KLDualInferenceMethod.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
 * 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.
 *
 * 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 _KLDUALINFERENCEMETHOD_H_
#define _KLDUALINFERENCEMETHOD_H_

#include <shogun/lib/config.h>

#ifdef HAVE_EIGEN3
#include <shogun/machine/gp/KLInferenceMethod.h>
#include <shogun/machine/gp/DualVariationalGaussianLikelihood.h>

namespace shogun
{

class CKLDualInferenceMethod: public CKLInferenceMethod
{
public:
    CKLDualInferenceMethod();

    CKLDualInferenceMethod(CKernel* kernel, CFeatures* features,
            CMeanFunction* mean, CLabels* labels, CLikelihoodModel* model);

    virtual ~CKLDualInferenceMethod();

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

    virtual EInferenceType get_inference_type() const { return INF_KL_DUAL; }

    static CKLDualInferenceMethod * obtain_from_generic(CInferenceMethod* inference);

    virtual SGVector<float64_t> get_alpha();

    virtual SGVector<float64_t> get_diagonal_vector();

    void set_model(CLikelihoodModel* mod);
protected:

    virtual void get_gradient_of_nlml_wrt_parameters(SGVector<float64_t> gradient){};

    virtual CDualVariationalGaussianLikelihood* get_dual_variational_likelihood() const;

    virtual void check_dual_inference(CLikelihoodModel* mod) const;

    virtual void update_approx_cov();

    virtual void update_alpha();

    virtual void update_chol();

    virtual void update_deriv();

    virtual float64_t get_negative_log_marginal_likelihood_helper();

    virtual bool lbfgs_precompute();

    virtual float64_t get_derivative_related_cov(SGMatrix<float64_t> dK);

    virtual float64_t lbfgs_optimization();

    virtual float64_t get_dual_objective_wrt_parameters();

    virtual void get_gradient_of_dual_objective_wrt_parameters(SGVector<float64_t> gradient);

private:
    void init();

    SGVector<float64_t> m_sW;

    SGVector<float64_t> m_W;

    SGVector<float64_t> m_dv;

    SGVector<float64_t> m_df;

    SGMatrix<float64_t> m_V;
    bool m_is_dual_valid;

    float64_t get_nlml_wrapper(SGVector<float64_t> alpha, SGVector<float64_t> mu, SGMatrix<float64_t> L);

    static float64_t evaluate(void *obj,
        const float64_t *parameters,
        float64_t *gradient, const int dim,
        const float64_t step);

    static float64_t adjust_step(void *obj,
        const float64_t *parameters,
        const float64_t *direction,
        const int dim, const float64_t step);

};
}
#endif /* HAVE_EIGEN3 */
#endif /* _KLDUALINFERENCEMETHOD_H_ */