def test_aiwpso_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_aiwpso = pso.AIWPSO()
search_space = search.SearchSpace(n_agents=10,
n_iterations=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_aiwpso.run(search_space, new_function, pre_evaluation=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 aiwpso failed to converge.'
def test_aiwpso_hyperparams_setter():
new_aiwpso = pso.AIWPSO()
try:
new_aiwpso.w_min = 'a'
except:
new_aiwpso.w_min = 0.5
try:
new_aiwpso.w_min = -1
except:
new_aiwpso.w_min = 0.5
assert new_aiwpso.w_min == 0.5
try:
new_aiwpso.w_max = 'b'
except:
new_aiwpso.w_max = 1.0
try:
new_aiwpso.w_max = -1
except:
new_aiwpso.w_max = 1.0
try:
new_aiwpso.w_max = 0
except:
new_aiwpso.w_max = 1.0
assert new_aiwpso.w_max == 1.0
def test_aiwpso_update():
search_space = search.SearchSpace(n_agents=2, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_aiwpso = pso.AIWPSO()
new_aiwpso.compile(search_space)
new_aiwpso.update(search_space, 0)
def test_aiwpso_compute_success():
search_space = search.SearchSpace(n_agents=2, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_aiwpso = pso.AIWPSO()
new_aiwpso.compile(search_space)
new_aiwpso.fitness = [1, 1]
new_aiwpso._compute_success(search_space.agents)
def test_aiwpso_hyperparams():
hyperparams = {
'w_min': 1,
'w_max': 3,
}
new_aiwpso = pso.AIWPSO(hyperparams=hyperparams)
assert new_aiwpso.w_min == 1
assert new_aiwpso.w_max == 3
def test_aiwpso_compute_success():
n_agents = 2
search_space = search.SearchSpace(n_agents=n_agents, n_iterations=10,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
new_aiwpso = pso.AIWPSO()
new_fitness = np.zeros(n_agents)
new_aiwpso._compute_success(search_space.agents, new_fitness)
assert new_aiwpso.w != 0
def test_aiwpso_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_aiwpso = pso.AIWPSO()
local_position = np.zeros((2, 2, 1))
new_aiwpso._evaluate(search_space, new_function, local_position)
assert search_space.best_agent.fit < sys.float_info.max
def test_aiwpso_rebuild():
new_aiwpso = pso.AIWPSO()
assert new_aiwpso.built == True
