def test_epo_params():
params = {'f': 2.0, 'l': 1.5}
new_epo = epo.EPO(params=params)
assert new_epo.f == 2.0
assert new_epo.l == 1.5
def test_epo_hyperparams():
hyperparams = {'f': 2.0, 'l': 1.5}
new_epo = epo.EPO(hyperparams=hyperparams)
assert new_epo.f == 2.0
assert new_epo.l == 1.5
def test_epo_params():
params = {"f": 2.0, "l": 1.5}
new_epo = epo.EPO(params=params)
assert new_epo.f == 2.0
assert new_epo.l == 1.5
def test_epo_update():
new_epo = epo.EPO()
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_epo.update(search_space, 1, 10)
def test_epo_params_setter():
new_epo = epo.EPO()
try:
new_epo.f = 'a'
except:
new_epo.f = 2.0
try:
new_epo.l = 'b'
except:
new_epo.l = 1.5
def test_epo_params_setter():
new_epo = epo.EPO()
try:
new_epo.f = "a"
except:
new_epo.f = 2.0
try:
new_epo.l = "b"
except:
new_epo.l = 1.5
def test_epo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_epo = epo.EPO()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_epo.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 epo failed to converge.'
def test_epo_build():
new_epo = epo.EPO()
assert new_epo.built == True