def test_sa_hyperparams_setter():
new_sa = sa.SA()
try:
new_sa.T = 'a'
except:
new_sa.T = 10
try:
new_sa.T = -1
except:
new_sa.T = 10
assert new_sa.T == 10
try:
new_sa.beta = 'b'
except:
new_sa.beta = 0.5
try:
new_sa.beta = -1
except:
new_sa.beta = 0.5
assert new_sa.beta == 0.5
def test_sa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'T': 100, 'beta': 0.99}
new_sa = sa.SA(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_sa.run(search_space, new_function, pre_evaluation=hook)
history = new_sa.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 sa failed to converge.'
def test_sa_params_setter():
new_sa = sa.SA()
try:
new_sa.T = "a"
except:
new_sa.T = 10
try:
new_sa.T = -1
except:
new_sa.T = 10
assert new_sa.T == 10
try:
new_sa.beta = "b"
except:
new_sa.beta = 0.5
try:
new_sa.beta = -1
except:
new_sa.beta = 0.5
assert new_sa.beta == 0.5
def test_sa_hyperparams():
hyperparams = {
'T': 100,
'beta': 0.99,
}
new_sa = sa.SA(hyperparams=hyperparams)
assert new_sa.T == 100
assert new_sa.beta == 0.99
def test_sa_params():
params = {
"T": 100,
"beta": 0.99,
}
new_sa = sa.SA(params=params)
assert new_sa.T == 100
assert new_sa.beta == 0.99
def test_sa_params():
params = {
'T': 100,
'beta': 0.99,
}
new_sa = sa.SA(params=params)
assert new_sa.T == 100
assert new_sa.beta == 0.99
def test_sa_update():
def square(x):
return np.sum(x**2)
new_sa = sa.SA()
search_space = search.SearchSpace(
n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_sa.update(search_space, square)
new_sa.update(search_space, square)
def test_sa_build():
new_sa = sa.SA()
assert new_sa.built == True