11 #ifndef LEASTANGLEREGRESSION_H__
12 #define LEASTANGLEREGRESSION_H__
110 m_max_l1_norm = norm;
117 return m_max_l1_norm;
127 REQUIRE(
w.
vlen > 0,
"Please train the model (i.e. run the model's train() method) before updating its weights.\n")
128 REQUIRE(
size_t(num_variable) < m_beta_idx.size() && num_variable >= 0,
129 "Cannot switch to an estimator of %d non-zero coefficients.\n", num_variable)
133 std::copy(m_beta_path[m_beta_idx[num_variable]].begin(),
134 m_beta_path[m_beta_idx[num_variable]].end(),
w.
vector);
147 return m_beta_idx.size();
186 virtual const char*
get_name()
const {
return "LeastAngleRegression"; }
200 template <
typename ST>
204 template <
typename ST>
207 template <
typename ST>
212 template <
typename ST>
213 static void find_max_abs(
const std::vector<ST> &vec,
const std::vector<bool> &ignore_mask,
214 int32_t &imax, ST& vmax);
223 template <
typename ST>
226 void activate_variable(int32_t v)
229 m_active_set.push_back(v);
230 m_is_active[v] =
true;
233 void deactivate_variable(int32_t v_idx)
236 m_is_active[m_active_set[v_idx]] =
false;
237 m_active_set.erase(m_active_set.begin() + v_idx);
245 std::vector<std::vector<float64_t> > m_beta_path;
246 std::vector<int32_t> m_beta_idx;
247 std::vector<int32_t> m_active_set;
248 std::vector<bool> m_is_active;
249 int32_t m_num_active;
255 #endif // LEASTANGLEREGRESSION_H__
MACHINE_PROBLEM_TYPE(PT_REGRESSION)
SGMatrix< ST > cholesky_delete(SGMatrix< ST > &R, int32_t i_kick)
The class DenseFeatures implements dense feature matrices.
virtual ~CLeastAngleRegression()
static void find_max_abs(const std::vector< ST > &vec, const std::vector< bool > &ignore_mask, int32_t &imax, ST &vmax)
static void plane_rot(ST x0, ST x1, ST &y0, ST &y1, SGMatrix< ST > &G)
virtual EMachineType get_classifier_type()
void switch_w(int32_t num_variable)
int32_t get_path_size() const
float64_t get_max_l1_norm() const
Class for Least Angle Regression, can be used to solve LASSO.
Class LinearMachine is a generic interface for all kinds of linear machines like classifiers.
bool train_machine(CFeatures *data)
void set_max_l1_norm(float64_t norm)
void set_epsilon(float64_t epsilon)
int32_t get_max_non_zero() const
all of classes and functions are contained in the shogun namespace
SGMatrix< ST > cholesky_insert(const SGMatrix< ST > &X, const SGMatrix< ST > &X_active, SGMatrix< ST > &R, int32_t i_max_corr, int32_t num_active)
CLeastAngleRegression(bool lasso=true)
SGVector< float64_t > get_w_for_var(int32_t num_var)
The class Features is the base class of all feature objects.
virtual const char * get_name() const
void set_max_non_zero(int32_t n)