def test_eho_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_eho = eho.EHO()
try:
search_space = search.SearchSpace(n_agents=5,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_eho.run(search_space, new_function, pre_evaluation=hook)
except:
search_space = search.SearchSpace(n_agents=20,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_eho.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 eho failed to converge.'
def test_eho_separating_operator():
search_space = search.SearchSpace(
n_agents=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_eho = eho.EHO()
new_eho.compile(search_space)
new_eho._separating_operator(search_space.agents)
def test_eho_hyperparams():
hyperparams = {'alpha': 0.5, 'beta': 0.1, 'n_clans': 10}
new_eho = eho.EHO(hyperparams=hyperparams)
assert new_eho.alpha == 0.5
assert new_eho.beta == 0.1
assert new_eho.n_clans == 10
def test_eho_params():
params = {"alpha": 0.5, "beta": 0.1, "n_clans": 10}
new_eho = eho.EHO(params=params)
assert new_eho.alpha == 0.5
assert new_eho.beta == 0.1
assert new_eho.n_clans == 10
def test_eho_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(
n_agents=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_eho = eho.EHO()
new_eho.compile(search_space)
new_eho.update(search_space, square)
def test_eho_get_agents_from_clan():
search_space = search.SearchSpace(n_agents=20,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_eho = eho.EHO()
new_eho.compile(search_space)
agents = new_eho._get_agents_from_clan(search_space.agents, 0)
assert len(agents) == 2
def test_eho_updating_operator():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(
n_agents=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_eho = eho.EHO()
new_eho.compile(search_space)
centers = [np.random.normal(size=(2, 1)) for _ in range(10)]
new_eho._updating_operator(search_space.agents, centers, square)
def test_eho_compile():
search_space = search.SearchSpace(
n_agents=20, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_eho = eho.EHO()
new_eho.compile(search_space)
try:
new_eho.n_ci = "a"
except:
new_eho.n_ci = 1
assert new_eho.n_ci == 1
try:
new_eho.n_ci = -1
except:
new_eho.n_ci = 1
assert new_eho.n_ci == 1
def test_eho_hyperparams_setter():
new_eho = eho.EHO()
try:
new_eho.alpha = 'a'
except:
new_eho.alpha = 0.5
try:
new_eho.alpha = -1
except:
new_eho.alpha = 0.5
assert new_eho.alpha == 0.5
try:
new_eho.beta = 'a'
except:
new_eho.beta = 0.1
try:
new_eho.beta = -1
except:
new_eho.beta = 0.1
assert new_eho.beta == 0.1
try:
new_eho.n_clans = 0.0
except:
new_eho.n_clans = 10
try:
new_eho.n_clans = 0
except:
new_eho.n_clans = 10
assert new_eho.n_clans == 10
def test_eho_build():
new_eho = eho.EHO()
assert new_eho.built == True