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/**
* DESCRIPTION OF THE FILE
*
* @author Michal Kravčenko
* @date 30.7.18 -
*/
#include <random.hpp>
#include <limits>
#include "message.h"
#include "LazyLearning.h"
namespace lib4neuro {
LazyLearning::LazyLearning(
LearningMethod &inner_trainer,
double tol
){
this->inner_method = &inner_trainer;
this->tolerance = tol;
}
LazyLearning::~LazyLearning( ) {}
void LazyLearning::optimize(
lib4neuro::ErrorFunction& ef,
std::ofstream* ofs
) {
std::vector<size_t> subset_indices;
std::vector<bool> active_subset;
std::vector<float> entry_errors;
while( true ){
ef.divide_data_worst_subset( subset_indices, active_subset, entry_errors );
/* errors of the active subset */
float subset_error_min = std::numeric_limits<float>::max();
float subset_error_max = 0.0;
float subset_error_total = 0.0;
size_t new_subset_index = subset_indices[subset_indices.size() - 1];
float new_subset_entry_error = entry_errors[new_subset_index];
float new_subset_entry_error_min = 0.0;
float new_subset_entry_error_max = 0.0;
/* errors of the elements not considered */
float shelved_error_min = subset_error_min;
float shelved_error_max = 0.0;
float shelved_error_total = 0.0;
/* processing of the errors */
for( size_t i = 0; i < entry_errors.size(); ++i){
if( active_subset[ i ] ){
if( i == new_subset_index ){
}
else{
subset_error_total += entry_errors[ i ];
subset_error_max = std::max(subset_error_max, entry_errors[ i ] );
subset_error_min = std::min(subset_error_min, entry_errors[ i ] );
}
}
else{
shelved_error_total += entry_errors[ i ];
shelved_error_max = std::max(shelved_error_max, entry_errors[ i ] );
shelved_error_min = std::min(shelved_error_min, entry_errors[ i ] );
}
}
int nactive_set = subset_indices.size();
int nshelved_set = active_subset.size() - subset_indices.size();
MPI_Allreduce( MPI_IN_PLACE, &nactive_set, 1, MPI_INT, MPI_SUM, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &subset_error_total, 1, MPI_FLOAT, MPI_SUM, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &subset_error_min, 1, MPI_FLOAT, MPI_MIN, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &subset_error_max, 1, MPI_FLOAT, MPI_MAX, lib4neuro::mpi_active_comm );
MPI_Allreduce( &new_subset_entry_error, &new_subset_entry_error_min, 1, MPI_FLOAT, MPI_MIN, lib4neuro::mpi_active_comm );
MPI_Allreduce( &new_subset_entry_error, &new_subset_entry_error_max, 1, MPI_FLOAT, MPI_MAX, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &nshelved_set, 1, MPI_INT, MPI_SUM, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &shelved_error_total, 1, MPI_FLOAT, MPI_SUM, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &shelved_error_min, 1, MPI_FLOAT, MPI_MIN, lib4neuro::mpi_active_comm );
MPI_Allreduce( MPI_IN_PLACE, &shelved_error_max, 1, MPI_FLOAT, MPI_MAX, lib4neuro::mpi_active_comm );
COUT_INFO( "[" << nactive_set << "] subset error: " << subset_error_total << ", in range: " << subset_error_min << " - " << subset_error_max << ", new entry errors: " << new_subset_entry_error_min << " - " << new_subset_entry_error_max );
COUT_INFO( "[" << nactive_set << "] new entry error: " << new_subset_entry_error_min << " - " << new_subset_entry_error_max );
COUT_INFO( "[" << nshelved_set << "] remaining error: " << shelved_error_total << ", in range: " << shelved_error_min << " - " << shelved_error_max << std::endl );
if( shelved_error_max < this->tolerance && subset_error_max < this->tolerance && new_subset_entry_error < this->tolerance ){
break;
}
this->inner_method->optimize( ef, ofs );
double sub_error_after = ef.eval( );
while( sub_error_after > this->tolerance ){
this->inner_method->optimize( ef, ofs );
sub_error_after = ef.eval( );
}
ef.return_full_data_set_for_training( );
COUT_INFO( "------------------------" );
}
}
}//end of namespace lib4neuro