33 #ifndef CSPARSEINFERENCE_H
34 #define CSPARSEINFERENCE_H
102 virtual const char*
get_name()
const {
return "SparseBaseInferenceMethod"; }
110 REQUIRE(feat,
"Input inducing features must be not empty\n");
112 REQUIRE(lat_type,
"Inducing features (%s) must be"
113 " DotFeatures or one of its subclasses\n", feat->
get_name());
126 SG_REF(inducing_features);
127 return inducing_features;
virtual const char * get_name() const =0
virtual SGVector< float64_t > get_derivative_wrt_inducing_noise(const TParameter *param)=0
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)=0
virtual void set_inducing_features(CFeatures *feat)
virtual ~CSparseInference()
virtual const char * get_name() const
The class Labels models labels, i.e. class assignments of objects.
SGVector< float64_t > m_mu
virtual SGVector< float64_t > get_derivative_wrt_mean(const TParameter *param)=0
virtual void set_inducing_noise(float64_t noise)
An abstract class of the mean function.
Features that support dot products among other operations.
virtual void check_members() const
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)=0
virtual SGMatrix< float64_t > get_posterior_covariance()=0
virtual EInferenceType get_inference_type() const
virtual void update_train_kernel()
virtual SGMatrix< float64_t > get_cholesky()
virtual void check_features()
SGMatrix< float64_t > m_Sigma
virtual CFeatures * get_inducing_features()
virtual void convert_features()
SGMatrix< float64_t > m_kuu
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)=0
virtual SGVector< float64_t > get_alpha()
SGVector< float64_t > m_ktrtr_diag
all of classes and functions are contained in the shogun namespace
The Inference Method base class.
SGMatrix< float64_t > m_inducing_features
virtual SGVector< float64_t > get_derivative_wrt_inducing_features(const TParameter *param)=0
The class Features is the base class of all feature objects.
virtual float64_t get_inducing_noise()
SGMatrix< float64_t > m_ktru
The Fully Independent Conditional Training inference base class.
SGMatrix< float64_t > get_computed_dot_feature_matrix()
The Likelihood model base class.
virtual SGVector< float64_t > get_posterior_mean()=0
float64_t m_log_ind_noise