KRR.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) 2006 Mikio L. Braun
 * Written (W) 1999-2009 Soeren Sonnenburg
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#include "lib/config.h"

#ifdef HAVE_LAPACK
#include "regression/KRR.h"
#include "lib/lapack.h"
#include "lib/Mathematics.h"

using namespace shogun;

CKRR::CKRR()
: CKernelMachine()
{
    alpha=NULL;
    tau=1e-6;
}

CKRR::CKRR(float64_t t, CKernel* k, CLabels* lab)
: CKernelMachine()
{
    tau=t;
    set_labels(lab);
    set_kernel(k);
    alpha=NULL;
}


CKRR::~CKRR()
{
    delete[] alpha;
}

bool CKRR::train(CFeatures* data)
{
    delete[] alpha;

    ASSERT(labels);
    if (data)
    {
        if (labels->get_num_labels() != data->get_num_vectors())
            SG_ERROR("Number of training vectors does not match number of labels\n");
        kernel->init(data, data);
    }
    ASSERT(kernel && kernel->has_features());

    // Get kernel matrix
    int32_t m=0;
    int32_t n=0;
    float64_t *K = kernel->get_kernel_matrix<float64_t>(m, n, NULL);
    ASSERT(K && m>0 && n>0);

    for(int32_t i=0; i < n; i++)
        K[i+i*n]+=tau;

    // Get labels
    int32_t numlabels=0;
    alpha=labels->get_labels(numlabels);
    ASSERT(alpha && numlabels==n);

    clapack_dposv(CblasRowMajor,CblasUpper, n, 1, K, n, alpha, n);

    delete[] K;
    return true;
}

bool CKRR::load(FILE* srcfile)
{
    return false;
}

bool CKRR::save(FILE* dstfile)
{
    return false;
}

CLabels* CKRR::classify()
{
    ASSERT(kernel);

    // Get kernel matrix
    int32_t m=0;
    int32_t n=0;
    float64_t* K=kernel->get_kernel_matrix<float64_t>(m, n, NULL);
    ASSERT(K && m>0 && n>0);
    float64_t* Yh=new float64_t[n];

    // predict
    // K is symmetric, CblasColMajor is same as CblasRowMajor 
    // and used that way in the origin call:
    // dgemv('T', m, n, 1.0, K, m, alpha, 1, 0.0, Yh, 1);
    int m_int = (int) m;
    int n_int = (int) n;
    cblas_dgemv(CblasColMajor, CblasTrans, m_int, n_int, 1.0, (double*) K,
        m_int, (double*) alpha, 1, 0.0, (double*) Yh, 1);

    delete[] K;

    CLabels* output=new CLabels(n);
    output->set_labels(Yh, n);

    delete[] Yh;

    return output;
}

float64_t CKRR::classify_example(int32_t num)
{
    ASSERT(kernel);

    // Get kernel matrix
    int32_t m=0;
    int32_t n=0;
    // TODO: use get_kernel_column instead of computing the whole matrix!
    float64_t* K=kernel->get_kernel_matrix<float64_t>(m, n, NULL);
    ASSERT(K && m>0 && n>0);
    float64_t Yh;

    // predict
    Yh = CMath::dot(K + m*num, alpha, m);

    delete[] K;
    return Yh;
}

#endif