def test_hho_calculate_initial_coefficients():
new_hho = hho.HHO()
E, J = new_hho._calculate_initial_coefficients(1, 10)
assert E[0] != 0
assert J[0] != 0
def test_hho_exploration_phase():
new_hho = hho.HHO()
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_hho._exploration_phase(search_space.agents, search_space.agents[0],
search_space.best_agent)
def test_hho_update():
def square(x):
return np.sum(x**2)
new_hho = hho.HHO()
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_hho.update(search_space, square, 1, 10)
def test_hho_exploitation_phase():
def square(x):
return np.sum(x**2)
assert square(2) == 4
new_hho = hho.HHO()
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_hho._exploitation_phase(1, 1, search_space.agents,
search_space.agents[0],
search_space.best_agent, square)
def test_hho_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_hho = hho.HHO()
search_space = search.SearchSpace(n_agents=10, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_hho.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 hho failed to converge.'
def test_hho_build():
new_hho = hho.HHO()
assert new_hho.built == True