41 #ifndef _KLINFERENCEMETHOD_H_ 42 #define _KLINFERENCEMETHOD_H_ 53 template <
class,
int,
int,
int,
int,
int>
class Matrix;
54 template <
class,
int>
class LDLT;
105 virtual const char*
get_name()
const {
return "KLInferenceMethod"; }
118 virtual float64_t get_negative_log_marginal_likelihood();
160 return m_model->supports_regression();
170 return m_model->supports_binary();
219 virtual void set_lbfgs_parameters(
int m = 100,
220 int max_linesearch = 1000,
222 int max_iterations = 1000,
233 int orthantwise_start = 0,
234 int orthantwise_end = 1);
260 virtual void set_noise_factor(
float64_t noise_factor);
268 virtual void set_max_attempt(
index_t max_attempt);
276 virtual void set_exp_factor(
float64_t exp_factor);
284 virtual void set_min_coeff_kernel(
float64_t min_coeff_kernel);
288 virtual void compute_gradient();
305 virtual void update_init();
326 virtual void update_approx_cov()=0;
388 virtual float64_t get_negative_log_marginal_likelihood_helper()=0;
395 virtual float64_t get_nlml_wrt_parameters();
412 virtual bool lbfgs_precompute()=0;
float64_t m_orthantwise_c
virtual bool supports_regression() const
The Inference Method base class.
The class Labels models labels, i.e. class assignments of objects.
The variational Gaussian Likelihood base class. The variational distribution is Gaussian.
An abstract class of the mean function.
virtual const char * get_name() const
static const float64_t epsilon
SGMatrix< float64_t > m_Sigma
float64_t m_min_coeff_kernel
Matrix< float64_t,-1,-1, 0,-1,-1 > MatrixXd
The KL approximation inference method class.
all of classes and functions are contained in the shogun namespace
virtual bool supports_binary() const
The class Features is the base class of all feature objects.
void(* update)(float *foo, float bar)
SGVector< float64_t > m_mu
SGVector< float64_t > m_s2
The Likelihood model base class.
virtual EInferenceType get_inference_type() const