from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score


def random_state_fit(i, x_train, y_train, x_test, y_test, func):
    mlp = MLPClassifier(random_state=i, max_iter=4000, n_iter_no_change=10,
                        activation=func, alpha=0.01, hidden_layer_sizes=[100, 100])
    mlp.fit(x_train, y_train)
    predictions = mlp.predict(x_test)
    acc_mlp = accuracy_score(y_test, predictions)
    print(f"Func {func} with accuracy {acc_mlp} \n")
    return acc_mlp