def test_gco_hyperparams_setter():
new_gco = gco.GCO()
try:
new_gco.CR = 'a'
except:
new_gco.CR = 0.75
try:
new_gco.CR = -1
except:
new_gco.CR = 0.75
assert new_gco.CR == 0.75
try:
new_gco.F = 'b'
except:
new_gco.F = 1.5
try:
new_gco.F = -1
except:
new_gco.F = 1.5
assert new_gco.F == 1.5
def test_gco_params_setter():
new_gco = gco.GCO()
try:
new_gco.CR = "a"
except:
new_gco.CR = 0.75
try:
new_gco.CR = -1
except:
new_gco.CR = 0.75
assert new_gco.CR == 0.75
try:
new_gco.F = "b"
except:
new_gco.F = 1.5
try:
new_gco.F = -1
except:
new_gco.F = 1.5
assert new_gco.F == 1.5
def test_gco_params():
params = {"CR": 0.7, "F": 1.25}
new_gco = gco.GCO(params=params)
assert new_gco.CR == 0.7
assert new_gco.F == 1.25
def test_gco_params():
params = {'CR': 0.7, 'F': 1.25}
new_gco = gco.GCO(params=params)
assert new_gco.CR == 0.7
assert new_gco.F == 1.25
def test_gco_light_zone():
search_space = search.SearchSpace(n_agents=4,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_gco = gco.GCO()
new_gco.compile(search_space)
new_gco._light_zone(search_space.agents)
def test_gco_hyperparams():
hyperparams = {
'CR': 0.7,
'F': 1.25
}
new_gco = gco.GCO(hyperparams=hyperparams)
assert new_gco.CR == 0.7
assert new_gco.F == 1.25
def test_gco_mutate_cell():
new_gco = gco.GCO()
search_space = search.SearchSpace(n_agents=4, n_iterations=10,
n_variables=2, lower_bound=[1, 1],
upper_bound=[10, 10])
cell = new_gco._mutate_cell(
search_space.agents[0], search_space.agents[1], search_space.agents[2], search_space.agents[3])
assert cell.position[0][0] != 0
def test_gco_dark_zone():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=4,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_gco = gco.GCO()
new_gco.compile(search_space)
new_gco._dark_zone(search_space.agents, square)
def test_gco_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_gco = gco.GCO()
search_space = search.SearchSpace(n_agents=4, n_iterations=10,
n_variables=2, lower_bound=[1, 1],
upper_bound=[10, 10])
new_gco._update(search_space.agents, new_function, np.array([70, 70, 70, 70]), np.array([1, 1, 1, 1]))
assert search_space.agents[0].position[0] != 0
def test_gco_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=4,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_gco = gco.GCO()
new_gco.compile(search_space)
new_gco.update(search_space, square)
assert search_space.agents[0].position[0] != 0
def test_gco_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_gco = gco.GCO()
search_space = search.SearchSpace(n_agents=10, n_iterations=30,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_gco.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 gco failed to converge.'
def test_gco_compile():
search_space = search.SearchSpace(n_agents=4,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_gco = gco.GCO()
new_gco.compile(search_space)
try:
new_gco.life = 1
except:
new_gco.life = np.array([1])
assert new_gco.life == np.array([1])
try:
new_gco.counter = 1
except:
new_gco.counter = np.array([1])
assert new_gco.counter == np.array([1])
def test_gco_build():
new_gco = gco.GCO()
assert new_gco.built == True
