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KLDualInferenceMethod.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Wu Lin
4  * All rights reserved.
5  *
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12  * this list of conditions and the following disclaimer in the documentation
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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
18  * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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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 
36 #ifndef _KLDUALINFERENCEMETHOD_H_
37 #define _KLDUALINFERENCEMETHOD_H_
38 
39 #include <shogun/lib/config.h>
40 
41 #ifdef HAVE_EIGEN3
44 
45 namespace shogun
46 {
47 
66 {
67 public:
70 
79  CKLDualInferenceMethod(CKernel* kernel, CFeatures* features,
80  CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);
81 
82  virtual ~CKLDualInferenceMethod();
83 
88  virtual const char* get_name() const { return "KLDualInferenceMethod"; }
89 
100  virtual SGVector<float64_t> get_alpha();
101 
114 
119  void set_model(CLikelihoodModel* mod);
120 protected:
121 
128 
133 
139  virtual void check_dual_inference(CLikelihoodModel* mod) const;
140 
142  virtual void update_approx_cov();
143 
145  virtual void update_alpha();
146 
148  virtual void update_chol();
149 
153  virtual void update_deriv();
154 
161 
170  virtual bool lbfgs_precompute();
171 
185 
187  virtual float64_t lbfgs_optimization();
188 
206 
223 
224 private:
225  void init();
226 
228  SGVector<float64_t> m_sW;
229 
234 
238  SGVector<float64_t> m_dv;
239 
241  SGVector<float64_t> m_df;
242 
253  bool m_is_dual_valid;
254 
266 
271  static float64_t evaluate(void *obj,
272  const float64_t *parameters,
273  float64_t *gradient, const int dim,
274  const float64_t step);
275 
281  static float64_t adjust_step(void *obj,
282  const float64_t *parameters,
283  const float64_t *direction,
284  const int dim, const float64_t step);
285 
286 };
287 }
288 #endif /* HAVE_EIGEN3 */
289 #endif /* _KLDUALINFERENCEMETHOD_H_ */
virtual void get_gradient_of_nlml_wrt_parameters(SGVector< float64_t > gradient)
virtual CDualVariationalGaussianLikelihood * get_dual_variational_likelihood() const
virtual const char * get_name() const
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
An abstract class of the mean function.
Definition: MeanFunction.h:28
virtual void check_dual_inference(CLikelihoodModel *mod) const
The dual KL approximation inference method class.
void set_model(CLikelihoodModel *mod)
virtual void get_gradient_of_dual_objective_wrt_parameters(SGVector< float64_t > gradient)
virtual SGVector< float64_t > get_alpha()
virtual float64_t get_derivative_related_cov(Eigen::MatrixXd eigen_dK)
double float64_t
Definition: common.h:50
The KL approximation inference method class.
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:68
The Kernel base class.
Definition: Kernel.h:153
virtual float64_t get_dual_objective_wrt_parameters()
virtual float64_t get_negative_log_marginal_likelihood_helper()
virtual SGVector< float64_t > get_diagonal_vector()
Class that models dual variational likelihood.
The Likelihood model base class.

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