EPInferenceMethod.h

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/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2013 Roman Votyakov
 *
 * Based on ideas from GAUSSIAN PROCESS REGRESSION AND CLASSIFICATION Toolbox
 * Copyright (C) 2005-2013 by Carl Edward Rasmussen & Hannes Nickisch under the
 * FreeBSD License
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 */

#ifndef _EPINFERENCEMETHOD_H_
#define _EPINFERENCEMETHOD_H_

#include <shogun/lib/config.h>

#ifdef HAVE_EIGEN3

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

namespace shogun
{

class CEPInferenceMethod : public CInferenceMethod
{
public:
    CEPInferenceMethod();

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

    virtual ~CEPInferenceMethod();

    virtual EInferenceType get_inference_type() const { return INF_EP; }

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

    virtual float64_t get_negative_log_marginal_likelihood();

    virtual SGVector<float64_t> get_alpha();

    virtual SGMatrix<float64_t> get_cholesky();

    virtual SGVector<float64_t> get_diagonal_vector();

    virtual SGVector<float64_t> get_posterior_mean();

    virtual SGMatrix<float64_t> get_posterior_covariance();

    virtual float64_t get_tolerance() const { return m_tol; }

    virtual void set_tolerance(const float64_t tol) { m_tol=tol; }

    virtual uint32_t get_min_sweep() const { return m_min_sweep; }

    virtual void set_min_sweep(const uint32_t min_sweep) { m_min_sweep=min_sweep; }

    virtual uint32_t get_max_sweep() const { return m_max_sweep; }

    virtual void set_max_sweep(const uint32_t max_sweep) { m_max_sweep=max_sweep; }

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

    virtual void update();

protected:
    virtual void update_alpha();

    virtual void update_chol();

    virtual void update_approx_cov();

    virtual void update_approx_mean();

    virtual void update_negative_ml();

    virtual void update_deriv();

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

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

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

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

private:
    void init();

private:
    SGVector<float64_t> m_mu;

    SGMatrix<float64_t> m_Sigma;

    float64_t m_nlZ;

    SGVector<float64_t> m_tnu;

    SGVector<float64_t> m_ttau;

    SGVector<float64_t> m_sttau;

    float64_t m_tol;

    uint32_t m_min_sweep;

    uint32_t m_max_sweep;

    SGMatrix<float64_t> m_F;
};
}
#endif /* HAVE_EIGEN3 */
#endif /* _EPINFERENCEMETHOD_H_ */