/**
 * 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;
        this->best_parameters = new std::vector<double>();
    }

    LearningSequence::~LearningSequence() {


    }

    std::vector<double>* LearningSequence::get_parameters() {
        if( this->learning_sequence.size() > 0 ){
            return this->learning_sequence[0]->get_parameters( );
        }
        return nullptr;
    }

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

    void LearningSequence::optimize(lib4neuro::ErrorFunction &ef, std::ofstream *ofs) {

        double error = ef.eval();
        double the_best_error = error;
        int mcycles = this->max_number_of_cycles, cycle_idx = 0;

        while( error > this->tol && mcycles != 0){
            mcycles--;
            cycle_idx++;

            for( auto m: this->learning_sequence ){
                m->optimize( ef, ofs );
                error = ef.eval(m->get_parameters());
                ef.get_network_instance()->copy_parameter_space(m->get_parameters());

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

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