class TestTabuSearch(unittest.TestCase):
def setUp(self):
self.flow = [
[0, 1, 2],
[4, 0, 5],
[7, 2, 0]]
self.distance = [
[0, 5, 2],
[1, 0, 1],
[6, 2, 0]]
self.i = Instance(None, self.distance, self.flow)
self.e = E(self.i)
self.ts = TabuSearch()
self.startpoint = Solution((0, 1, 2))
def test_filter_moves(self):
moves = [(1, 2),
(1, 3),
(2, 3)]
tabu = defaultdict(int, {(1, 2): 1, (2, 3): 1})
expected = [(1, 3)]
actual = list(self.ts._get_filtered_moves(moves, tabu=tabu))
self.assertEqual(expected, actual)
def test_filter_moves_with_aspiration(self):
self.i = Instance(None, self.distance, self.flow)
self.e = E(self.i)
self.startpoint = Solution((2, 1, 0))
moves = [(0, 1),
(0, 2),
(1, 2)]
tabu = defaultdict(int, {(0, 1): 1, (1, 2): 2, (0, 2): 3})
expected = [(0, 1),
(0, 2),
(1, 2)]
actual = list(self.ts._get_filtered_moves(moves, e=self.e,
current=(self.startpoint, self.e.evaluate(self.startpoint)),
tabu=tabu))
self.assertEqual(expected, actual)
def test_decrease_tabu_penalty(self):
tabu = defaultdict(int, {(1, 2): 2, (2, 3): 1})
expected = [(1, 2)]
self.ts._decrease_tabu_penalty(tabu)
actual = tabu.keys()
self.assertEqual(expected, actual)
def test_select_best_move(self):
current = self.startpoint
moves = [(0, 1),
(0, 2),
(1, 2)]
expected = (0, 2)
actual = self.ts._select_best_move(current, moves, self.e)
self.assertEqual(expected, actual)
def test_select_best_moves(self):
current = self.startpoint
moves = [(0, 1),
(0, 2),
(1, 2)]
expected = [(0, 2),
(1, 2)]
actual = self.ts._select_best_moves(current, moves, self.e, 2)
self.assertEqual(expected, actual)
def test_with_startpoint(self):
expected = (2, 1, 0)
actual = self.ts.solve(self.i, self.startpoint).sequence
self.assertEqual(expected, actual)
