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

#include "LearningSequence.h"
#include "../message.h"

namespace lib4neuro {

    LearningSequence::LearningSequence( double tolerance, int max_n_cycles ){
        this->tol = tolerance;
        this->max_number_of_cycles = max_n_cycles;
    }

    LearningSequence::~LearningSequence() = default;

    void LearningSequence::add_learning_method(std::shared_ptr<LearningMethod> method) {
        this->learning_sequence.push_back( method );
    }

    void LearningSequence::optimize(lib4neuro::ErrorFunction &ef, std::ofstream *ofs) {
        double error = ef.eval();
        this->optimal_parameters = ef.get_parameters();
        double the_best_error = error;
        int mcycles = this->max_number_of_cycles, cycle_idx = 0;

        std::vector<double> params;
        while( error > this->tol && mcycles != 0){
            mcycles--;
            cycle_idx++;

            for(auto m: this->learning_sequence ){
                m->optimize( ef, ofs );

                //TODO do NOT copy vectors if not needed
                params = *m->get_parameters();
                error = ef.eval(&params);

                ef.get_network_instance()->copy_parameter_space(&params);

                if( error < the_best_error ){
                    the_best_error = error;
                    this->optimal_parameters = ef.get_parameters();
                }

                if( error <= this->tol ){
                    ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
                    return;
                }
            }
            COUT_DEBUG("Cycle: " << cycle_idx << ", the lowest error: " << the_best_error << std::endl );
        }
        ef.get_network_instance()->copy_parameter_space( &this->optimal_parameters );
    }
}