41 #ifndef _KLINFERENCE_H_
42 #define _KLINFERENCE_H_
51 template <
class,
int,
int,
int,
int,
int>
class Matrix;
52 template <
class,
int>
class LDLT;
103 virtual const char*
get_name()
const {
return "KLInference"; }
virtual float64_t optimization()
virtual void set_min_coeff_kernel(float64_t min_coeff_kernel)
virtual bool supports_regression() const
virtual SGVector< float64_t > get_derivative_wrt_kernel(const TParameter *param)
virtual void set_max_attempt(index_t max_attempt)
virtual bool supports_binary() const
The class Labels models labels, i.e. class assignments of objects.
virtual CVariationalGaussianLikelihood * get_variational_likelihood() const
virtual SGVector< float64_t > get_derivative_wrt_mean(const TParameter *param)
The variational Gaussian Likelihood base class. The variational distribution is Gaussian.
virtual void set_model(CLikelihoodModel *mod)
An abstract class of the mean function.
virtual float64_t get_negative_log_marginal_likelihood_helper()=0
virtual void register_minimizer(Minimizer *minimizer)
virtual Eigen::LDLT< Eigen::MatrixXd, 0x1 > update_init_helper()
friend class KLInferenceCostFunction
virtual void check_variational_likelihood(CLikelihoodModel *mod) const
float64_t m_min_coeff_kernel
virtual SGVector< float64_t > get_derivative_wrt_inference_method(const TParameter *param)
The KL approximation inference method class.
virtual SGMatrix< float64_t > get_posterior_covariance()
virtual void set_noise_factor(float64_t noise_factor)
virtual SGVector< float64_t > get_posterior_mean()
virtual float64_t get_derivative_related_cov(SGMatrix< float64_t > dK)=0
virtual void get_gradient_of_nlml_wrt_parameters(SGVector< float64_t > gradient)=0
virtual bool supports_regression() const
virtual bool supports_binary() const
SGVector< float64_t > m_mu
virtual SGVector< float64_t > get_derivative_wrt_likelihood_model(const TParameter *param)
Matrix< float64_t,-1,-1, 0,-1,-1 > MatrixXd
virtual const char * get_name() const
SGMatrix< float64_t > m_Sigma
virtual void update_init()
virtual SGMatrix< float64_t > get_cholesky()
SGVector< float64_t > m_s2
virtual EInferenceType get_inference_type() const
virtual bool precompute()=0
virtual float64_t get_nlml_wrt_parameters()
all of classes and functions are contained in the shogun namespace
The Inference Method base class.
The class Features is the base class of all feature objects.
virtual float64_t get_negative_log_marginal_likelihood()
virtual void update_approx_cov()=0
The minimizer base class.
virtual void set_exp_factor(float64_t exp_factor)
CLikelihoodModel * m_model
virtual void compute_gradient()
The Likelihood model base class.
virtual void check_members() const