SMDMinimizer.cpp

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 /* * Copyright (c) The Shogun Machine Learning Toolbox
 * Written (w) 2015 Wu Lin
 * All rights reserved.
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 * modification, are permitted provided that the following conditions are met:
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 *    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
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 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
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#include <shogun/optimization/SMDMinimizer.h>
#include <shogun/base/Parameter.h>
using namespace shogun;

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

SMDMinimizer::~SMDMinimizer()
{
    SG_UNREF(m_mapping_fun);
}

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

float64_t SMDMinimizer::minimize()
{
    REQUIRE(m_mapping_fun, "Mapping function must set\n");
    init_minimization();
    SGVector<float64_t> variable_reference=m_fun->obtain_variable_reference();
    SGVector<float64_t> dual_variable=m_mapping_fun->get_dual_variable(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(dual_variable,grad,learning_rate);
            m_mapping_fun->update_variable(variable_reference,dual_variable);
        }
    }
    float64_t cost=m_fun->get_cost();
    return cost+get_penalty(variable_reference);
}

void SMDMinimizer::init()
{
    m_mapping_fun=NULL;
    SG_ADD((CSGObject **)&m_mapping_fun, "SMDMinimizer__m_mapping_fun",
        "m_mapping_fun in SMDMinimizer", MS_NOT_AVAILABLE);
}

void SMDMinimizer::set_mapping_function(MappingFunction* mapping_fun)
{
    REQUIRE(mapping_fun, "mapping/projection function must be set\n");
    if(m_mapping_fun!=mapping_fun)
    {
        SG_REF(mapping_fun);
        SG_UNREF(m_mapping_fun);
        m_mapping_fun=mapping_fun;
    }
}

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