MultiGridLearning.cpp

/**
 * DESCRIPTION OF THE FILE
 *
 * @author Michal Kravčenko
 * @date 29.09.19 -
 */

#include <random.hpp>
#include <limits>
#include <mpi.h>

#include "message.h"

#include "MultiGridLearning.h"

namespace lib4neuro {
	MultiGridLearning::MultiGridLearning(
		LearningMethod &inner_trainer,
		double tol,
		double res
	){
		this->inner_method = &inner_trainer;
		this->learning_tolerance = tol;
		this->resolution_coeff = res;
	}

	MultiGridLearning::~MultiGridLearning( ) {}

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

	    DataSet *ds_orig = ef.get_dataset();
        double res = this->resolution_coeff;

		double multi_grid_coeff = this->resolution_coeff;

		std::vector<DataSet*> data_set_hierarchies;
		data_set_hierarchies.push_back( ds_orig );
		DataSet *new_data_set = ds_orig->get_approximated_set( 1e-12, 0 );
		data_set_hierarchies.push_back( new_data_set );

		size_t nref = new_data_set->get_n_elements();
		while(new_data_set->get_n_elements() >= nref){
			res += multi_grid_coeff;
			multi_grid_coeff *= 0.95;
			new_data_set = new_data_set->get_approximated_set( res, 0 );
			data_set_hierarchies.push_back( new_data_set );
		}

		for( size_t i = data_set_hierarchies.size() - 1; i > 0; --i ){
			ef.set_dataset( data_set_hierarchies[ i ] );

//			while( true ){
                this->inner_method->optimize( ef, ofs );
//                if( ef.eval() < this->learning_tolerance ){
//                    break;
//                }
//			}
			delete data_set_hierarchies[ i ];
			COUT_INFO("--------------------------------------------");
		}

//		std::vector<double> output(1);
//        for(auto e : *ds_orig->get_data()) {
//            std::cout << "{";
//            for( size_t j = 0; j < e.first.size(); ++j ){
//                std::cout << e.first[ j ] << " ";
//            }
//            std::cout << "} -> {";
//
//            std::cout << e.second.at(0) << " ";
//            ef.get_nets()[0]->eval_single(e.first, output);
//            std::cout << output.at(0) << "}" << std::endl;
//        }
//
//        for(auto e : *ef.get_dataset()->get_data()) {
//            std::cout << "{";
//            for( size_t j = 0; j < e.first.size(); ++j ){
//                std::cout << e.first[ j ] << " ";
//            }
//            std::cout << "} -> {";
//
//            std::cout << e.second.at(0) << " ";
//            ef.get_nets()[0]->eval_single(e.first, output);
//            std::cout << output.at(0) << "}" << std::endl;
//        }
		ef.set_dataset( ds_orig );
		this->inner_method->optimize( ef, ofs );
	}

}//end of namespace lib4neuro