SHOGUN  6.1.3
SingleSparseInference.h
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31 
32 
33 #ifndef CSINGLESPARSEINFERENCE_H
34 #define CSINGLESPARSEINFERENCE_H
35 
36 #include <shogun/lib/config.h>
39 #include <shogun/lib/Lock.h>
40 
41 namespace shogun
42 {
43 class SingleSparseInferenceCostFunction;
44 
49 {
51 
52 public:
55 
65  CSingleSparseInference(CKernel* kernel, CFeatures* features,
66  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model,
67  CFeatures* inducing_features);
68 
69  virtual ~CSingleSparseInference();
70 
75  virtual const char* get_name() const { return "SingleSparseInference"; }
76 
81  virtual void set_kernel(CKernel* kern);
82 
86  virtual void optimize_inducing_features();
87 
99 
111 
117 
122  virtual void set_max_iterations_for_inducing_features(int32_t it);
123 
129  virtual void enable_optimizing_inducing_features(bool is_optmization, FirstOrderMinimizer* minimizer=NULL);
130 
131 protected:
132 
148 
156  const TParameter* param)=0;
157 
158 
167  const TParameter* param);
168 
169 
178  const TParameter* param);
179 
185  virtual void check_bound(SGVector<float64_t> bound, const char* name);
186 
189 
192 
195 
198 
201 
210  virtual void check_fully_sparse();
211 
222 
225 
228 
231 private:
232  /* init */
233  void init();
234 };
235 }
236 #endif /* CSINGLESPARSEINFERENCE_H */
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual SGVector< float64_t > get_derivative_wrt_inducing_features(const TParameter *param)=0
virtual void set_kernel(CKernel *kern)
virtual void set_max_iterations_for_inducing_features(int32_t it)
parameter struct
FirstOrderMinimizer * m_inducing_minimizer
virtual void enable_optimizing_inducing_features(bool is_optmization, FirstOrderMinimizer *minimizer=NULL)
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 void set_tolearance_for_inducing_features(float64_t tol)
Class Lock used for synchronization in concurrent programs.
Definition: Lock.h:19
The sparse inference base class for classification and regression for 1-D labels (1D regression and b...
virtual const char * get_name() const
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)
double float64_t
Definition: common.h:60
virtual void set_upper_bound_of_inducing_features(SGVector< float64_t > bound)
virtual void check_bound(SGVector< float64_t > bound, const char *name)
virtual void set_lower_bound_of_inducing_features(SGVector< float64_t > bound)
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:69
virtual float64_t get_derivative_related_cov(SGVector< float64_t > ddiagKi, SGMatrix< float64_t > dKuui, SGMatrix< float64_t > dKui)=0
The Kernel base class.
virtual SGVector< float64_t > get_derivative_wrt_inducing_noise(const TParameter *param)=0
The Fully Independent Conditional Training inference base class.
The Likelihood model base class.
The first order minimizer base class.

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