def test_hc_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
hyperparams = {'r_mean': 0, 'r_var': 0.1}
new_hc = hc.HC(hyperparams=hyperparams)
search_space = search.SearchSpace(n_agents=50,
n_iterations=100,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_hc.run(search_space, new_function, pre_evaluation=hook)
history = new_hc.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 hc failed to converge.'
def test_hc_update():
search_space = search.SearchSpace(n_agents=50, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_hc = hc.HC()
new_hc.update(search_space)
def test_hc_params():
params = {
"r_mean": 0,
"r_var": 0.1,
}
new_hc = hc.HC(params=params)
assert new_hc.r_mean == 0
assert new_hc.r_var == 0.1
def test_hc_hyperparams():
hyperparams = {
'r_mean': 0,
'r_var': 0.1,
}
new_hc = hc.HC(hyperparams=hyperparams)
assert new_hc.r_mean == 0
assert new_hc.r_var == 0.1
def test_hc_params():
params = {
'r_mean': 0,
'r_var': 0.1,
}
new_hc = hc.HC(params=params)
assert new_hc.r_mean == 0
assert new_hc.r_var == 0.1
def test_hc_params_setter():
new_hc = hc.HC()
try:
new_hc.r_mean = "a"
except:
new_hc.r_mean = 0.1
assert new_hc.r_mean == 0.1
try:
new_hc.r_var = "b"
except:
new_hc.r_var = 2
try:
new_hc.r_var = -1
except:
new_hc.r_var = 2
assert new_hc.r_var == 2
def test_hc_hyperparams_setter():
new_hc = hc.HC()
try:
new_hc.r_mean = 'a'
except:
new_hc.r_mean = 0.1
assert new_hc.r_mean == 0.1
try:
new_hc.r_var = 'b'
except:
new_hc.r_var = 2
try:
new_hc.r_var = -1
except:
new_hc.r_var = 2
assert new_hc.r_var == 2
def test_hc_build():
new_hc = hc.HC()
assert new_hc.built == True
