def test_cem_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'n_updates': 5, 'alpha': 0.7}
new_cem = cem.CEM(hyperparams=hyperparams)
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_cem.run(search_space, new_function, pre_evaluation=hook)
history = new_cem.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 cem failed to converge.'
def test_cem_params_setter():
new_cem = cem.CEM()
try:
new_cem.n_updates = 'a'
except:
new_cem.n_updates = 10
try:
new_cem.n_updates = -1
except:
new_cem.n_updates = 10
assert new_cem.n_updates == 10
try:
new_cem.alpha = 'b'
except:
new_cem.alpha = 0.5
try:
new_cem.alpha = -1
except:
new_cem.alpha = 0.5
assert new_cem.alpha == 0.5
def test_cem_update_std():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_cem = cem.CEM()
new_cem.compile(search_space)
new_cem._update_std(np.array([1, 1]))
assert new_cem.std[0] != 0
def test_cem_create_new_samples():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_cem = cem.CEM()
new_cem.compile(search_space)
new_cem._create_new_samples(search_space.agents, square)
def test_cem_params():
params = {
'n_updates': 5,
'alpha': 0.7,
}
new_cem = cem.CEM(params=params)
assert new_cem.n_updates == 5
assert new_cem.alpha == 0.7
def test_cem_hyperparams():
hyperparams = {
'n_updates': 5,
'alpha': 0.7,
}
new_cem = cem.CEM(hyperparams=hyperparams)
assert new_cem.n_updates == 5
assert new_cem.alpha == 0.7
def test_cem_params():
params = {
"n_updates": 5,
"alpha": 0.7,
}
new_cem = cem.CEM(params=params)
assert new_cem.n_updates == 5
assert new_cem.alpha == 0.7
def test_cem_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_cem = cem.CEM()
new_cem.compile(search_space)
new_cem.update(search_space, new_function)
def test_cem_create_new_samples():
def square(x):
return np.sum(x**2)
new_cem = cem.CEM()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_cem._create_new_samples(search_space.agents, square, np.array([1, 1]),
np.array([1, 1]))
def test_cem_compile():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_cem = cem.CEM()
new_cem.compile(search_space)
try:
new_cem.mean = 1
except:
new_cem.mean = np.array([1])
assert new_cem.mean == 1
try:
new_cem.std = 1
except:
new_cem.std = np.array([1])
assert new_cem.std == 1
def test_cem_update_std():
new_cem = cem.CEM()
std = new_cem._update_std(np.array([1, 1]), 1, 0.25)
assert std != 0
def test_cem_update_mean():
new_cem = cem.CEM()
mean = new_cem._update_mean(np.array([1, 1]), 1)
assert mean != 0
def test_cem_build():
new_cem = cem.CEM()
assert new_cem.built == True
