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
class EnvGoldsteinPrice(BaseTask):
def __init__(self):
self.goldstein_price = GoldsteinPrice()
X_lower = np.concatenate((self.goldstein_price.original_X_lower, np.array([0])))
X_upper = np.concatenate((self.goldstein_price.original_X_upper, np.array([1])))
self.is_env = np.array([0, 0, 1])
super(EnvGoldsteinPrice, self).__init__(X_lower, X_upper)
def objective_function(self, x):
y = self.goldstein_price.objective_function(x[:, :-1]) * np.exp(-(x[0, -1] - 1))
return y
def objective_function_test(self, x):
return self.goldstein_price.objective_function(x[:, :-1])
def __init__(self):
self.goldstein_price = GoldsteinPrice()
X_lower = np.concatenate((self.goldstein_price.original_X_lower, np.array([0])))
X_upper = np.concatenate((self.goldstein_price.original_X_upper, np.array([1])))
self.is_env = np.array([0, 0, 1])
super(EnvGoldsteinPrice, self).__init__(X_lower, X_upper)
