DualVariationalGaussianLikelihood.h

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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
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 * The views and conclusions contained in the software and documentation are those
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 * either expressed or implied, of the Shogun Development Team.
 *
 * the reference paper is
 * Mohammad Emtiyaz Khan, Aleksandr Y. Aravkin, Michael P. Friedlander, Matthias Seeger
 * Fast Dual Variational Inference for Non-Conjugate Latent Gaussian Models. ICML2013
 */

#ifndef _DUALVARIATIONALGAUSSIANLIKELIHOOD_H_
#define _DUALVARIATIONALGAUSSIANLIKELIHOOD_H_

#include <shogun/lib/config.h>

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

namespace shogun
{
class CDualVariationalGaussianLikelihood : public CVariationalGaussianLikelihood
{
public:
    CDualVariationalGaussianLikelihood();

    virtual ~CDualVariationalGaussianLikelihood();

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

    virtual SGVector<float64_t> get_variational_expection();

    virtual SGVector<float64_t> get_variational_first_derivative(const TParameter* param) const;

    virtual bool supports_derivative_wrt_hyperparameter() const;

    virtual SGVector<float64_t> get_first_derivative_wrt_hyperparameter(const TParameter* param) const;

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

    virtual bool dual_parameters_valid() const;

    virtual float64_t adjust_step_wrt_dual_parameter(SGVector<float64_t> direction, const float64_t step) const;

    virtual void set_dual_parameters(SGVector<float64_t> the_lambda,  const CLabels* lab);

    virtual SGVector<float64_t> get_mu_dual_parameter() const=0;

    virtual SGVector<float64_t> get_variance_dual_parameter() const=0;

    virtual float64_t get_dual_upper_bound() const=0;

    virtual float64_t get_dual_lower_bound() const=0;

    virtual bool dual_upper_bound_strict() const=0;

    virtual bool dual_lower_bound_strict() const=0;

    virtual SGVector<float64_t> get_dual_objective_value()=0;

    virtual SGVector<float64_t> get_dual_first_derivative(const TParameter* param) const=0;

    virtual void set_strict_scale(float64_t strict_scale);


    virtual void set_noise_factor(float64_t noise_factor);
protected:

    SGVector<float64_t> m_lambda;

    float64_t m_strict_scale;

    bool m_is_valid;

    virtual void precompute();

    virtual CVariationalGaussianLikelihood* get_variational_likelihood() const;
private:
    void init();

};
}
#endif /* _DUALVARIATIONALGAUSSIANLIKELIHOOD_H_ */