LinearRidgeRegression.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.
 *
 * Copyright (C) 2012 Soeren Sonnenburg
 */

#include <shogun/lib/config.h>

#ifdef HAVE_LAPACK
#include <shogun/regression/LinearRidgeRegression.h>
#include <shogun/mathematics/lapack.h>
#include <shogun/mathematics/Math.h>
#include <shogun/labels/RegressionLabels.h>

using namespace shogun;

CLinearRidgeRegression::CLinearRidgeRegression()
: CLinearMachine()
{
    init();
}

CLinearRidgeRegression::CLinearRidgeRegression(float64_t tau, CDenseFeatures<float64_t>* data, CLabels* lab)
: CLinearMachine()
{
    init();

    m_tau=tau;
    set_labels(lab);
    set_features(data);
}

void CLinearRidgeRegression::init()
{
    m_tau=1e-6;

    SG_ADD(&m_tau, "tau", "Regularization parameter", MS_AVAILABLE);
}

bool CLinearRidgeRegression::train_machine(CFeatures* data)
{
    if (!m_labels)
        SG_ERROR("No labels set\n");

    if (!data)
        data=features;

    if (!data)
        SG_ERROR("No features set\n");

    if (m_labels->get_num_labels() != data->get_num_vectors())
        SG_ERROR("Number of training vectors does not match number of labels\n");

    if (data->get_feature_class() != C_DENSE)
        SG_ERROR("Expected Dense Features\n");

    if (data->get_feature_type() != F_DREAL)
        SG_ERROR("Expected Real Features\n");

    CDenseFeatures<float64_t>* feats=(CDenseFeatures<float64_t>*) data;
    int32_t num_feat=feats->get_num_features();
    int32_t num_vec=feats->get_num_vectors();

    // Get kernel matrix
    SGMatrix<float64_t> kernel_matrix(num_feat,num_feat);
    SGVector<float64_t> y(num_feat);

    // init
    kernel_matrix.zero();
    y.zero();

    for (int32_t i=0; i<num_feat; i++)
        kernel_matrix.matrix[i+i*num_feat]+=m_tau;

    for (int32_t i=0; i<num_vec; i++)
    {
        SGVector<float64_t> v = feats->get_feature_vector(i);
        ASSERT(v.vlen==num_feat);

        cblas_dger(CblasColMajor, num_feat,num_feat, 1.0, v.vector,1,
                v.vector,1, kernel_matrix.matrix, num_feat);

        cblas_daxpy(num_feat, ((CRegressionLabels*) m_labels)->get_label(i), v.vector, 1, y.vector, 1);

        feats->free_feature_vector(v, i);
    }

    clapack_dposv(CblasRowMajor,CblasUpper, num_feat, 1, kernel_matrix.matrix, num_feat,
            y.vector, num_feat);

    set_w(y);

    return true;
}

bool CLinearRidgeRegression::load(FILE* srcfile)
{
    SG_SET_LOCALE_C;
    SG_RESET_LOCALE;
    return false;
}

bool CLinearRidgeRegression::save(FILE* dstfile)
{
    SG_SET_LOCALE_C;
    SG_RESET_LOCALE;
    return false;
}
#endif