def test_umda_sample_position():
new_umda = umda.UMDA()
probs = np.zeros((1, 1))
position = new_umda._sample_position(probs)
assert position == 1
def test_umda_calculate_probability():
new_umda = umda.UMDA()
boolean_space = boolean.BooleanSpace(n_agents=5, n_variables=2)
probs = new_umda._calculate_probability(boolean_space.agents)
assert probs.shape == (2, 1)
def test_umda_params():
params = {'p_selection': 0.75, 'lower_bound': 0.05, 'upper_bound': 0.95}
new_umda = umda.UMDA(params=params)
assert new_umda.p_selection == 0.75
assert new_umda.lower_bound == 0.05
assert new_umda.upper_bound == 0.95
def test_umda_params():
params = {"p_selection": 0.75, "lower_bound": 0.05, "upper_bound": 0.95}
new_umda = umda.UMDA(params=params)
assert new_umda.p_selection == 0.75
assert new_umda.lower_bound == 0.05
assert new_umda.upper_bound == 0.95
def test_umda_hyperparams_setter():
new_umda = umda.UMDA()
try:
new_umda.p_selection = 'a'
except:
new_umda.p_selection = 0.75
assert new_umda.p_selection == 0.75
try:
new_umda.p_selection = -1
except:
new_umda.p_selection = 0.75
assert new_umda.p_selection == 0.75
try:
new_umda.lower_bound = 'a'
except:
new_umda.lower_bound = 0.05
assert new_umda.lower_bound == 0.05
try:
new_umda.lower_bound = -1
except:
new_umda.lower_bound = 0.05
assert new_umda.lower_bound == 0.05
try:
new_umda.upper_bound = 'a'
except:
new_umda.upper_bound = 0.95
assert new_umda.upper_bound == 0.95
try:
new_umda.upper_bound = -1
except:
new_umda.upper_bound = 0.95
assert new_umda.upper_bound == 0.95
try:
new_umda.upper_bound = 0.04
except:
new_umda.upper_bound = 0.95
assert new_umda.upper_bound == 0.95
def test_umda_run():
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=Knapsack(
values=(55, 10, 47, 5, 4), weights=(95, 4, 60, 32, 23), max_capacity=100))
new_umda = umda.UMDA()
boolean_space = boolean.BooleanSpace(
n_agents=2, n_iterations=10, n_variables=5)
history = new_umda.run(boolean_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 umda failed to converge.'
def test_umda_build():
new_umda = umda.UMDA()
assert new_umda.built == True
def test_umda_update():
new_umda = umda.UMDA()
boolean_space = boolean.BooleanSpace(n_agents=2, n_variables=5)
new_umda.update(boolean_space)
