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00011 #ifndef LEASTANGLEREGRESSION_H__
00012 #define LEASTANGLEREGRESSION_H__
00013
00014 #include <shogun/lib/config.h>
00015
00016 #ifdef HAVE_LAPACK
00017 #include <vector>
00018 #include <shogun/machine/LinearMachine.h>
00019
00020 namespace shogun
00021 {
00022 class CFeatures;
00023
00072 class CLeastAngleRegression: public CLinearMachine
00073 {
00074 public:
00076 MACHINE_PROBLEM_TYPE(PT_REGRESSION);
00077
00082 CLeastAngleRegression(bool lasso=true);
00083
00085 virtual ~CLeastAngleRegression();
00086
00091 void set_max_non_zero(int32_t n)
00092 {
00093 m_max_nonz = n;
00094 }
00095
00098 int32_t get_max_non_zero() const
00099 {
00100 return m_max_nonz;
00101 }
00102
00107 void set_max_l1_norm(float64_t norm)
00108 {
00109 m_max_l1_norm = norm;
00110 }
00111
00114 float64_t get_max_l1_norm() const
00115 {
00116 return m_max_l1_norm;
00117 }
00118
00123 void switch_w(int32_t num_variable)
00124 {
00125 if (w.vlen <= 0)
00126 SG_ERROR("cannot swith estimator before training");
00127 if (size_t(num_variable) >= m_beta_idx.size() || num_variable < 0)
00128 SG_ERROR("cannot switch to an estimator of %d non-zero coefficients", num_variable);
00129 if (w.vector == NULL)
00130 w = SGVector<float64_t>(w.vlen);
00131 std::copy(m_beta_path[m_beta_idx[num_variable]].begin(),
00132 m_beta_path[m_beta_idx[num_variable]].end(), w.vector);
00133 }
00134
00143 int32_t get_path_size() const
00144 {
00145 return m_beta_idx.size();
00146 }
00147
00157 SGVector<float64_t> get_w(int32_t num_var)
00158 {
00159 return SGVector<float64_t>(&m_beta_path[m_beta_idx[num_var]][0], w.vlen, false);
00160 }
00161
00166 virtual EMachineType get_classifier_type()
00167 {
00168 return CT_LARS;
00169 }
00170
00172 virtual const char* get_name() const { return "LARS"; }
00173
00174 protected:
00175 virtual bool train_machine(CFeatures* data=NULL);
00176
00177 private:
00178 void activate_variable(int32_t v)
00179 {
00180 m_num_active++;
00181 m_active_set.push_back(v);
00182 m_is_active[v] = true;
00183 }
00184 void deactivate_variable(int32_t v_idx)
00185 {
00186 m_num_active--;
00187 m_is_active[m_active_set[v_idx]] = false;
00188 m_active_set.erase(m_active_set.begin() + v_idx);
00189 }
00190
00191 SGMatrix<float64_t> cholesky_insert(SGMatrix<float64_t> X, SGMatrix<float64_t> R, int32_t i_max_corr);
00192 SGMatrix<float64_t> cholesky_delete(SGMatrix<float64_t> R, int32_t i_kick);
00193
00194
00195 bool m_lasso;
00196
00197 int32_t m_max_nonz;
00198 float64_t m_max_l1_norm;
00199
00200 std::vector<std::vector<float64_t> > m_beta_path;
00201 std::vector<int32_t> m_beta_idx;
00202
00203 std::vector<int32_t> m_active_set;
00204 std::vector<bool> m_is_active;
00205 int32_t m_num_active;
00206 };
00207
00208 }
00209
00210 #endif // HAVE_LAPACK
00211 #endif // LEASTANGLEREGRESSION_H__