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
