(SGVector< float64_t > ddiagKi, SGMatrix< float64_t > dKuui, SGMatrix< float64_t > dKui)
parameter struct
virtual SGVector< float64_t > get_diagonal_vector()
virtual SGVector< float64_t > get_derivative_wrt_mean(const TParameter *param)
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)
The sparse inference base class for classification and regression for 1-D labels (1D regression and b...
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)
double float64_t
Definition: common.h:60
static CVarDTCInferenceMethod * obtain_from_generic(CInference *inference)
virtual bool supports_regression() const
EInferenceType
Definition: Inference.h:53
virtual SGMatrix< float64_t > get_posterior_covariance()
virtual bool supports_regression() const
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
The Inference Method base class.
Definition: Inference.h:81
virtual EInferenceType get_inference_type() const
The class Features is the base class of all feature objects.
Definition: Features.h:69
virtual const char * get_name() const
The Kernel base class.
virtual SGVector< float64_t > get_derivative_wrt_inducing_features(const TParameter *param)
The minimizer base class.
Definition: Minimizer.h:43
virtual float64_t get_negative_log_marginal_likelihood()
CLikelihoodModel * m_model
Definition: Inference.h:470
The Likelihood model base class.
virtual void register_minimizer(Minimizer *minimizer)

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