def test_mvo_hyperparams():
hyperparams = {'WEP_min': 0.2, 'WEP_max': 1.0, 'p': 0.5}
new_mvo = mvo.MVO(hyperparams=hyperparams)
assert new_mvo.WEP_min == 0.2
assert new_mvo.WEP_max == 1.0
assert new_mvo.p == 0.5
def test_mvo_params():
params = {"WEP_min": 0.2, "WEP_max": 1.0, "p": 0.5}
new_mvo = mvo.MVO(params=params)
assert new_mvo.WEP_min == 0.2
assert new_mvo.WEP_max == 1.0
assert new_mvo.p == 0.5
def test_mvo_update():
def square(x):
return np.sum(x**2)
new_mvo = mvo.MVO()
search_space = search.SearchSpace(n_agents=2,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_mvo.update(search_space, square, 1, 10)
new_mvo.update(search_space, square, 5, 10)
def test_mvo_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_mvo = mvo.MVO()
search_space = search.SearchSpace(n_agents=2,
n_iterations=10,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_mvo._update(search_space.agents, search_space.best_agent, new_function,
1, 1)
assert search_space.agents[0].position[0] != 0
def test_mvo_params_setter():
new_mvo = mvo.MVO()
try:
new_mvo.WEP_min = "a"
except:
new_mvo.WEP_min = 0.75
try:
new_mvo.WEP_min = -1
except:
new_mvo.WEP_min = 0.75
assert new_mvo.WEP_min == 0.75
try:
new_mvo.WEP_max = "b"
except:
new_mvo.WEP_max = 0.9
try:
new_mvo.WEP_max = 0.1
except:
new_mvo.WEP_max = 0.9
try:
new_mvo.WEP_max = -1
except:
new_mvo.WEP_max = 0.9
assert new_mvo.WEP_max == 0.9
try:
new_mvo.p = "c"
except:
new_mvo.p = 0.25
try:
new_mvo.p = -1
except:
new_mvo.p = 0.25
assert new_mvo.p == 0.25
def test_mvo_hyperparams_setter():
new_mvo = mvo.MVO()
try:
new_mvo.WEP_min = 'a'
except:
new_mvo.WEP_min = 0.75
try:
new_mvo.WEP_min = -1
except:
new_mvo.WEP_min = 0.75
assert new_mvo.WEP_min == 0.75
try:
new_mvo.WEP_max = 'b'
except:
new_mvo.WEP_max = 0.9
try:
new_mvo.WEP_max = 0.1
except:
new_mvo.WEP_max = 0.9
try:
new_mvo.WEP_max = -1
except:
new_mvo.WEP_max = 0.9
assert new_mvo.WEP_max == 0.9
try:
new_mvo.p = 'c'
except:
new_mvo.p = 0.25
try:
new_mvo.p = -1
except:
new_mvo.p = 0.25
assert new_mvo.p == 0.25
def test_mvo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_mvo = mvo.MVO()
search_space = search.SearchSpace(n_agents=10,
n_iterations=30,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_mvo.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 mvo failed to converge.'
def test_mvo_build():
new_mvo = mvo.MVO()
assert new_mvo.built == True