def test_woa_params_setter():
new_woa = woa.WOA()
try:
new_woa.b = 'a'
except:
new_woa.b = 1
def test_woa_update():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_woa = woa.WOA()
new_woa.update(search_space, 1, 10)
def test_woa_generate_random_agent():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_woa = woa.WOA()
agent = new_woa._generate_random_agent(search_space.agents[0])
assert type(agent).__name__ == 'Agent'
def test_woa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_woa = woa.WOA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_woa.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 woa failed to converge.'
def test_woa_params():
params = {'b': 1}
new_woa = woa.WOA(params=params)
assert new_woa.b == 1
def test_woa_build():
new_woa = woa.WOA()
assert new_woa.built == True
def test_woa_hyperparams():
hyperparams = {'b': 1}
new_woa = woa.WOA(hyperparams=hyperparams)
assert new_woa.b == 1