SingleFITCLaplacianInferenceMethodWithLBFGS.h

浏览该文件的文档.

/*
 * 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 Gaussian Process Machine Learning Toolbox
 * http://www.gaussianprocess.org/gpml/code/matlab/doc/
 */
#ifndef CSINGLEFITCLAPLACIANINFERENCEMETHODWITHLBFGS_H
#define CSINGLEFITCLAPLACIANINFERENCEMETHODWITHLBFGS_H

#include <shogun/lib/config.h>

#ifdef HAVE_EIGEN3

#include <shogun/machine/gp/SingleFITCLaplacianInferenceMethod.h>
#include <shogun/optimization/lbfgs/lbfgs.h>


namespace shogun
{

class CSingleFITCLaplacianInferenceMethodWithLBFGS: public CSingleFITCLaplacianInferenceMethod
{
public:
    /* default constructor */
    CSingleFITCLaplacianInferenceMethodWithLBFGS();

    /* constructor
     *
     * @param kernel covariance function
     * @param features features to use in inference
     * @param mean mean function
     * @param labels labels of the features
     * @param model Likelihood model to use
     * @param inducing_features features to use
     */
    CSingleFITCLaplacianInferenceMethodWithLBFGS(CKernel* kernel,
            CFeatures* features,
            CMeanFunction* mean,
            CLabels* labels,
            CLikelihoodModel* model,
            CFeatures* inducing_features
            );

    virtual ~CSingleFITCLaplacianInferenceMethodWithLBFGS();

    /* returns the name of the inference method
     *
     * @return name SingleFITCLaplacianWithLBFGS
     */
    virtual const char* get_name() const
    {return "SingleFITCLaplacianInferenceMethodWithLBFGS";}

    /* set L-BFGS parameters
     * For details please see shogun/optimization/lbfgs/lbfgs.h
     * @param m The number of corrections to approximate the inverse hessian matrix.
     * Default value is 100.
     * @param max_linesearch The maximum number of trials to do line search for each L-BFGS update.
     * Default value is 1000.
     * @param linesearch The line search algorithm.
     * Default value is using the backtracking with the strong Wolfe condition line search
     * @param max_iterations The maximum number of iterations for L-BFGS update.
     * Default value is 1000.
     * @param delta Delta for convergence test based on the change of function value.
     * Default value is 0.
     * @param past Distance for delta-based convergence test.
     * Default value is 0.
     * @param epsilon Epsilon for convergence test based on the change of gradient.
     * Default value is 1e-5
     * @param min_step The minimum step of the line search.
     * The default value is 1e-20
     * @param max_step The maximum step of the line search.
     * The default value is 1e+20
     * @param ftol A parameter used in Armijo condition.
     * Default value is 1e-4
     * @param wolfe A parameter used in curvature condition.
     * Default value is 0.9
     * @param gtol A parameter used in Morethuente linesearch to control the accuracy.
     * Default value is 0.9
     * @param xtol The machine precision for floating-point values.
     * Default value is 1e-16.
     * @param orthantwise_c Coeefficient for the L1 norm of variables.
     * This parameter should be set to zero for standard minimization problems.
     * Setting this parameter to a positive value activates
     * Orthant-Wise Limited-memory Quasi-Newton (OWL-QN) method. Default value is 0.
     * @param orthantwise_start Start index for computing L1 norm of the variables.
     * This parameter is valid only for OWL-QN method. Default value is 0.
     * @param orthantwise_end End index for computing L1 norm of the variables.
     * Default value is 1.
     */
    virtual void set_lbfgs_parameters(int m = 100,
            int max_linesearch = 1000,
            int linesearch = LBFGS_LINESEARCH_BACKTRACKING_STRONG_WOLFE,
            int max_iterations = 1000,
            float64_t delta = 0.0,
            int past = 0,
            float64_t epsilon = 1e-5,
            float64_t min_step = 1e-20,
            float64_t max_step = 1e+20,
            float64_t ftol = 1e-4,
            float64_t wolfe = 0.9,
            float64_t gtol = 0.9,
            float64_t xtol = 1e-16,
            float64_t orthantwise_c = 0.0,
            int orthantwise_start = 0,
            int orthantwise_end = 1);

    /* wheter we use Newton method as rollbak if LBFGS optimizer fails
     *
     * @param enable_newton_if_fail if LBFGS optimizer fails, should we use Newton method.
     */
    virtual void set_newton_method(bool enable_newton_if_fail);
protected:
    /* update alpha using the LBFGS method*/
    virtual void update_alpha();

private:
    /* a parameter used to compute function value and gradient for LBFGS update*/
    SGVector<float64_t> * m_mean_f;

    /* should we enable the original Newton method
     * if the L-BFGS method fails
     * */
    bool m_enable_newton_if_fail;

    /* The number of corrections to approximate the inverse hessian matrix.*/
    int m_m;

    /* The maximum number of trials to do line search for each L-BFGS update.*/
    int m_max_linesearch;

    /* The line search algorithm.*/
    int m_linesearch;

    /* The maximum number of iterations for L-BFGS update.*/
    int m_max_iterations;

    /* Delta for convergence test based on the change of function value.*/
    float64_t m_delta;

    /* Distance for delta-based convergence test.*/
    int m_past;

    /* Epsilon for convergence test based on the change of gradient.*/
    float64_t m_epsilon;

    /* The minimum step of the line search.*/
    float64_t m_min_step;

    /* The maximum step of the line search.*/
    float64_t m_max_step;

    /* A parameter used in Armijo condition.*/
    float64_t m_ftol;

    /* A parameter used in curvature condition.*/
    float64_t m_wolfe;

    /* A parameter used in Morethuente linesearch to control the accuracy.*/
    float64_t m_gtol;

    /* The machine precision for floating-point values.*/
    float64_t m_xtol;

    /* Coeefficient for the L1 norm of variables.*/
    float64_t m_orthantwise_c;

    /* Start index for computing L1 norm of the variables.*/
    int m_orthantwise_start;

    /* End index for computing L1 norm of the variables.*/
    int m_orthantwise_end;

    void init();

    /* helper function is passed to the LBFGS API
     * Note that this function should be static and
     * private.
     * */
    static float64_t evaluate(void *obj,
            const float64_t *alpha,
            float64_t *gradient,
            const int dim,
            const float64_t step);

    /* compute the gradient given the current alpha*/
    void get_gradient_wrt_alpha(float64_t *alpha,
            float64_t *gradient, const int dim);

    /* compute the function value given the current alpha*/
    void get_psi_wrt_alpha(float64_t *alpha,
            const int dim, float64_t &psi);
};

} /* namespace shogun */
#endif /* HAVE_EIGEN3 */
#endif /* CSINGLEFITCLAPLACIANINFERENCEMETHODWITHLBFGS_H */