LeastAngleRegression.h

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2012 Chiyuan Zhang
 * Copyright (C) 2012 Chiyuan Zhang
 */

#ifndef LEASTANGLEREGRESSION_H__
#define LEASTANGLEREGRESSION_H__

#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK
#include <vector>
#include <shogun/machine/LinearMachine.h>

namespace shogun
{
class CFeatures;

class CLeastAngleRegression: public CLinearMachine
{
public:
    MACHINE_PROBLEM_TYPE(PT_REGRESSION);

    CLeastAngleRegression(bool lasso=true);

    virtual ~CLeastAngleRegression();

    void set_max_non_zero(int32_t n)
    {
        m_max_nonz = n;
    }

    int32_t get_max_non_zero() const
    {
        return m_max_nonz;
    }

    void set_max_l1_norm(float64_t norm)
    {
        m_max_l1_norm = norm;
    }

    float64_t get_max_l1_norm() const
    {
        return m_max_l1_norm;
    }

    void switch_w(int32_t num_variable)
    {
        if (w.vlen <= 0)
            SG_ERROR("cannot swith estimator before training")
        if (size_t(num_variable) >= m_beta_idx.size() || num_variable < 0)
            SG_ERROR("cannot switch to an estimator of %d non-zero coefficients", num_variable)
        if (w.vector == NULL)
            w = SGVector<float64_t>(w.vlen);
        std::copy(m_beta_path[m_beta_idx[num_variable]].begin(),
            m_beta_path[m_beta_idx[num_variable]].end(), w.vector);
    }

    int32_t get_path_size() const
    {
        return m_beta_idx.size();
    }

    SGVector<float64_t> get_w_for_var(int32_t num_var)
    {
        return SGVector<float64_t>(&m_beta_path[m_beta_idx[num_var]][0], w.vlen, false);
    }

    virtual EMachineType get_classifier_type()
    {
        return CT_LARS;
    }

    virtual const char* get_name() const { return "LeastAngleRegression"; }

protected:
    virtual bool train_machine(CFeatures* data=NULL);

private:
    void activate_variable(int32_t v)
    {
        m_num_active++;
        m_active_set.push_back(v);
        m_is_active[v] = true;
    }
    void deactivate_variable(int32_t v_idx)
    {
        m_num_active--;
        m_is_active[m_active_set[v_idx]] = false;
        m_active_set.erase(m_active_set.begin() + v_idx);
    }

    SGMatrix<float64_t> cholesky_insert(SGMatrix<float64_t> X, SGMatrix<float64_t> R, int32_t i_max_corr);
    SGMatrix<float64_t> cholesky_delete(SGMatrix<float64_t> R, int32_t i_kick);


    bool m_lasso; 

    int32_t m_max_nonz;  
    float64_t m_max_l1_norm; 

    std::vector<std::vector<float64_t> > m_beta_path;
    std::vector<int32_t> m_beta_idx;

    std::vector<int32_t> m_active_set;
    std::vector<bool> m_is_active;
    int32_t m_num_active;
}; // class LARS

} // namespace shogun

#endif // HAVE_LAPACK
#endif // LEASTANGLEREGRESSION_H__