def test_isa_params_setter():
new_isa = isa.ISA()
try:
new_isa.w = "a"
except:
new_isa.w = 0.7
try:
new_isa.w = -1
except:
new_isa.w = 0.7
assert new_isa.w == 0.7
try:
new_isa.tau = "b"
except:
new_isa.tau = 0.3
try:
new_isa.tau = -1
except:
new_isa.tau = 0.3
assert new_isa.tau == 0.3
def test_isa_params():
params = {"w": 0.7, "tau": 0.3}
new_isa = isa.ISA(params=params)
assert new_isa.w == 0.7
assert new_isa.tau == 0.3
def test_isa_params():
params = {
'w': 0.7,
'tau': 0.3
}
new_isa = isa.ISA(params=params)
assert new_isa.w == 0.7
assert new_isa.tau == 0.3
def test_isa_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_isa = isa.ISA()
new_isa.compile(search_space)
for _ in range(10):
new_isa.update(search_space, square)
def test_isa_evaluate():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_isa = isa.ISA()
new_isa.compile(search_space)
new_isa.evaluate(search_space, square)
assert search_space.best_agent.fit != constant.FLOAT_MAX
def test_isa_compile():
search_space = search.SearchSpace(n_agents=10, n_variables=2,
lower_bound=[0, 0], upper_bound=[10, 10])
new_isa = isa.ISA()
new_isa.compile(search_space)
try:
new_isa.local_position = 1
except:
new_isa.local_position = np.array([1])
assert new_isa.local_position == 1
try:
new_isa.velocity = 1
except:
new_isa.velocity = np.array([1])
assert new_isa.velocity == 1