StudentsTVGLikelihood.cpp

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2014 Wu Lin
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions are met:
 *
 * 1. Redistributions of source code must retain the above copyright notice, this
 *    list of conditions and the following disclaimer.
 * 2. Redistributions in binary form must reproduce the above copyright notice,
 *    this list of conditions and the following disclaimer in the documentation
 *    and/or other materials provided with the distribution.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
 * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 *
 * The views and conclusions contained in the software and documentation are those
 * of the authors and should not be interpreted as representing official policies,
 * either expressed or implied, of the Shogun Development Team.
 *
 * Code adapted from
 * http://hannes.nickisch.org/code/approxXX.tar.gz
 * and the reference paper is
 * Nickisch, Hannes, and Carl Edward Rasmussen.
 * "Approximations for Binary Gaussian Process Classification."
 * Journal of Machine Learning Research 9.10 (2008).
 *
 * This code specifically adapted from function in approxKL.m
 */

#include <shogun/machine/gp/StudentsTVGLikelihood.h>

#include <shogun/machine/gp/StudentsTLikelihood.h>

using namespace Eigen;

namespace shogun
{

CStudentsTVGLikelihood::CStudentsTVGLikelihood()
    : CNumericalVGLikelihood()
{
    m_log_sigma = 0.0;
    m_log_df = CMath::log(2.0);
    init();
}

CStudentsTVGLikelihood::CStudentsTVGLikelihood(float64_t sigma, float64_t df)
    : CNumericalVGLikelihood()
{
    REQUIRE(sigma>0.0, "Scale parameter (%f) must be greater than zero\n", sigma);
    REQUIRE(df>1.0, "Number of degrees of freedom (%f) must be greater than one\n", df);

    m_log_sigma=CMath::log(sigma);
    m_log_df=CMath::log(df-1.0);
    init();
}

CStudentsTVGLikelihood::~CStudentsTVGLikelihood()
{
}

void CStudentsTVGLikelihood::init_likelihood()
{
    set_likelihood(new CStudentsTLikelihood(CMath::exp(m_log_sigma), CMath::exp(m_log_df)+1.0));
}

void CStudentsTVGLikelihood::init()
{
    init_likelihood();
    SG_ADD(&m_log_df, "log_df", "Degrees of freedom in log domain", MS_AVAILABLE, GRADIENT_AVAILABLE);
    SG_ADD(&m_log_sigma, "log_sigma", "Scale parameter in log domain", MS_AVAILABLE, GRADIENT_AVAILABLE);
}

} /* namespace shogun */