def test_ihs_hyperparams_setter():
new_ihs = hs.IHS()
try:
new_ihs.PAR_min = 'a'
except:
new_ihs.PAR_min = 0.5
try:
new_ihs.PAR_min = -1
except:
new_ihs.PAR_min = 0.5
assert new_ihs.PAR_min == 0.5
try:
new_ihs.PAR_max = 'b'
except:
new_ihs.PAR_max = 1.0
try:
new_ihs.PAR_max = -1
except:
new_ihs.PAR_max = 1.0
try:
new_ihs.PAR_max = 0
except:
new_ihs.PAR_max = 1.0
assert new_ihs.PAR_max == 1.0
try:
new_ihs.bw_min = 'c'
except:
new_ihs.bw_min = 1.0
try:
new_ihs.bw_min = -1
except:
new_ihs.bw_min = 1.0
assert new_ihs.bw_min == 1.0
try:
new_ihs.bw_max = 'd'
except:
new_ihs.bw_max = 10.0
try:
new_ihs.bw_max = -1
except:
new_ihs.bw_max = 10.0
try:
new_ihs.bw_max = 0
except:
new_ihs.bw_max = 10.0
assert new_ihs.bw_max == 10.0
def test_ihs_params_setter():
new_ihs = hs.IHS()
try:
new_ihs.PAR_min = "a"
except:
new_ihs.PAR_min = 0.5
try:
new_ihs.PAR_min = -1
except:
new_ihs.PAR_min = 0.5
assert new_ihs.PAR_min == 0.5
try:
new_ihs.PAR_max = "b"
except:
new_ihs.PAR_max = 1.0
try:
new_ihs.PAR_max = -1
except:
new_ihs.PAR_max = 1.0
try:
new_ihs.PAR_max = 0
except:
new_ihs.PAR_max = 1.0
assert new_ihs.PAR_max == 1.0
try:
new_ihs.bw_min = "c"
except:
new_ihs.bw_min = 1.0
try:
new_ihs.bw_min = -1
except:
new_ihs.bw_min = 1.0
assert new_ihs.bw_min == 1.0
try:
new_ihs.bw_max = "d"
except:
new_ihs.bw_max = 10.0
try:
new_ihs.bw_max = -1
except:
new_ihs.bw_max = 10.0
try:
new_ihs.bw_max = 0
except:
new_ihs.bw_max = 10.0
assert new_ihs.bw_max == 10.0
def test_ihs_update():
def square(x):
return np.sum(x**2)
new_ihs = hs.IHS()
search_space = search.SearchSpace(n_agents=20, n_variables=2,
lower_bound=[0, 0], upper_bound=[5, 5])
new_ihs.update(search_space, square, 1, 10)
def test_ihs_hyperparams():
hyperparams = {'PAR_min': 0.5, 'PAR_max': 1, 'bw_min': 2, 'bw_max': 5}
new_ihs = hs.IHS(hyperparams=hyperparams)
assert new_ihs.PAR_min == 0.5
assert new_ihs.PAR_max == 1
assert new_ihs.bw_min == 2
assert new_ihs.bw_max == 5
def test_ihs_params():
params = {"PAR_min": 0.5, "PAR_max": 1, "bw_min": 2, "bw_max": 5}
new_ihs = hs.IHS(params=params)
assert new_ihs.PAR_min == 0.5
assert new_ihs.PAR_max == 1
assert new_ihs.bw_min == 2
assert new_ihs.bw_max == 5
def test_ihs_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_ihs = hs.IHS()
search_space = search.SearchSpace(n_agents=20, n_iterations=50,
n_variables=2, lower_bound=[0, 0],
upper_bound=[5, 5])
history = new_ihs.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 ihs failed to converge.'
def test_ihs_rebuild():
new_ihs = hs.IHS()
assert new_ihs.built == True
