def test_fa_update():
new_fa = fa.FA()
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_fa.update(search_space, 100)
def test_fa_params():
params = {"alpha": 0.5, "beta": 0.2, "gamma": 1.0}
new_fa = fa.FA(params=params)
assert new_fa.alpha == 0.5
assert new_fa.beta == 0.2
assert new_fa.gamma == 1.0
def test_fa_hyperparams():
hyperparams = {'alpha': 0.5, 'beta': 0.2, 'gamma': 1.0}
new_fa = fa.FA(hyperparams=hyperparams)
assert new_fa.alpha == 0.5
assert new_fa.beta == 0.2
assert new_fa.gamma == 1.0
def test_fa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_fa = fa.FA()
search_space = search.SearchSpace(n_agents=10, n_iterations=10,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_fa.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 fa failed to converge.'
def test_fa_params_setter():
new_fa = fa.FA()
try:
new_fa.alpha = "a"
except:
new_fa.alpha = 0.5
try:
new_fa.alpha = -1
except:
new_fa.alpha = 0.5
assert new_fa.alpha == 0.5
try:
new_fa.beta = "b"
except:
new_fa.beta = 0.2
try:
new_fa.beta = -1
except:
new_fa.beta = 0.2
assert new_fa.beta == 0.2
try:
new_fa.gamma = "c"
except:
new_fa.gamma = 1.0
try:
new_fa.gamma = -1
except:
new_fa.gamma = 1.0
assert new_fa.gamma == 1.0
def test_fa_hyperparams_setter():
new_fa = fa.FA()
try:
new_fa.alpha = 'a'
except:
new_fa.alpha = 0.5
try:
new_fa.alpha = -1
except:
new_fa.alpha = 0.5
assert new_fa.alpha == 0.5
try:
new_fa.beta = 'b'
except:
new_fa.beta = 0.2
try:
new_fa.beta = -1
except:
new_fa.beta = 0.2
assert new_fa.beta == 0.2
try:
new_fa.gamma = 'c'
except:
new_fa.gamma = 1.0
try:
new_fa.gamma = -1
except:
new_fa.gamma = 1.0
assert new_fa.gamma == 1.0
def test_fa_build():
new_fa = fa.FA()
assert new_fa.built == True
