def test_bsa_params():
params = {"F": 3.0, "mix_rate": 1}
new_bsa = bsa.BSA(params=params)
assert new_bsa.F == 3.0
assert new_bsa.mix_rate == 1
def test_bsa_hyperparams():
hyperparams = {'F': 3.0, 'mix_rate': 1}
new_bsa = bsa.BSA(hyperparams=hyperparams)
assert new_bsa.F == 3.0
assert new_bsa.mix_rate == 1
def test_bsa_params():
params = {'F': 3.0, 'mix_rate': 1}
new_bsa = bsa.BSA(params=params)
assert new_bsa.F == 3.0
assert new_bsa.mix_rate == 1
def test_bsa_permute():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bsa = bsa.BSA()
new_bsa.compile(search_space)
new_bsa._permute(search_space.agents)
def test_bsa_crossover():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bsa = bsa.BSA()
new_bsa.compile(search_space)
trial_agents = new_bsa._mutate(search_space.agents)
new_bsa._crossover(search_space.agents, trial_agents)
def test_bsa_mutate():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bsa = bsa.BSA()
new_bsa.compile(search_space)
trial_agents = new_bsa._mutate(search_space.agents)
assert len(trial_agents) == 10
def test_bsa_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=75,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bsa = bsa.BSA()
new_bsa.compile(search_space)
new_bsa.update(search_space, square)
def test_bsa_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bsa = bsa.BSA()
new_bsa.compile(search_space)
try:
new_bsa.old_agents = 1
except:
new_bsa.old_agents = []
assert new_bsa.old_agents == []
def test_bsa_params_setter():
new_bsa = bsa.BSA()
try:
new_bsa.F = "a"
except:
new_bsa.F = 3.0
try:
new_bsa.mix_rate = "b"
except:
new_bsa.mix_rate = 1
try:
new_bsa.mix_rate = -1
except:
new_bsa.mix_rate = 1
assert new_bsa.mix_rate == 1
def test_bsa_hyperparams_setter():
new_bsa = bsa.BSA()
try:
new_bsa.F = 'a'
except:
new_bsa.F = 3.0
try:
new_bsa.mix_rate = 'b'
except:
new_bsa.mix_rate = 1
try:
new_bsa.mix_rate = -1
except:
new_bsa.mix_rate = 1
assert new_bsa.mix_rate == 1
def test_bsa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_bsa = bsa.BSA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_bsa.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 bsa failed to converge.'
def test_bsa_build():
new_bsa = bsa.BSA()
assert new_bsa.built == True
