def test_gsa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {
'G': 100
}
new_gsa = gsa.GSA(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_gsa.run(search_space, new_function, pre_evaluation_hook=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 gsa failed to converge.'
def test_gsa_hyperparams():
hyperparams = {
'G': 2.467,
}
new_gsa = gsa.GSA(hyperparams=hyperparams)
assert new_gsa.G == 2.467
def test_gsa_calculate_mass():
new_gsa = gsa.GSA()
search_space = search.SearchSpace(n_agents=10, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
search_space.agents[0].fit = 1
search_space.agents.sort(key=lambda x: x.fit)
mass = new_gsa._calculate_mass(search_space.agents)
assert len(mass) > 0
def test_gsa_hyperparams_setter():
new_gsa = gsa.GSA()
try:
new_gsa.G = 'a'
except:
new_gsa.G = 0.1
try:
new_gsa.G = -1
except:
new_gsa.G = 0.1
assert new_gsa.G == 0.1
def test_gsa_calculate_force():
new_gsa = gsa.GSA()
search_space = search.SearchSpace(n_agents=10, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
search_space.agents[0].fit = 1
search_space.agents.sort(key=lambda x: x.fit)
mass = new_gsa._calculate_mass(search_space.agents)
gravity = 1
force = new_gsa._calculate_force(search_space.agents, mass, gravity)
assert force.shape[0] > 0
def test_gsa_update_position():
new_gsa = gsa.GSA()
position = new_gsa._update_position(1, 1)
assert position == 2
def test_gsa_build():
new_gsa = gsa.GSA()
assert new_gsa.built == True