def do_MLP(X, y, X_test, y_test):
#params = {
# 'hidden_layer_sizes':[(10,10,10),4*(10,),5*(10,),6*(10,),7*(10,),8*(10,),9*(10,),(100,),(10,10,),(10,10,10,)],
# 'activation':['relu','identity'],
# 'solver':['sgd','adam'],
# 'random_state':[42,],
# 'verbose':[3,],
#
#}
#mlp = GridSearchCV(estimator=MLPRegressor(), param_grid=params, scoring='r2')
mlp = MLPRegressor(hidden_layer_sizes=7 * (10, ), random_state=42)
mlp.fit(X, y)
#print(mlp.cv_results_)
#print(mlp.best_params_)
mlp.test_score = mlp.score(X_test, y_test)
print('MLP score:', mlp.test_score)
return mlp
