def test_bwo_mutation():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_bwo = bwo.BWO()
alpha = new_bwo._mutation(search_space.agents[0])
assert type(alpha).__name__ == 'Agent'
def test_bwo_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_bwo = bwo.BWO()
new_bwo.update(search_space, square)
def test_bwo_procreating():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_bwo = bwo.BWO()
y1, y2 = new_bwo._procreating(search_space.agents[0],
search_space.agents[1])
assert type(y1).__name__ == 'Agent'
assert type(y2).__name__ == 'Agent'
def test_bwo_hyperparams():
hyperparams = {
'pp': 0.6,
'cr': 0.44,
'pm': 0.4,
}
new_bwo = bwo.BWO(hyperparams=hyperparams)
assert new_bwo.pp == 0.6
assert new_bwo.cr == 0.44
assert new_bwo.pm == 0.4
def test_bwo_params():
params = {
'pp': 0.6,
'cr': 0.44,
'pm': 0.4,
}
new_bwo = bwo.BWO(params=params)
assert new_bwo.pp == 0.6
assert new_bwo.cr == 0.44
assert new_bwo.pm == 0.4
def test_bwo_params():
params = {
"pp": 0.6,
"cr": 0.44,
"pm": 0.4,
}
new_bwo = bwo.BWO(params=params)
assert new_bwo.pp == 0.6
assert new_bwo.cr == 0.44
assert new_bwo.pm == 0.4
def test_bwo_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_bwo = bwo.BWO()
search_space = search.SearchSpace(n_agents=10, n_iterations=10,
n_variables=2, lower_bound=[1, 1],
upper_bound=[10, 10])
new_bwo._evaluate(search_space, new_function)
new_bwo._update(search_space.agents,
search_space.n_variables, new_function)
assert search_space.agents[0].position[0] != 0
def test_bwo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_bwo = bwo.BWO()
search_space = search.SearchSpace(n_agents=10, n_iterations=30,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_bwo.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 bwo failed to converge.'
def test_bwo_hyperparams_setter():
new_bwo = bwo.BWO()
try:
new_bwo.pp = 'a'
except:
new_bwo.pp = 0.6
try:
new_bwo.pp = -1
except:
new_bwo.pp = 0.6
assert new_bwo.pp == 0.6
try:
new_bwo.cr = 'b'
except:
new_bwo.cr = 0.44
try:
new_bwo.cr = -1
except:
new_bwo.cr = 0.44
assert new_bwo.cr == 0.44
try:
new_bwo.pm = 'c'
except:
new_bwo.pm = 0.4
try:
new_bwo.pm = -1
except:
new_bwo.pm = 0.4
assert new_bwo.pm == 0.4
def test_bwo_params_setter():
new_bwo = bwo.BWO()
try:
new_bwo.pp = "a"
except:
new_bwo.pp = 0.6
try:
new_bwo.pp = -1
except:
new_bwo.pp = 0.6
assert new_bwo.pp == 0.6
try:
new_bwo.cr = "b"
except:
new_bwo.cr = 0.44
try:
new_bwo.cr = -1
except:
new_bwo.cr = 0.44
assert new_bwo.cr == 0.44
try:
new_bwo.pm = "c"
except:
new_bwo.pm = 0.4
try:
new_bwo.pm = -1
except:
new_bwo.pm = 0.4
assert new_bwo.pm == 0.4
def test_bwo_build():
new_bwo = bwo.BWO()
assert new_bwo.built == True