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GaussianLikelihood.h
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1 /*
2  * Copyright (c) The Shogun Machine Learning Toolbox
3  * Written (W) 2013 Roman Votyakov
4  * Written (W) 2012 Jacob Walker
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32 #ifndef CGAUSSIANLIKELIHOOD_H_
33 #define CGAUSSIANLIKELIHOOD_H_
34 
35 #include <shogun/lib/config.h>
37 
38 
40 
41 namespace shogun
42 {
43 
55 {
56 public:
59 
65 
66  virtual ~CGaussianLikelihood();
67 
72  virtual const char* get_name() const { return "GaussianLikelihood"; }
73 
78  float64_t get_sigma() { return CMath::exp(m_log_sigma); }
79 
84  void set_sigma(float64_t sigma)
85  {
86  REQUIRE(sigma>0.0, "Standard deviation (%f) must be greater than zero\n",
87  sigma)
88  m_log_sigma=CMath::log(sigma);
89  }
90 
97 
114  SGVector<float64_t> s2, const CLabels* lab=NULL) const;
115 
131  SGVector<float64_t> s2, const CLabels* lab=NULL) const;
132 
137  virtual ELikelihoodModelType get_model_type() const { return LT_GAUSSIAN; }
138 
151  SGVector<float64_t> func) const;
152 
164  const CLabels* lab, SGVector<float64_t> func, index_t i) const;
165 
176  SGVector<float64_t> func, const TParameter* param) const;
177 
189  SGVector<float64_t> func, const TParameter* param) const;
190 
202  SGVector<float64_t> func, const TParameter* param) const;
203 
221  SGVector<float64_t> s2, const CLabels* lab) const;
222 
238  SGVector<float64_t> s2, const CLabels* lab, index_t i) const;
239 
255  SGVector<float64_t> s2, const CLabels* lab, index_t i) const;
256 
261  virtual bool supports_regression() const { return true; }
262 
263 private:
265  void init();
266 
268  float64_t m_log_sigma;
269 };
270 }
271 #endif /* CGAUSSIANLIKELIHOOD_H_ */
ELikelihoodModelType
Class that models Gaussian likelihood.
virtual SGVector< float64_t > get_log_probability_f(const CLabels *lab, SGVector< float64_t > func) const
int32_t index_t
Definition: common.h:62
The class Labels models labels, i.e. class assignments of objects.
Definition: Labels.h:43
parameter struct
#define REQUIRE(x,...)
Definition: SGIO.h:206
void set_sigma(float64_t sigma)
virtual float64_t get_second_moment(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const
virtual ELikelihoodModelType get_model_type() const
virtual SGVector< float64_t > get_first_derivative(const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
virtual SGVector< float64_t > get_predictive_variances(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
virtual SGVector< float64_t > get_predictive_means(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab=NULL) const
double float64_t
Definition: common.h:50
virtual const char * get_name() const
virtual float64_t get_first_moment(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab, index_t i) const
static CGaussianLikelihood * obtain_from_generic(CLikelihoodModel *lik)
virtual SGVector< float64_t > get_second_derivative(const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
virtual SGVector< float64_t > get_log_zeroth_moments(SGVector< float64_t > mu, SGVector< float64_t > s2, const CLabels *lab) const
all of classes and functions are contained in the shogun namespace
Definition: class_list.h:18
static float64_t exp(float64_t x)
Definition: Math.h:621
static float64_t log(float64_t v)
Definition: Math.h:922
virtual SGVector< float64_t > get_log_probability_derivative_f(const CLabels *lab, SGVector< float64_t > func, index_t i) const
virtual bool supports_regression() const
virtual SGVector< float64_t > get_third_derivative(const CLabels *lab, SGVector< float64_t > func, const TParameter *param) const
The Likelihood model base class.

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