def test_default_update_formatting():
init_state = list(cities.keys())
tsp = TravellingSalesmanProblem(distance_matrix, initial_state=init_state)
# fix the start time and patch time.time() to give predictable Elapsed and Remaining times
tsp.start = 1.0
time.time = lambda: 9.0
# for step=0, the output should be column headers followed by partial data
sys.stderr = StringIO()
tsp.default_update(0, 1, 2, 3, 4)
output = sys.stderr.getvalue().split('\n')
assert 3 == len(output)
assert ' Temperature Energy Accept Improve Elapsed Remaining' == output[
1]
assert '\r 1.00000 2.00 0:00:08 ' == output[
2]
# when step>0, default_update should use \r to overwrite the previous data
sys.stderr = StringIO()
tsp.default_update(10, 1, 2, 3, 4)
output = sys.stderr.getvalue().split('\n')
assert 1 == len(output)
assert '\r 1.00000 2.00 300.00% 400.00% 0:00:08 11:06:32' == output[
0]
def test_load_state_init(tmpdir):
# initial state, a randomly-ordered itinerary
init_state = list(cities.keys())
random.shuffle(init_state)
tsp = TravellingSalesmanProblem(distance_matrix, initial_state=init_state)
tsp.copy_strategy = "slice"
statefile = str(tmpdir.join("state.pickle"))
tsp.save_state(fname=statefile)
tsp2 = TravellingSalesmanProblem(distance_matrix, load_state=statefile)
assert tsp.state == tsp2.state
def test_tsp_example():
# initial state, a randomly-ordered itinerary
init_state = list(cities.keys())
random.shuffle(init_state)
tsp = TravellingSalesmanProblem(init_state, distance_matrix)
# since our state is just a list, slice is the fastest way to copy
tsp.copy_strategy = "slice"
tsp.steps = 50000
state, e = tsp.anneal()
while state[0] != 'New York City':
state = state[1:] + state[:1] # rotate NYC to start
assert len(state) == len(cities)
def test_tsp_example():
# initial state, a randomly-ordered itinerary
init_state = list(cities.keys())
random.shuffle(init_state)
tsp = TravellingSalesmanProblem(distance_matrix, initial_state=init_state)
# since our state is just a list, slice is the fastest way to copy
tsp.copy_strategy = "slice"
tsp.steps = 50000
state, e = tsp.anneal()
while state[0] != 'New York City':
state = state[1:] + state[:1] # rotate NYC to start
assert len(state) == len(cities)
def test_auto():
# initial state, a randomly-ordered itinerary
init_state = list(cities.keys())
random.shuffle(init_state)
tsp = TravellingSalesmanProblem(init_state, distance_matrix)
# since our state is just a list, slice is the fastest way to copy
tsp.copy_strategy = "slice"
auto_schedule = tsp.auto(minutes=0.05)
tsp.set_schedule(auto_schedule)
assert tsp.Tmax == auto_schedule['tmax']
assert tsp.Tmin == auto_schedule['tmin']
assert tsp.steps == auto_schedule['steps']
assert tsp.updates == auto_schedule['updates']
def test_auto():
# initial state, a randomly-ordered itinerary
init_state = list(cities.keys())
random.shuffle(init_state)
tsp = TravellingSalesmanProblem(distance_matrix, initial_state=init_state)
# since our state is just a list, slice is the fastest way to copy
tsp.copy_strategy = "slice"
auto_schedule = tsp.auto(minutes=0.05)
tsp.set_schedule(auto_schedule)
assert tsp.Tmax == auto_schedule['tmax']
assert tsp.Tmin == auto_schedule['tmin']
assert tsp.steps == auto_schedule['steps']
assert tsp.updates == auto_schedule['updates']