12 #ifndef _LIBLINEAR_H___
13 #define _LIBLINEAR_H___
102 liblinear_solver_type=st;
155 virtual const char*
get_name()
const {
return "LibLinear"; }
193 void train_one(
const liblinear_problem *prob,
const liblinear_parameter *param,
double Cp,
double Cn);
194 void solve_l2r_l1l2_svc(
197 void solve_l1r_l2_svc(liblinear_problem *prob_col,
double eps,
double Cp,
double Cn);
198 void solve_l1r_lr(
const liblinear_problem *prob_col,
double eps,
double Cp,
double Cn);
199 void solve_l2r_lr_dual(
const liblinear_problem *prob,
double eps,
double Cp,
double Cn);
223 #endif //_LIBLINEAR_H___
L2 regularized linear logistic regression via dual.
The class Labels models labels, i.e. class assignments of objects.
void set_C(float64_t c_neg, float64_t c_pos)
L2 regularized SVM with L2-loss using newton in the primal.
L1 regularized logistic regression.
L1 regularized SVM with L2-loss using dual coordinate descent.
Features that support dot products among other operations.
SGVector< float64_t > get_linear_term()
void set_bias_enabled(bool enable_bias)
void set_linear_term(const SGVector< float64_t > linear_term)
#define MACHINE_PROBLEM_TYPE(PT)
void set_max_iterations(int32_t max_iter=1000)
L2 regularized linear logistic regression.
This class provides an interface to the LibLinear library for large- scale linear learning focusing o...
L2 regularized SVM with L2-loss using dual coordinate descent.
Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
LIBLINEAR_SOLVER_TYPE liblinear_solver_type
all of classes and functions are contained in the shogun namespace
LIBLINEAR_SOLVER_TYPE get_liblinear_solver_type()
The class Features is the base class of all feature objects.
virtual bool train_machine(CFeatures *data=NULL)
virtual const char * get_name() const
virtual EMachineType get_classifier_type()
int32_t get_max_iterations()
L2 regularized linear SVM with L1-loss using dual coordinate descent.
void set_epsilon(float64_t eps)
void set_liblinear_solver_type(LIBLINEAR_SOLVER_TYPE st)
SGVector< float64_t > m_linear_term