def test_random():
"""Test random method"""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
problem = TSPOpt(5, distances=dists)
rand = problem.random()
assert (len(rand) == 5 and len(set(rand)) == 5)
def test_adjust_probs_all_zero():
"""Test adjust_probs method when all elements in input vector sum to
zero."""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
problem = TSPOpt(5, distances=dists)
probs = np.zeros(5)
assert np.array_equal(problem.adjust_probs(probs), np.zeros(5))
def test_adjust_probs_non_zero():
"""Test adjust_probs method when all elements in input vector sum to
some value other than zero."""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
problem = TSPOpt(5, distances=dists)
probs = np.array([0.1, 0.2, 0, 0, 0.5])
x = np.array([0.125, 0.25, 0, 0, 0.625])
assert np.array_equal(problem.adjust_probs(probs), x)
def test_reproduce_mut1():
"""Test reproduce method when mutation_prob is 1"""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
problem = TSPOpt(5, distances=dists)
father = np.array([0, 1, 2, 3, 4])
mother = np.array([4, 3, 2, 1, 0])
child = problem.reproduce(father, mother, mutation_prob=1)
assert (len(child) == 5 and len(set(child)) == 5)
def test_random_neighbor():
"""Test random_neighbor method"""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
problem = TSPOpt(5, distances=dists)
x = np.array([0, 1, 2, 3, 4])
problem.set_state(x)
neigh = problem.random_neighbor()
abs_diff = np.abs(x - neigh)
abs_diff[abs_diff > 0] = 1
sum_diff = np.sum(abs_diff)
assert (len(neigh) == 5 and sum_diff == 2 and len(set(neigh)) == 5)
def tsp(distances=None, num_of_nodes=None):
'''https://mlrose.readthedocs.io/en/stable/source/tutorial2.html'''
fitness_coordinates = TravellingSales(distances=distances)
problem_fit = TSPOpt(length=num_of_nodes,
fitness_fn=fitness_coordinates,
maximize=False)
best_route, _ = genetic_alg(problem_fit, random_state=2)
return best_route
def generateTSP(seed, number_of_cities, area_width=250, area_height=250):
np.random.seed(seed)
x_coords = np.random.randint(area_width, size=number_of_cities)
y_coords = np.random.randint(area_height, size=number_of_cities)
coords = list(tuple(zip(x_coords, y_coords)))
duplicates = TSPGenerator.list_duplicates_(coords)
while len(duplicates) > 0:
for d in duplicates:
x_coords = np.random.randint(area_width, size=len(d))
y_coords = np.random.randint(area_height, size=len(d))
for i in range(len(d)):
coords[d[i]] = (x_coords[i], y_coords[i])
pass
duplicates = TSPGenerator.list_duplicates_(coords)
distances = TSPGenerator.get_distances(coords, False)
return TSPOpt(coords=coords, distances=distances, maximize=True)
def test_random_mimic():
"""Test random_mimic method"""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
pop = np.array([[1, 0, 3, 2, 4],
[0, 2, 1, 3, 4],
[0, 2, 4, 3, 1],
[4, 1, 3, 2, 0],
[3, 4, 0, 2, 1],
[2, 4, 0, 3, 1]])
problem = TSPOpt(5, distances=dists)
problem.keep_sample = pop
problem.eval_node_probs()
problem.find_sample_order()
rand = problem.random_mimic()
assert (len(rand) == 5 and len(set(rand)) == 5)
def generate(seed, number_of_cities, area_width=250, area_height=250):
np.random.seed(seed)
x_coords = np.random.randint(area_width, size=number_of_cities)
y_coords = np.random.randint(area_height, size=number_of_cities)
coords = list(tuple(zip(x_coords, y_coords)))
duplicates = TSPGenerator.list_duplicates_(coords)
while len(duplicates) > 0:
for d in duplicates:
x_coords = np.random.randint(area_width, size=len(d))
y_coords = np.random.randint(area_height, size=len(d))
for i in range(len(d)):
coords[d[i]] = (x_coords[i], y_coords[i])
pass
duplicates = TSPGenerator.list_duplicates_(coords)
distances = TSPGenerator.get_distances(coords, False)
g = nx.Graph()
for a, b, distance in distances:
g.add_edge(a, b, length=int(round(distance)))
return TSPOpt(coords=coords, distances=distances, maximize=False, source_graph=g)
def test_sample_pop():
"""Test sample_pop method"""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
pop = np.array([[1, 0, 3, 2, 4],
[0, 2, 1, 3, 4],
[0, 2, 4, 3, 1],
[4, 1, 3, 2, 0],
[3, 4, 0, 2, 1],
[2, 4, 0, 3, 1]])
problem = TSPOpt(5, distances=dists)
problem.keep_sample = pop
problem.eval_node_probs()
sample = problem.sample_pop(100)
row_sums = np.sum(sample, axis=1)
assert (np.shape(sample)[0] == 100 and np.shape(sample)[1] == 5
and max(row_sums) == 10 and min(row_sums) == 10)
def test_find_neighbors():
"""Test find_neighbors method"""
dists = [(0, 1, 3), (0, 2, 5), (0, 3, 1), (0, 4, 7), (1, 3, 6),
(4, 1, 9), (2, 3, 8), (2, 4, 2), (3, 2, 8), (3, 4, 4)]
problem = TSPOpt(5, distances=dists)
x = np.array([0, 1, 2, 3, 4])
problem.set_state(x)
problem.find_neighbors()
neigh = np.array([[1, 0, 2, 3, 4],
[2, 1, 0, 3, 4],
[3, 1, 2, 0, 4],
[4, 1, 2, 3, 0],
[0, 2, 1, 3, 4],
[0, 3, 2, 1, 4],
[0, 4, 2, 3, 1],
[0, 1, 3, 2, 4],
[0, 1, 4, 3, 2],
[0, 1, 2, 4, 3]])
assert np.array_equal(np.array(problem.neighbors), neigh)