MulticlassLogisticRegression.cpp

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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 Sergey Lisitsyn
 * Copyright (C) 2012 Sergey Lisitsyn
 */

#include <shogun/multiclass/MulticlassLogisticRegression.h>
#ifdef HAVE_EIGEN3
#include <shogun/multiclass/MulticlassOneVsRestStrategy.h>
#include <shogun/io/SGIO.h>
#include <shogun/mathematics/Math.h>
#include <shogun/labels/MulticlassLabels.h>
#include <shogun/lib/slep/slep_mc_plain_lr.h>

using namespace shogun;

CMulticlassLogisticRegression::CMulticlassLogisticRegression() :
    CLinearMulticlassMachine()
{
    init_defaults();
}

CMulticlassLogisticRegression::CMulticlassLogisticRegression(float64_t z, CDotFeatures* feats, CLabels* labs) :
    CLinearMulticlassMachine(new CMulticlassOneVsRestStrategy(),feats,NULL,labs)
{
    init_defaults();
    set_z(z);
}

void CMulticlassLogisticRegression::init_defaults()
{
    set_z(0.1);
    set_epsilon(1e-2);
    set_max_iter(10000);
}

void CMulticlassLogisticRegression::register_parameters()
{
    SG_ADD(&m_z, "m_z", "regularization constant",MS_AVAILABLE);
    SG_ADD(&m_epsilon, "m_epsilon", "tolerance epsilon",MS_NOT_AVAILABLE);
    SG_ADD(&m_max_iter, "m_max_iter", "max number of iterations",MS_NOT_AVAILABLE);
}

CMulticlassLogisticRegression::~CMulticlassLogisticRegression()
{
}

bool CMulticlassLogisticRegression::train_machine(CFeatures* data)
{
    if (data)
        set_features((CDotFeatures*)data);

    REQUIRE(m_features, "%s::train_machine(): No features attached!\n");
    REQUIRE(m_labels, "%s::train_machine(): No labels attached!\n");
    REQUIRE(m_labels->get_label_type()==LT_MULTICLASS, "%s::train_machine(): "
            "Attached labels are no multiclass labels\n");
    REQUIRE(m_multiclass_strategy, "%s::train_machine(): No multiclass strategy"
            " attached!\n");

    int32_t n_classes = ((CMulticlassLabels*)m_labels)->get_num_classes();
    int32_t n_feats = m_features->get_dim_feature_space();

    slep_options options = slep_options::default_options();
    if (m_machines->get_num_elements()!=0)
    {
        SGMatrix<float64_t> all_w_old(n_feats, n_classes);
        SGVector<float64_t> all_c_old(n_classes);
        for (int32_t i=0; i<n_classes; i++)
        {
            CLinearMachine* machine = (CLinearMachine*)m_machines->get_element(i);
            SGVector<float64_t> w = machine->get_w();
            for (int32_t j=0; j<n_feats; j++)
                all_w_old(j,i) = w[j];
            all_c_old[i] = machine->get_bias();
            SG_UNREF(machine);
        }
        options.last_result = new slep_result_t(all_w_old,all_c_old);
        m_machines->reset_array();
    }
    options.tolerance = m_epsilon;
    options.max_iter = m_max_iter;
    slep_result_t result = slep_mc_plain_lr(m_features,(CMulticlassLabels*)m_labels,m_z,options);

    SGMatrix<float64_t> all_w = result.w;
    SGVector<float64_t> all_c = result.c;
    for (int32_t i=0; i<n_classes; i++)
    {
        SGVector<float64_t> w(n_feats);
        for (int32_t j=0; j<n_feats; j++)
            w[j] = all_w(j,i);
        float64_t c = all_c[i];
        CLinearMachine* machine = new CLinearMachine();
        machine->set_w(w);
        machine->set_bias(c);
        m_machines->push_back(machine);
    }
    return true;
}
#endif /* HAVE_EIGEN3 */