def test_gpr_mse_is_small():
    x_train = np.random.rand(1000, 2)
    y_train = np.multiply(x_train, x_train).mean(axis=(- 1))
    x_test = np.random.rand(1000, 2)
    y_test = np.multiply(x_test, x_test).mean(axis=(- 1))
    gpr = bo_module.GaussianProcessRegressor(alpha=0.0001, seed=3)
    gpr.fit(x_train, y_train)
    (y_predict_mean, y_predict_std) = gpr.predict(x_test)
    assert (((y_predict_mean - y_test) ** 2).mean(axis=0) < 1e-08)
    assert (y_predict_std.shape == (1000,))
def test_gpr_mse_is_small():
    x_train = np.random.rand(1000, 2)
    y_train = np.multiply(x_train, x_train).mean(axis=(- 1))
    x_test = np.random.rand(1000, 2)
    y_test = np.multiply(x_test, x_test).mean(axis=(- 1))
    gpr = bo_module.GaussianProcessRegressor(alpha=0.0001, seed=3)
    gpr.fit(x_train, y_train)
    (y_predict_mean, y_predict_std) = gpr.predict(x_test)
    assert (((y_predict_mean - y_test) ** 2).mean(axis=0) < 1e-08)
    assert (y_predict_std.shape == (1000,))