MultiLaplaceInferenceMethod.h

Go to the documentation of this file.

/*
 * 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.
 *
 * Code adapted from
 * https://gist.github.com/yorkerlin/14ace49f2278f3859614
 * Gaussian Process Machine Learning Toolbox
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 * and
 * GPstuff - Gaussian process models for Bayesian analysis
 * http://becs.aalto.fi/en/research/bayes/gpstuff/
 *
 * The reference pseudo code is the algorithm 3.3 of the GPML textbook
 *
 */

#ifndef CMULTILAPLACEINFERENCEMETHOD_H_
#define CMULTILAPLACEINFERENCEMETHOD_H_

#include <shogun/lib/config.h>

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

namespace shogun
{

class CMultiLaplaceInferenceMethod: public CLaplaceInference
{
public:
    CMultiLaplaceInferenceMethod();

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

    virtual ~CMultiLaplaceInferenceMethod();

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


    virtual EInferenceType get_inference_type() const { return INF_LAPLACE_MULTIPLE; }

    static CMultiLaplaceInferenceMethod* obtain_from_generic(CInference* inference);

    virtual float64_t get_negative_log_marginal_likelihood();

    virtual SGVector<float64_t> get_diagonal_vector();

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

    virtual SGVector<float64_t> get_posterior_mean();

    virtual float64_t get_newton_tolerance() { return m_tolerance; }

    virtual void set_newton_tolerance(float64_t tol) { m_tolerance=tol; }

    virtual int32_t get_newton_iterations() { return m_iter; }

    virtual void set_newton_iterations(int32_t iter) { m_iter=iter; }

    virtual float64_t get_minimization_tolerance() { return m_opt_tolerance; }

    virtual void set_minimization_tolerance(float64_t tol) { m_opt_tolerance=tol; }

    virtual float64_t get_minimization_max() { return m_opt_max; }

    virtual void set_minimization_max(float64_t max) { m_opt_max=max; }

protected:

    virtual void check_members() const;

    virtual void update_alpha();

    virtual void update_chol();

    virtual void update_approx_cov();

    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();

protected:

    SGMatrix<float64_t> m_U;

    float64_t m_nlz;

    virtual float64_t get_derivative_helper(SGMatrix<float64_t> dK);

    virtual void get_dpi_helper();

    float64_t m_tolerance;

    index_t m_iter;

    float64_t m_opt_tolerance;

    float64_t m_opt_max;
};
}
#endif /*  CMULTILAPLACEINFERENCEMETHOD_H_ */