def test_case2(self):
"""
Case 2. Reference front: [[1.0, 1.0], [2.0, 2.0], front: [[1.0, 1.0]]
Expected result: the distance to the nearest point of the reference front is 0.0
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0], [2.0, 2.0]]))
front = np.array([[1.0, 1.0]])
result = indicator.compute(front)
self.assertEqual(0.0, result)
def test_case5(self):
"""
Case 5. reference front: [[1.0, 1.0], [2.1, 2.1]], front: [[1.5, 1.5]]
Expected result: the distance to the nearest point of the reference front is the euclidean distance
to the nearest point of the reference front ([1.0, 1.0])
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0], [2.1, 2.1]]))
front = np.array([[1.5, 1.5]])
result = indicator.compute(front)
self.assertEqual(np.sqrt(pow(1.0 - 1.5, 2) + pow(1.0 - 1.5, 2)),
result)
self.assertEqual(np.sqrt(pow(2.0 - 1.5, 2) + pow(2.0 - 1.5, 2)),
result)
def test_case6(self):
"""
Case 6. reference front: [[1.0, 1.0], [2.1, 2.1]], front: [[1.5, 1.5], [2.2, 2.2]]
Expected result: the distance to the nearest point of the reference front is the average of the sum of each point
of the front to the nearest point of the reference front
:return:
"""
indicator = GenerationalDistance(np.array([[1.0, 1.0], [2.1, 2.1]]))
front = np.array([[1.5, 1.5], [2.2, 2.2]])
result = indicator.compute(front)
distance_of_first_point = np.sqrt(
pow(1.0 - 1.5, 2) + pow(1.0 - 1.5, 2))
distance_of_second_point = np.sqrt(
pow(2.1 - 2.2, 2) + pow(2.1 - 2.2, 2))
self.assertEqual(
(distance_of_first_point + distance_of_second_point) / 2.0, result)