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DiscreteDistribution.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (w) 2014 Parijat Mazumdar
4  * All rights reserved.
5  *
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29  */
30 #ifndef _DISCRETEDISTRIBUTION_H__
31 #define _DISCRETEDISTRIBUTION_H__
32 
33 #include <shogun/lib/config.h>
35 
36 namespace shogun
37 {
38 
42 {
43  public:
44  /* constructor */
46 
47  /* destructor */
49 
56  virtual bool train(CFeatures* data=NULL)=0;
57 
64  virtual int32_t get_num_model_parameters()=0;
65 
72  virtual float64_t get_log_model_parameter(int32_t num_param)=0;
73 
82  virtual float64_t get_log_derivative(int32_t num_param, int32_t num_example)=0;
83 
91  virtual float64_t get_log_likelihood_example(int32_t num_example)=0;
92 
100  virtual void update_params_em(SGVector<float64_t> alpha_k)=0;
101 };
102 }
103 #endif /* _DISCRETEDISTRIBUTION_H__ */
virtual float64_t get_log_model_parameter(int32_t num_param)=0
This is the base interface class for all discrete distributions.
virtual bool train(CFeatures *data=NULL)=0
Base class Distribution from which all methods implementing a distribution are derived.
Definition: Distribution.h:44
double float64_t
Definition: common.h:50
virtual float64_t get_log_derivative(int32_t num_param, int32_t num_example)=0
virtual int32_t get_num_model_parameters()=0
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
virtual float64_t get_log_likelihood_example(int32_t num_example)=0
virtual void update_params_em(SGVector< float64_t > alpha_k)=0

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