def test_rpso_hyperparams_setter():
new_rpso = rpso.RPSO()
try:
new_rpso.c1 = 'a'
except:
new_rpso.c1 = 1.5
try:
new_rpso.c1 = -1
except:
new_rpso.c1 = 1.5
assert new_rpso.c1 == 1.5
try:
new_rpso.c2 = 'b'
except:
new_rpso.c2 = 1.5
try:
new_rpso.c2 = -1
except:
new_rpso.c2 = 1.5
assert new_rpso.c2 == 1.5
def test_rpso_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_rpso = rpso.RPSO()
search_space = search.SearchSpace(n_agents=5,
n_iterations=20,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_rpso.run(search_space,
new_function,
pre_evaluation_hook=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
assert len(history.local) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm rpso failed to converge.'
def test_rpso_hyperparams():
hyperparams = {'c1': 1.7, 'c2': 1.7}
new_rpso = rpso.RPSO(hyperparams=hyperparams)
assert new_rpso.c1 == 1.7
assert new_rpso.c2 == 1.7
def test_rpso_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
search_space = search.SearchSpace(n_agents=2,
n_iterations=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_rpso = rpso.RPSO()
local_position = np.zeros((2, 2, 1))
new_rpso._evaluate(search_space, new_function, local_position)
assert search_space.best_agent.fit < sys.float_info.max
def test_rpso_update_position():
new_rpso = rpso.RPSO()
position = new_rpso._update_position(1, 1)
assert position == 2
def test_rpso_update_velocity():
new_rpso = rpso.RPSO()
velocity = new_rpso._update_velocity(1, 1, 1, 10, 1, 1)
assert velocity != 0
def test_rpso_build():
new_rpso = rpso.RPSO()
assert new_rpso.built == True