type_learning_rate, alfa, v_lambda, fun,
weight, early_stopping, num_training)
modello = Regression.startexecution_k_fold()
num_training = Regression.num_training
#take the best model and insert it
ensamble.insert_model(modello[0])
#devolopment set prima del retraining
ensamble.write_result(fn.top_result_ML_cup)
#test set prima del retraining
ensamble.output_average(Regression.test_set, fn.top_result_test)
#retraing
top_models, mee_result = Regression.retraining(ensamble.getNN())
print("Risultati MEE sul devolpment set dopo il retraining:", mee_result)
ensamble = Ensemble(top_models, 8)
#compute average of development set and save it on top_result_test_retraing
output, mse, mee = ensamble.output_average(Regression.devolopment_set,
fn.top_result_test_retraing)
print("MEE ENSEMBLE devolopment_set:", mee)
#compute average of test_set and save it on top_result_test_retraing
output, mse, mee = ensamble.output_average(Regression.test_set,
fn.top_result_test_retraing)
print("MEE ENSEMBLE test_set:", mee)
#compute blind test result and save it in blind_test file
