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/**
* DESCRIPTION OF THE FILE
*
* @author Michal Kravčenko
* @date 19.2.19 -
*/
#include "LearningSequence.h"

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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>();

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}
LearningSequence::~LearningSequence() = default;

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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(std::shared_ptr<LearningMethod> method) {

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this->learning_sequence.push_back( method );
}
void LearningSequence::optimize(lib4neuro::ErrorFunction &ef, std::ofstream *ofs) {
puts("*********************** 11");

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double error = ef.eval();
puts("*********************** 12");

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double the_best_error = error;
int mcycles = this->max_number_of_cycles, cycle_idx = 0;
while( error > this->tol && mcycles != 0){
mcycles--;
cycle_idx++;

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for( auto m: this->learning_sequence ){
puts("*********************** 8");

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m->optimize( ef, ofs );

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error = ef.eval(m->get_parameters());
puts("*********************** 9");

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ef.get_network_instance()->copy_parameter_space(m->get_parameters());

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puts("*********************** 10");

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

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}
if( error <= this->tol ){
ef.get_network_instance()->copy_parameter_space( &this->best_parameters );

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return;
}
}
COUT_DEBUG("Cycle: " << cycle_idx << ", the lowest error: " << the_best_error << std::endl );

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}
ef.get_network_instance()->copy_parameter_space( &this->best_parameters );