10 #ifndef _REGRESSIONLIBLINEAR_H___
11 #define _REGRESSIONLIBLINEAR_H___
67 return "LibLinearRegression";
146 void solve_l2r_l1l2_svr(
const liblinear_problem *prob);
149 void init_defaults();
152 void register_parameters();
void set_max_iter(int32_t max_iter)
virtual bool train_machine(CFeatures *data=NULL)
The class Labels models labels, i.e. class assignments of objects.
L2 regularized support vector regression with L1 epsilon tube loss.
void set_liblinear_regression_type(LIBLINEAR_REGRESSION_TYPE st)
Features that support dot products among other operations.
L2 regularized support vector regression with L2 epsilon tube loss.
static const float64_t epsilon
L2 regularized support vector regression with L2 epsilon tube loss (dual)
LIBLINEAR_REGRESSION_TYPE get_liblinear_regression_type()
#define MACHINE_PROBLEM_TYPE(PT)
int32_t get_max_iter() const
void set_tube_epsilon(float64_t eps)
Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
LIBLINEAR_REGRESSION_TYPE m_liblinear_regression_type
float64_t get_tube_epsilon()
bool get_use_bias() const
LIBLINEAR_REGRESSION_TYPE
The class Features is the base class of all feature objects.
void set_use_bias(bool use_bias)
virtual ~CLibLinearRegression()
float64_t get_epsilon() const
LibLinear for regression.
void set_epsilon(float64_t epsilon)
virtual const char * get_name() const