EPInferenceMethod.h

Go to the documentation of this file.

/*
 * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (W) 2013 Roman Votyakov
 * 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.
 *
 *
 * Based on ideas from GAUSSIAN PROCESS REGRESSION AND CLASSIFICATION Toolbox
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 */
#ifndef _EPINFERENCEMETHOD_H_
#define _EPINFERENCEMETHOD_H_

#include <shogun/lib/config.h>


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

namespace shogun
{

class CEPInferenceMethod : public CInference
{
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"; }

    static CEPInferenceMethod* obtain_from_generic(CInference* inference);

    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();

    virtual void register_minimizer(Minimizer* minimizer);
protected:
    virtual void compute_gradient();

    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 /* _EPINFERENCEMETHOD_H_ */