def test_cs_hyperparams():
hyperparams = {'alpha': 1.0, 'beta': 1.5, 'p': 0.2}
new_cs = cs.CS(hyperparams=hyperparams)
assert new_cs.alpha == 1.0
assert new_cs.beta == 1.5
assert new_cs.p == 0.2
def test_cs_params():
params = {"alpha": 1.0, "beta": 1.5, "p": 0.2}
new_cs = cs.CS(params=params)
assert new_cs.alpha == 1.0
assert new_cs.beta == 1.5
assert new_cs.p == 0.2
def test_cs_generate_abandoned_nests():
search_space = search.SearchSpace(n_agents=20,
n_variables=2,
lower_bound=[-10, -10],
upper_bound=[10, 10])
new_cs = cs.CS()
new_agents = new_cs._generate_abandoned_nests(search_space.agents, 0.5)
assert len(new_agents) == 20
def test_cs_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=20,
n_variables=2,
lower_bound=[-10, -10],
upper_bound=[10, 10])
new_cs = cs.CS()
new_cs.update(search_space, square)
def test_cs_evaluate_nests():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=20,
n_variables=2,
lower_bound=[-10, -10],
upper_bound=[10, 10])
new_cs = cs.CS()
new_agents = new_cs._generate_abandoned_nests(search_space.agents, 0.5)
new_cs._evaluate_nests(search_space.agents, new_agents, square)
def test_cs_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_cs = cs.CS()
search_space = search.SearchSpace(n_agents=20,
n_iterations=100,
n_variables=2,
lower_bound=[-10, -10],
upper_bound=[10, 10])
new_cs._update(search_space.agents, search_space.best_agent, new_function)
assert search_space.agents[0].position[0] != 0
def test_cs_params_setter():
new_cs = cs.CS()
try:
new_cs.alpha = "a"
except:
new_cs.alpha = 0.001
try:
new_cs.alpha = -1
except:
new_cs.alpha = 0.001
assert new_cs.alpha == 0.001
try:
new_cs.beta = "b"
except:
new_cs.beta = 0.75
try:
new_cs.beta = -1
except:
new_cs.beta = 0.75
assert new_cs.beta == 0.75
try:
new_cs.p = "c"
except:
new_cs.p = 0.25
try:
new_cs.p = -1
except:
new_cs.p = 0.25
assert new_cs.p == 0.25
def test_cs_hyperparams_setter():
new_cs = cs.CS()
try:
new_cs.alpha = 'a'
except:
new_cs.alpha = 0.001
try:
new_cs.alpha = -1
except:
new_cs.alpha = 0.001
assert new_cs.alpha == 0.001
try:
new_cs.beta = 'b'
except:
new_cs.beta = 0.75
try:
new_cs.beta = -1
except:
new_cs.beta = 0.75
assert new_cs.beta == 0.75
try:
new_cs.p = 'c'
except:
new_cs.p = 0.25
try:
new_cs.p = -1
except:
new_cs.p = 0.25
assert new_cs.p == 0.25
def test_cs_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_cs = cs.CS()
search_space = search.SearchSpace(n_agents=25,
n_iterations=30,
n_variables=2,
lower_bound=[-10, -10],
upper_bound=[10, 10])
history = new_cs.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 abc failed to converge.'
def test_cs_build():
new_cs = cs.CS()
assert new_cs.built == True