def test_bha_event_horizon():
new_bha = bha.BHA()
search_space = search.SearchSpace(n_agents=20,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bha._event_horizon(search_space.agents, search_space.best_agent, 10)
assert search_space.best_agent.fit != 0
def test_bha_update_position():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_bha = bha.BHA()
search_space = search.SearchSpace(n_agents=20,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
cost = new_bha._update_position(search_space.agents,
search_space.best_agent, new_function)
assert cost != 0
def test_bha_run():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_bha = bha.BHA()
search_space = search.SearchSpace(n_agents=2,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_bha.run(search_space, new_function)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
def test_bha_run():
def square(x):
return np.sum(x)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_bha = bha.BHA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=50,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[5, 5])
history = new_bha.run(search_space, new_function, pre_evaluation_hook=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 bha failed to converge.'
def test_bha_build():
new_bha = bha.BHA()
assert new_bha.built == True