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/**
* 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 "ILearningMethods.h"
#include "../ErrorFunction/ErrorFunctions.h"
#include "GradientDescentBB.h"
namespace lib4neuro {
/**
*
*/
class GradientDescentSingleItem : public ILearningMethods {
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::vector<double> *get_parameters() override;
};
}
#endif //LIB4NEURO_GRADIENTDESCENTSINGLEITEM_H