def test_bh_event_horizon():
new_bh = bh.BH()
search_space = search.SearchSpace(n_agents=20, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
new_bh._event_horizon(search_space.agents, search_space.best_agent, 10)
assert search_space.best_agent.fit != 0
def test_bh_update():
def square(x):
return np.sum(x**2)
new_bh = bh.BH()
search_space = search.SearchSpace(n_agents=20,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_bh.update(search_space, square)
def test_bh_update_position():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_bh = bh.BH()
search_space = search.SearchSpace(n_agents=20, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
cost = new_bh._update_position(
search_space.agents, search_space.best_agent, new_function)
assert cost != 0
def test_bh_run():
def square(x):
return np.sum(x)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_bh = bh.BH()
search_space = search.SearchSpace(n_agents=10, n_iterations=50,
n_variables=2, lower_bound=[0, 0],
upper_bound=[5, 5])
history = new_bh.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 bh failed to converge.'
def test_bh_build():
new_bh = bh.BH()
assert new_bh.built == True