StudentsTLikelihood.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (W) 2013 Roman Votyakov
 * Written (W) 2012 Jacob Walker
 * 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 the GPML Toolbox:
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 */

#ifndef CSTUDENTSTLIKELIHOOD_H_
#define CSTUDENTSTLIKELIHOOD_H_

#include <shogun/lib/config.h>


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

namespace shogun
{

class CStudentsTLikelihood: public CLikelihoodModel
{
public:
    CStudentsTLikelihood();

    CStudentsTLikelihood(float64_t sigma, float64_t df);

    virtual ~CStudentsTLikelihood();

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

    float64_t get_sigma() const { return CMath::exp(m_log_sigma); }

    void set_sigma(float64_t sigma)
    {
        REQUIRE(sigma>0.0, "Scale parameter (%f) must be greater than zero\n", sigma);
        m_log_sigma=CMath::log(sigma);
    }

    float64_t get_degrees_freedom() const { return CMath::exp(m_log_df)+1; }

    void set_degrees_freedom(float64_t df)
    {
        REQUIRE(df>1.0, "Number of degrees (%f) of freedom must be greater than one\n", df)
        m_log_df=CMath::log(df-1);
    }

    static CStudentsTLikelihood* obtain_from_generic(CLikelihoodModel* likelihood);

    virtual SGVector<float64_t> get_predictive_means(SGVector<float64_t> mu,
            SGVector<float64_t> s2, const CLabels* lab=NULL) const;

    virtual SGVector<float64_t> get_predictive_variances(SGVector<float64_t> mu,
            SGVector<float64_t> s2, const CLabels* lab=NULL) const;

    virtual ELikelihoodModelType get_model_type() const { return LT_STUDENTST; }

    virtual SGVector<float64_t> get_log_probability_f(const CLabels* lab,
            SGVector<float64_t> func) const;

    virtual SGVector<float64_t> get_log_probability_derivative_f(
            const CLabels* lab, SGVector<float64_t> func, index_t i) const;

    virtual SGVector<float64_t> get_first_derivative(const CLabels* lab,
            SGVector<float64_t> func, const TParameter* param) const;

    virtual SGVector<float64_t> get_second_derivative(const CLabels* lab,
            SGVector<float64_t> func, const TParameter* param) const;

    virtual SGVector<float64_t> get_third_derivative(const CLabels* lab,
            SGVector<float64_t> func, const TParameter* param) const;

    virtual SGVector<float64_t> get_log_zeroth_moments(SGVector<float64_t> mu,
            SGVector<float64_t> s2, const CLabels* lab) const;

    virtual float64_t get_first_moment(SGVector<float64_t> mu,
            SGVector<float64_t> s2, const CLabels* lab, index_t i) const;

    virtual float64_t get_second_moment(SGVector<float64_t> mu,
            SGVector<float64_t> s2, const CLabels* lab, index_t i) const;

    virtual bool supports_regression() const { return true; }

private:
    void init();

    float64_t m_log_sigma;

    float64_t m_log_df;
};
}
#endif /* CSTUDENTSTLIKELIHOOD_H_ */