def test_sos_update():
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_sos = sos.SOS()
new_sos.update(search_space, square)
def test_sos_parasitism():
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_sos = sos.SOS()
new_sos._parasitism(search_space.agents[0], search_space.agents[1], square)
def test_sos_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_sos = sos.SOS()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_sos.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 sos failed to converge.'
def test_sos_build():
new_sos = sos.SOS()
assert new_sos.built == True