void NextEpoch(vector<dna>& dnas)
{
size_t cnt = dnas.size();
#pragma omp parallel
#pragma omp for
for (short it = 0; it < cnt; ++it)
{
FANN::neural_net net;
net.create_standard(3, data.num_input_train_data(), dnas[it].numberOfHidden, data.num_output_train_data());
net.set_learning_rate(dnas[it].learningRate);
net.set_learning_momentum(dnas[it].momentum);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
net.set_activation_function_hidden(FANN::LINEAR);
net.set_activation_function_output(FANN::LINEAR);
net.set_callback(print_fake, NULL);
net.set_error_log(NULL);
net.train_on_data(data, dnas[it].maxIteration,
-1, dnas[it].desiredError);
dnas[it].fitness = (1. - net.test_data(testdata)) ;
}
}
void PrintBest(vector<dna>& dnas, int gen)
{
cout << "Best gen : "<< gen << "----------" << endl;
for (int i = 0; i < gen; ++i)
{
cout << endl;
cout << "-********************-" << endl;
cout << "fitness" << dnas[i].fitness << endl;
cout << "learningRate" << dnas[i].learningRate << endl;
cout << "momentum" << dnas[i].momentum << endl;
cout << "numberOfHidden" << dnas[i].numberOfHidden << endl;
cout << "maxIteration" << dnas[i].maxIteration << endl;
cout << "desiredError" << dnas[i].desiredError<< endl;
cout << "-********************-" << endl;
cout << endl;
}
FANN::neural_net net;
net.create_standard(3, data.num_input_train_data(), dnas[0].numberOfHidden, data.num_output_train_data());
net.set_learning_rate(dnas[0].learningRate);
net.set_learning_momentum(dnas[0].momentum);
net.set_activation_steepness_hidden(1.0);
net.set_activation_steepness_output(1.0);
net.set_activation_function_hidden(FANN::SIGMOID);
net.set_activation_function_output(FANN::SIGMOID);
ofstream file("ErrorHistory/" + file + ".csv");
net.set_callback(print_callback, &file);
net.set_error_log(NULL);
net.train_on_data(data, dnas[0].maxIteration,
1, dnas[0].desiredError);
cout << "----------------------" << endl;
}
