def test_aso_params():
params = {'alpha': 50.0, 'beta': 0.2}
new_aso = aso.ASO(params=params)
assert new_aso.alpha == 50.0
assert new_aso.beta == 0.2
def test_aso_hyperparams():
hyperparams = {'alpha': 50.0, 'beta': 0.2}
new_aso = aso.ASO(hyperparams=hyperparams)
assert new_aso.alpha == 50.0
assert new_aso.beta == 0.2
def test_aso_params():
params = {"alpha": 50.0, "beta": 0.2}
new_aso = aso.ASO(params=params)
assert new_aso.alpha == 50.0
assert new_aso.beta == 0.2
def test_aso_update():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_aso = aso.ASO()
new_aso.compile(search_space)
new_aso.update(search_space, 1, 10)
def test_aso_calculate_potential():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_aso = aso.ASO()
new_aso.compile(search_space)
new_aso._calculate_potential(search_space.agents[0],
search_space.agents[1], np.array([1]), 1, 10)
def test_aso_calculate_acceleration():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_aso = aso.ASO()
new_aso.compile(search_space)
mass = new_aso._calculate_mass(search_space.agents)
new_aso._calculate_acceleration(search_space.agents,
search_space.best_agent, mass, 1, 10)
def test_aso_calculate_mass():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_aso = aso.ASO()
new_aso.compile(search_space)
mass = new_aso._calculate_mass(search_space.agents)
assert mass[0] == 0.1
def test_aso_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_aso = aso.ASO()
new_aso.compile(search_space)
try:
new_aso.velocity = 1
except:
new_aso.velocity = np.array([1])
assert new_aso.velocity == np.array([1])
def test_aso_hyperparams_setter():
new_aso = aso.ASO()
try:
new_aso.alpha = 'a'
except:
new_aso.alpha = 50.0
try:
new_aso.beta = 'b'
except:
new_aso.beta = 0.2
try:
new_aso.beta = -1
except:
new_aso.beta = 0.2
assert new_aso.beta == 0.2
def test_aso_params_setter():
new_aso = aso.ASO()
try:
new_aso.alpha = "a"
except:
new_aso.alpha = 50.0
try:
new_aso.beta = "b"
except:
new_aso.beta = 0.2
try:
new_aso.beta = -1
except:
new_aso.beta = 0.2
assert new_aso.beta == 0.2
def test_aso_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_aso = aso.ASO()
search_space = search.SearchSpace(n_agents=10,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_aso.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 aso failed to converge.'
def test_aso_build():
new_aso = aso.ASO()
assert new_aso.built == True
