def test_pio_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_pio = pio.PIO()
new_pio.compile(search_space)
try:
new_pio.n_p = 'a'
except:
new_pio.n_p = 1
assert new_pio.n_p == 1
try:
new_pio.n_p = -1
except:
new_pio.n_p = 1
assert new_pio.n_p == 1
try:
new_pio.velocity = 1
except:
new_pio.velocity = np.array([1])
assert new_pio.velocity == np.array([1])
def test_pio_hyperparams():
hyperparams = {'n_c1': 150, 'n_c2': 200, 'R': 0.2}
new_pio = pio.PIO(hyperparams=hyperparams)
assert new_pio.n_c1 == 150
assert new_pio.n_c2 == 200
assert new_pio.R == 0.2
def test_pio_calculate_center():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_pio = pio.PIO()
new_pio.compile(search_space)
center = new_pio._calculate_center(search_space.agents)
def test_pio_params():
params = {"n_c1": 150, "n_c2": 200, "R": 0.2}
new_pio = pio.PIO(params=params)
assert new_pio.n_c1 == 150
assert new_pio.n_c2 == 200
assert new_pio.R == 0.2
def test_pio_update():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_pio = pio.PIO()
new_pio.compile(search_space)
new_pio.update(search_space, 1)
new_pio.update(search_space, 175)
def test_pio_hyperparams_setter():
new_pio = pio.PIO()
try:
new_pio.n_c1 = 0.0
except:
new_pio.n_c1 = 150
try:
new_pio.n_c1 = 0
except:
new_pio.n_c1 = 150
assert new_pio.n_c1 == 150
try:
new_pio.n_c2 = 0.0
except:
new_pio.n_c2 = 200
try:
new_pio.n_c2 = 0
except:
new_pio.n_c2 = 200
assert new_pio.n_c2 == 200
try:
new_pio.R = 'a'
except:
new_pio.R = 0.2
try:
new_pio.R = -1
except:
new_pio.R = 0.2
assert new_pio.R == 0.2
def test_pio_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_pio = pio.PIO()
search_space = search.SearchSpace(n_agents=10,
n_iterations=175,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_pio.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 pio failed to converge.'
def test_pio_build():
new_pio = pio.PIO()
assert new_pio.built == True
