def test_ba_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {
'f_min': 0,
'f_max': 2,
'A': 1,
'r': 0.5
}
new_ba = ba.BA(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_ba.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 ba failed to converge.'
def test_ba_hyperparams_setter():
new_ba = ba.BA()
try:
new_ba.f_min = 'a'
except:
new_ba.f_min = 0.1
try:
new_ba.f_min = -1
except:
new_ba.f_min = 0.1
assert new_ba.f_min == 0.1
try:
new_ba.f_max = 'b'
except:
new_ba.f_max = 2
try:
new_ba.f_max = -1
except:
new_ba.f_max = 2
try:
new_ba.f_max = 0
except:
new_ba.f_max = 2
assert new_ba.f_max == 2
try:
new_ba.A = 'c'
except:
new_ba.A = 0.5
try:
new_ba.A = -1
except:
new_ba.A = 0.5
assert new_ba.A == 0.5
try:
new_ba.r = 'd'
except:
new_ba.r = 0.5
try:
new_ba.r = -1
except:
new_ba.r = 0.5
assert new_ba.r == 0.5
def test_ba_params_setter():
new_ba = ba.BA()
try:
new_ba.f_min = "a"
except:
new_ba.f_min = 0.1
try:
new_ba.f_min = -1
except:
new_ba.f_min = 0.1
assert new_ba.f_min == 0.1
try:
new_ba.f_max = "b"
except:
new_ba.f_max = 2
try:
new_ba.f_max = -1
except:
new_ba.f_max = 2
try:
new_ba.f_max = 0
except:
new_ba.f_max = 2
assert new_ba.f_max == 2
try:
new_ba.A = "c"
except:
new_ba.A = 0.5
try:
new_ba.A = -1
except:
new_ba.A = 0.5
assert new_ba.A == 0.5
try:
new_ba.r = "d"
except:
new_ba.r = 0.5
try:
new_ba.r = -1
except:
new_ba.r = 0.5
assert new_ba.r == 0.5
def test_ba_params():
params = {'f_min': 0, 'f_max': 2, 'A': 0.5, 'r': 0.5}
new_ba = ba.BA(params=params)
assert new_ba.f_min == 0
assert new_ba.f_max == 2
assert new_ba.A == 0.5
assert new_ba.r == 0.5
def test_ba_params():
params = {"f_min": 0, "f_max": 2, "A": 0.5, "r": 0.5}
new_ba = ba.BA(params=params)
assert new_ba.f_min == 0
assert new_ba.f_max == 2
assert new_ba.A == 0.5
assert new_ba.r == 0.5
def test_ba_update():
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_ba = ba.BA()
new_ba.compile(search_space)
new_ba.update(search_space, square, 1)
def test_ba_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_ba = ba.BA()
new_ba.compile(search_space)
try:
new_ba.frequency = 1
except:
new_ba.frequency = np.array([1])
assert new_ba.frequency == np.array([1])
try:
new_ba.velocity = 1
except:
new_ba.velocity = np.array([1])
assert new_ba.velocity == np.array([1])
try:
new_ba.loudness = 1
except:
new_ba.loudness = np.array([1])
assert new_ba.loudness == np.array([1])
try:
new_ba.pulse_rate = 1
except:
new_ba.pulse_rate = np.array([1])
assert new_ba.pulse_rate == np.array([1])
def test_ba_update_frequency():
new_ba = ba.BA()
frequency = new_ba._update_frequency(0, 2)
assert frequency != 0
def test_ba_build():
new_ba = ba.BA()
assert new_ba.built == True
def test_ba_update_position():
new_ba = ba.BA()
position = new_ba._update_position(1, 1)
assert position == 2
def test_ba_update_velocity():
new_ba = ba.BA()
velocity = new_ba._update_velocity(1, 1, 1, 1)
assert velocity != 0
