def test_boa_update():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_boa = boa.BOA()
new_boa.compile(search_space)
new_boa.update(search_space)
def test_boa_local_movement():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_boa = boa.BOA()
new_boa.compile(search_space)
new_boa._local_movement(search_space.agents[0].position, search_space.agents[1].position,
search_space.agents[2].position, new_boa.fragrance[0], 0.5)
def test_boa_params():
params = {"c": 0.01, "a": 0.1, "p": 0.8}
new_boa = boa.BOA(params=params)
assert new_boa.c == 0.01
assert new_boa.a == 0.1
assert new_boa.p == 0.8
def test_boa_hyperparams():
hyperparams = {'c': 0.01, 'a': 0.1, 'p': 0.8}
new_boa = boa.BOA(hyperparams=hyperparams)
assert new_boa.c == 0.01
assert new_boa.a == 0.1
assert new_boa.p == 0.8
def test_boa_compile():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_boa = boa.BOA()
new_boa.compile(search_space)
try:
new_boa.fragrance = 1
except:
new_boa.fragrance = np.array([1])
assert new_boa.fragrance == np.array([1])
def test_boa_params():
params = {
'c': 0.01,
'a': 0.1,
'p': 0.8
}
new_boa = boa.BOA(params=params)
assert new_boa.c == 0.01
assert new_boa.a == 0.1
assert new_boa.p == 0.8
def test_boa_params_setter():
new_boa = boa.BOA()
try:
new_boa.c = 'a'
except:
new_boa.c = 0.01
try:
new_boa.c = -1
except:
new_boa.c = 0.01
assert new_boa.c == 0.01
try:
new_boa.a = 'b'
except:
new_boa.a = 0.1
try:
new_boa.a = -1
except:
new_boa.a = 0.1
assert new_boa.a == 0.1
try:
new_boa.p = 'c'
except:
new_boa.p = 0.8
try:
new_boa.p = -1
except:
new_boa.p = 0.8
assert new_boa.p == 0.8
def test_boa_params_setter():
new_boa = boa.BOA()
try:
new_boa.c = "a"
except:
new_boa.c = 0.01
try:
new_boa.c = -1
except:
new_boa.c = 0.01
assert new_boa.c == 0.01
try:
new_boa.a = "b"
except:
new_boa.a = 0.1
try:
new_boa.a = -1
except:
new_boa.a = 0.1
assert new_boa.a == 0.1
try:
new_boa.p = "c"
except:
new_boa.p = 0.8
try:
new_boa.p = -1
except:
new_boa.p = 0.8
assert new_boa.p == 0.8
def test_boa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_boa = boa.BOA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_boa.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 boa failed to converge.'
def test_boa_build():
new_boa = boa.BOA()
assert new_boa.built == True
