SGDMinimizer.cpp

Go to the documentation of this file.

 /*
 * 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.
 *
 */
#include <shogun/optimization/SGDMinimizer.h>
#include <shogun/optimization/GradientDescendUpdater.h>
#include <shogun/lib/config.h>
using namespace shogun;

SGDMinimizer::SGDMinimizer()
    :FirstOrderStochasticMinimizer()
{
    init();
}

SGDMinimizer::~SGDMinimizer()
{
}

SGDMinimizer::SGDMinimizer(FirstOrderStochasticCostFunction *fun)
    :FirstOrderStochasticMinimizer(fun)
{
    init();
}

float64_t SGDMinimizer::minimize()
{
    init_minimization();

    SGVector<float64_t> variable_reference=m_fun->obtain_variable_reference();
    FirstOrderStochasticCostFunction *fun=dynamic_cast<FirstOrderStochasticCostFunction *>(m_fun);
    REQUIRE(fun,"the cost function must be a stochastic cost function\n");
    for(;m_cur_passes<m_num_passes;m_cur_passes++)
    {
        fun->begin_sample();
        while(fun->next_sample())
        {
            m_iter_counter++;
            float64_t learning_rate=1.0;
            if(m_learning_rate)
                learning_rate=m_learning_rate->get_learning_rate(m_iter_counter);
            SGVector<float64_t> grad=m_fun->get_gradient();
            update_gradient(grad,variable_reference);
            m_gradient_updater->update_variable(variable_reference,grad,learning_rate);

            do_proximal_operation(variable_reference);
        }
    }
    float64_t cost=m_fun->get_cost();
    return cost+get_penalty(variable_reference);
}

void SGDMinimizer::init()
{
}

void SGDMinimizer::init_minimization()
{
    FirstOrderStochasticMinimizer::init_minimization();
}