def fit(self, verbose=False): data = self.data # split into train and test data train = data.T[0:150000] test = data.T[150000:] x_train = train.T[0:6].T y_train = train.T[6].T x_test = test.T[0:6].T y_test = test.T[6].T # train the model mlp = MLPRegressor() mlp.hidden_layer_sizes = self.hidden_layer_sizes mlp.activation = self.activation mlp.solver = self.solver mlp.alpha = self.alpha mlp.fit(x_train, y_train) self.mlp = mlp # predict y_train_p = mlp.predict(x_train) y_test_p = mlp.predict(x_test) # print training and test error train_error = np.mean((y_train - y_train_p)**2) test_error = np.mean((y_test - y_test_p)**2) if verbose: print(mlp.get_params()) print("average training error is : %f" % (train_error)) print("average test error is : %f" % (test_error)) return train_error, test_error