GradientDescent.h

/**
 * DESCRIPTION OF THE FILE
 *
 * @author Michal Kravčenko
 * @date 30.7.18 -
 */

#ifndef INC_4NEURO_GRADIENTDESCENT_H
#define INC_4NEURO_GRADIENTDESCENT_H

#include "../settings.h"
#include "../constants.h"
#include "ILearningMethods.h"
#include "../ErrorFunction/ErrorFunctions.h"

namespace lib4neuro {
    class GradientDescent : public ILearningMethods {

    private:

        /**
         *
         */
        double tolerance;
		double max_error;
        /**
         *
         */
        size_t restart_frequency;
		size_t batch;
		size_t iter_max;

        std::vector<double> *optimal_parameters;

        /**
         *
         * @param gamma
         * @param beta
         * @param c
         * @param grad_norm_prev
         * @param grad_norm
         * @param fi
         * @param fim
         */
        virtual void
        eval_step_size_mk(double &gamma, double beta, long double &c,long double grad_norm_prev,long double grad_norm, double fi,
                          double fim);


    public:

        /**
         *
         * @param epsilon
         */
        LIB4NEURO_API GradientDescent(double epsilon = 1e-3, size_t n_to_restart = 100, size_t batch = 0, size_t iter_max = 0, double max_error=1e-3);

        /**
         *
         */
        LIB4NEURO_API ~GradientDescent();

        /**
         *
         * @param ef
         */
        LIB4NEURO_API virtual void optimize(lib4neuro::ErrorFunction &ef);

        /**
         *
         * @return
         */
        LIB4NEURO_API virtual std::vector<double> *get_parameters();


    };

}

#endif //INC_4NEURO_GRADIENTDESCENT_H