def test_csa_hyperparams():
hyperparams = {'fl': 2.0, 'AP': 0.1}
new_csa = csa.CSA(hyperparams=hyperparams)
assert new_csa.fl == 2.0
assert new_csa.AP == 0.1
def test_csa_params():
params = {'fl': 2.0, 'AP': 0.1}
new_csa = csa.CSA(params=params)
assert new_csa.fl == 2.0
assert new_csa.AP == 0.1
def test_csa_update():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_csa = csa.CSA()
new_csa.compile(search_space)
new_csa.update(search_space)
new_csa.AP = 1
new_csa.update(search_space)
def test_csa_evaluate():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_csa = csa.CSA()
new_csa.compile(search_space)
new_csa.evaluate(search_space, square)
def test_csa_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_csa = csa.CSA()
new_csa.compile(search_space)
try:
new_csa.memory = 1
except:
new_csa.memory = np.array([1])
assert new_csa.memory == 1
def test_csa_hyperparams_setter():
new_csa = csa.CSA()
try:
new_csa.fl = 'a'
except:
new_csa.fl = 2.0
try:
new_csa.AP = 'b'
except:
new_csa.AP = 0.1
try:
new_csa.AP = -1
except:
new_csa.AP = 0.1
assert new_csa.AP == 0.1
def test_csa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_csa = csa.CSA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_csa.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 csa failed to converge.'
def test_csa_build():
new_csa = csa.CSA()
assert new_csa.built == True