SparseVGInferenceMethod.h

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/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (W) 2015 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://www.aueb.gr/users/mtitsias/code/varsgp.tar.gz
 */

#ifndef CSPARSEVGINFERENCEMETHOD_H
#define CSPARSEVGINFERENCEMETHOD_H

#include <shogun/lib/config.h>

#ifdef HAVE_EIGEN3

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

namespace shogun
{
class CSparseVGInferenceMethod: public CSingleSparseInferenceBase
{
public:
    CSparseVGInferenceMethod();

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

    virtual ~CSparseVGInferenceMethod();

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

    virtual EInferenceType get_inference_type() const { return INF_KL_SPARSE_REGRESSION; }

    static CSparseVGInferenceMethod* obtain_from_generic(CInferenceMethod* inference);

    virtual float64_t get_negative_log_marginal_likelihood();


    virtual SGVector<float64_t> get_diagonal_vector();

    virtual bool supports_regression() const
    {
        check_members();
        return m_model->supports_regression();
    }

    virtual SGVector<float64_t> get_posterior_mean();

    virtual SGMatrix<float64_t> get_posterior_covariance();

    virtual void update();
protected:
    virtual void check_members() const;

    virtual void update_alpha();

    virtual void update_chol();

    virtual void update_deriv();

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

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

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

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

    virtual float64_t get_derivative_related_cov(SGVector<float64_t> ddiagKi,
        SGMatrix<float64_t> dKuui, SGMatrix<float64_t> dKui);

    virtual void compute_gradient();
protected:
    /* inv_Lm=inv(Lm) where Lm*Lm'=Kmm */
    SGMatrix<float64_t> m_inv_Lm;
    /* Knm*inv_Lm */
    SGMatrix<float64_t> m_Knm_inv_Lm;
    /* invLa=inv(La) where La*La'=sigma2*eye(m)+inv_Lm*Kmn*Knm*inv_Lm' */
    SGMatrix<float64_t> m_inv_La;
    /* yy=(y-meanfun)'*(y-meanfun) */
    float64_t m_yy;
    /* the term used to compute gradient wrt likelihood and marginal likelihood*/
    float64_t m_f3;
    /* square of sigma from Gaussian likelihood*/
    float64_t m_sigma2;
    /* the trace term to compute marginal likelihood*/
    float64_t m_trk;
    /* a matrix used to compute gradients wrt kernel (Kmm)*/
    SGMatrix<float64_t> m_Tmm;
    /* a matrix used to compute gradients wrt kernel (Knm)*/
    SGMatrix<float64_t> m_Tnm;
private:
    void init();
};
}
#endif /* HAVE_EIGEN3 */
#endif /* CSPARSEVGINFERENCEMETHOD_H */