AdaGradUpdater.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.
 *
 */

#ifndef ADAGRADUPDATER_H
#define ADAGRADUPDATER_H
#include <shogun/optimization/DescendUpdaterWithCorrection.h>
#include <shogun/optimization/LearningRate.h>
namespace shogun
{
 class AdaGradUpdater: public DescendUpdaterWithCorrection
{
public:
    /* Constructor */
    AdaGradUpdater();

    AdaGradUpdater(float64_t learning_rate,float64_t epsilon);

    /* Destructor */
    virtual ~AdaGradUpdater();

    virtual void set_learning_rate(float64_t learning_rate);

    virtual void set_epsilon(float64_t epsilon);

    virtual void update_context(CMinimizerContext* context);

    virtual void load_from_context(CMinimizerContext* context);

    virtual void update_variable(SGVector<float64_t> variable_reference,
        SGVector<float64_t> raw_negative_descend_direction,
        float64_t learning_rate);

protected:
    virtual float64_t get_negative_descend_direction(float64_t variable,
        float64_t gradient, index_t idx, float64_t learning_rate);

    float64_t m_build_in_learning_rate;

    float64_t m_epsilon;

    SGVector<float64_t> m_gradient_accuracy;
private:
    void init();
};

}
#endif