def test_goa_update():
def square(x):
return np.sum(x**2)
new_goa = goa.GOA()
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_goa.update(search_space, square, 1, 10)
def test_goa_params():
params = {'c_min': 0.00001, 'c_max': 1.0, 'f': 0.5, 'l': 1.5}
new_goa = goa.GOA(params=params)
assert new_goa.c_min == 0.00001
assert new_goa.c_max == 1.0
assert new_goa.f == 0.5
assert new_goa.l == 1.5
def test_goa_params():
params = {"c_min": 0.00001, "c_max": 1.0, "f": 0.5, "l": 1.5}
new_goa = goa.GOA(params=params)
assert new_goa.c_min == 0.00001
assert new_goa.c_max == 1.0
assert new_goa.f == 0.5
assert new_goa.l == 1.5
def test_goa_params_setter():
new_goa = goa.GOA()
try:
new_goa.c_min = "a"
except:
new_goa.c_min = 0.00001
try:
new_goa.c_min = -1
except:
new_goa.c_min = 0.00001
assert new_goa.c_min == 0.00001
try:
new_goa.c_max = "b"
except:
new_goa.c_max = 2.0
try:
new_goa.c_max = 0
except:
new_goa.c_max = 1.0
assert new_goa.c_max == 1.0
try:
new_goa.f = "c"
except:
new_goa.f = 0.5
try:
new_goa.f = -1
except:
new_goa.f = 0.5
assert new_goa.f == 0.5
try:
new_goa.l = "d"
except:
new_goa.l = 1.5
try:
new_goa.l = -1
except:
new_goa.l = 1.5
assert new_goa.l == 1.5
def test_goa_params_setter():
new_goa = goa.GOA()
try:
new_goa.c_min = 'a'
except:
new_goa.c_min = 0.00001
try:
new_goa.c_min = -1
except:
new_goa.c_min = 0.00001
assert new_goa.c_min == 0.00001
try:
new_goa.c_max = 'b'
except:
new_goa.c_max = 2.0
try:
new_goa.c_max = 0
except:
new_goa.c_max = 1.0
assert new_goa.c_max == 1.0
try:
new_goa.f = 'c'
except:
new_goa.f = 0.5
try:
new_goa.f = -1
except:
new_goa.f = 0.5
assert new_goa.f == 0.5
try:
new_goa.l = 'd'
except:
new_goa.l = 1.5
try:
new_goa.l = -1
except:
new_goa.l = 1.5
assert new_goa.l == 1.5
def test_goa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_goa = goa.GOA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_goa.run(search_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm goa failed to converge.'
def test_goa_social_force():
new_goa = goa.GOA()
r = new_goa._social_force(np.array([1, 1, 1]))
assert r[0] == -0.11117088165514633
def test_goa_build():
new_goa = goa.GOA()
assert new_goa.built == True