# LeastAngleRegression.h

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```00001 /*
00002  * This program is free software; you can redistribute it and/or modify
00004  * the Free Software Foundation; either version 3 of the License, or
00005  * (at your option) any later version.
00006  *
00007  * Written (W) 2012 Chiyuan Zhang
00008  * Copyright (C) 2012 Chiyuan Zhang
00009  */
00010
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 }; // class LARS
00207
00208 } // namespace shogun
00209
00210 #endif // HAVE_LAPACK
00211 #endif // LEASTANGLEREGRESSION_H__
```

SHOGUN Machine Learning Toolbox - Documentation