GradientDescentSingleItem.h

/**
 * DESCRIPTION OF THE FILE
 *
 * @author Michal Kravčenko
 * @date 19.2.19 -
 */

#ifndef LIB4NEURO_GRADIENTDESCENTSINGLEITEM_H
#define LIB4NEURO_GRADIENTDESCENTSINGLEITEM_H


#include "../settings.h"
#include "../constants.h"
#include "LearningMethod.h"
#include "../ErrorFunction/ErrorFunctions.h"
#include "GradientDescentBB.h"

namespace lib4neuro {
    /**
     *
     */
    class GradientDescentSingleItem : public GradientLearningMethod {

    private:

        /**
         * Threshold for the successful ending of the optimization - deviation from minima
         */
        double tolerance;

        /**
         *
         */
        double max_error;

        /**
         * Number of iterations to reset step size to tolerance/10.0
         */
        size_t restart_frequency;

        /**
         *
         */
        size_t batch;

        /**
         *
         */
        size_t iter_max;

        /**
         * Maximal number of iterations - optimization will stop after that, even if not converged
         */
        long long int maximum_niters;

        /**
         * Vector of minima coordinates
         */
        std::vector<double> optimal_parameters;


    protected:

        /**
         * Finds the optimal value of step-length in direction @d from position @x of function @f
         * @param f
         * @param x
         * @param d
         * @param n_elems
         * @return
         */
        virtual double get_optimal_step_size(lib4neuro::ErrorFunction &f, std::vector<double> &x, std::vector<double> &d, size_t n_elems);


    public:

        /**
         * Creates an instance of Gradient Descent Optimizer (i.e. back-propagation)
         * @param epsilon Threshold for the successful ending of the optimization - deviation from minima
         * @param n_to_restart Number of iterations to reset step size to tolerance/10.0
         * @param max_iters Maximal number of iterations - optimization will stop after that, even if not converged
         */
        LIB4NEURO_API explicit GradientDescentSingleItem(double epsilon = 1e-3, size_t n_to_restart = 100, int max_iters = 1000, size_t batch = 0);

        /**
         * Deallocates the instance
         */
        LIB4NEURO_API ~GradientDescentSingleItem();

        /**
         *
         * @param ef
         */
        LIB4NEURO_API void optimize(lib4neuro::ErrorFunction &ef, std::ofstream* ofs = nullptr) override;

        /**
         *
         * @return
         */
        LIB4NEURO_API std::shared_ptr<std::vector<double>> get_parameters() override;
    };

}


#endif //LIB4NEURO_GRADIENTDESCENTSINGLEITEM_H