def test_goldstein_price(self):
task = GoldsteinPrice()
# Check batch computation
n_points = 10
X = np.random.rand(n_points, task.n_dims)
X[:, 0] = X[:, 0].dot(task.X_upper[0] - task.X_lower[0]) + task.X_lower[0]
X[:, 1] = X[:, 1].dot(task.X_upper[1] - task.X_lower[1]) + task.X_lower[1]
y = task.evaluate(X)
assert y.shape[0] == n_points
assert y.shape[1] == 1
# Check single computation
X = np.array([np.random.rand(task.n_dims)])
X[:, 0] = X[:, 0].dot(task.X_upper[0] - task.X_lower[0]) + task.X_lower[0]
X[:, 1] = X[:, 1].dot(task.X_upper[1] - task.X_lower[1]) + task.X_lower[1]
y = task.evaluate(X)
assert y.shape[0] == 1
# Check optimas
X = np.array([[0, -1]])
y = task.evaluate(X)
assert np.all(np.round(y) == 3) == True
