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DualVariationalGaussianLikelihood.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
4  * All rights reserved.
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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
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29  *
30  * the reference paper is
31  * Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger
32  * Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML2013
33  */
34 
35 #ifndef _DUALVARIATIONALGAUSSIANLIKELIHOOD_H_
36 #define _DUALVARIATIONALGAUSSIANLIKELIHOOD_H_
37 
38 #include <shogun/lib/config.h>
39 
40 #ifdef HAVE_EIGEN3
43 
44 namespace shogun
45 {
65 {
66 public:
69 
71 
76  virtual const char* get_name() const { return "DualVariationalGaussianLikelihood"; }
77 
84 
93 
100  virtual bool supports_derivative_wrt_hyperparameter() const;
101 
111 
121  SGVector<float64_t> s2, const CLabels* lab);
122 
127  virtual bool dual_parameters_valid() const;
128 
139  virtual float64_t adjust_step_wrt_dual_parameter(SGVector<float64_t> direction, const float64_t step) const;
140 
149  virtual void set_dual_parameters(SGVector<float64_t> lambda, const CLabels* lab);
150 
155  virtual SGVector<float64_t> get_mu_dual_parameter() const=0;
156 
162 
167  virtual float64_t get_dual_upper_bound() const=0;
168 
173  virtual float64_t get_dual_lower_bound() const=0;
174 
179  virtual bool dual_upper_bound_strict() const=0;
180 
185  virtual bool dual_lower_bound_strict() const=0;
186 
192 
199  virtual SGVector<float64_t> get_dual_first_derivative(const TParameter* param) const=0;
200 
206  virtual void set_strict_scale(float64_t strict_scale);
207 protected:
208 
217 
225 
228 
233  virtual void precompute();
234 
239 private:
241  void init();
242 
243 };
244 }
245 #endif /* HAVE_EIGEN3 */
246 #endif /* _DUALVARIATIONALGAUSSIANLIKELIHOOD_H_ */
virtual SGVector< float64_t > get_first_derivative_wrt_hyperparameter(const TParameter *param) const
virtual SGVector< float64_t > get_mu_dual_parameter() const =0
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
virtual bool dual_lower_bound_strict() const =0
The variational Gaussian Likelihood base class. The variational distribution is Gaussian.
parameter struct
Definition: Parameter.h:32
virtual SGVector< float64_t > get_dual_objective_value()=0
virtual float64_t get_dual_upper_bound() const =0
virtual float64_t adjust_step_wrt_dual_parameter(SGVector< float64_t > direction, const float64_t step) const
double float64_t
Definition: common.h:50
virtual SGVector< float64_t > get_variance_dual_parameter() const =0
virtual SGVector< float64_t > get_dual_first_derivative(const TParameter *param) const =0
virtual CVariationalGaussianLikelihood * get_variational_likelihood() const
virtual float64_t get_dual_lower_bound() const =0
virtual void set_dual_parameters(SGVector< float64_t > lambda, const CLabels *lab)
virtual void set_variational_distribution(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab)
virtual bool dual_upper_bound_strict() const =0
virtual SGVector< float64_t > get_variational_first_derivative(const TParameter *param) const
Class that models dual variational likelihood.

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