def test_iwo_update():
def square(x):
return np.sum(x**2)
new_iwo = iwo.IWO()
new_iwo.min_seeds = 5
new_iwo.max_seeds = 20
search_space = search.SearchSpace(n_agents=5,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_iwo.update(search_space, square, 1, 10)
def test_iwo_produce_offspring():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=2,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_iwo = iwo.IWO()
agent = new_iwo._produce_offspring(search_space.agents[0], square)
assert type(agent).__name__ == 'Agent'
def test_iwo_update():
def square(x):
return np.sum(x**2)
assert square(2) == 4
new_function = function.Function(pointer=square)
new_iwo = iwo.IWO()
search_space = search.SearchSpace(n_agents=2,
n_iterations=10,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_iwo._update(search_space.agents, search_space.n_agents, new_function)
assert search_space.agents[0].position[0] != 0
def test_iwo_hyperparams():
hyperparams = {
'min_seeds': 0,
'max_seeds': 5,
'e': 2,
'final_sigma': 0.001,
'init_sigma': 3
}
new_iwo = iwo.IWO(hyperparams=hyperparams)
assert new_iwo.min_seeds == 0
assert new_iwo.max_seeds == 5
assert new_iwo.e == 2
assert new_iwo.final_sigma == 0.001
assert new_iwo.init_sigma == 3
def test_iwo_params():
params = {
"min_seeds": 0,
"max_seeds": 5,
"e": 2,
"final_sigma": 0.001,
"init_sigma": 3,
}
new_iwo = iwo.IWO(params=params)
assert new_iwo.min_seeds == 0
assert new_iwo.max_seeds == 5
assert new_iwo.e == 2
assert new_iwo.final_sigma == 0.001
assert new_iwo.init_sigma == 3
def test_iwo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_iwo = iwo.IWO()
search_space = search.SearchSpace(n_agents=10,
n_iterations=30,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_iwo.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 iwo failed to converge.'
def test_iwo_build():
new_iwo = iwo.IWO()
assert new_iwo.built == True
def test_iwo_hyperparams_setter():
new_iwo = iwo.IWO()
try:
new_iwo.min_seeds = 'a'
except:
new_iwo.min_seeds = 0
try:
new_iwo.min_seeds = -1
except:
new_iwo.min_seeds = 0
assert new_iwo.min_seeds == 0
try:
new_iwo.max_seeds = 'b'
except:
new_iwo.max_seeds = 2
try:
new_iwo.max_seeds = -1
except:
new_iwo.max_seeds = 2
assert new_iwo.max_seeds == 2
try:
new_iwo.e = 'c'
except:
new_iwo.e = 1.5
try:
new_iwo.e = -1
except:
new_iwo.e = 1.5
assert new_iwo.e == 1.5
try:
new_iwo.final_sigma = 'd'
except:
new_iwo.final_sigma = 1.5
try:
new_iwo.final_sigma = -1
except:
new_iwo.final_sigma = 1.5
assert new_iwo.final_sigma == 1.5
try:
new_iwo.init_sigma = 'e'
except:
new_iwo.init_sigma = 2.0
try:
new_iwo.init_sigma = -1
except:
new_iwo.init_sigma = 2.0
try:
new_iwo.init_sigma = 1.3
except:
new_iwo.init_sigma = 2.0
assert new_iwo.init_sigma == 2.0
def test_iwo_spatial_dispersal():
new_iwo = iwo.IWO()
new_iwo._spatial_dispersal(1, 10)
assert new_iwo.sigma == 2.43019
def test_iwo_params_setter():
new_iwo = iwo.IWO()
try:
new_iwo.min_seeds = "a"
except:
new_iwo.min_seeds = 0
try:
new_iwo.min_seeds = -1
except:
new_iwo.min_seeds = 0
assert new_iwo.min_seeds == 0
try:
new_iwo.max_seeds = "b"
except:
new_iwo.max_seeds = 2
try:
new_iwo.max_seeds = -1
except:
new_iwo.max_seeds = 2
assert new_iwo.max_seeds == 2
try:
new_iwo.e = "c"
except:
new_iwo.e = 1.5
try:
new_iwo.e = -1
except:
new_iwo.e = 1.5
assert new_iwo.e == 1.5
try:
new_iwo.final_sigma = "d"
except:
new_iwo.final_sigma = 1.5
try:
new_iwo.final_sigma = -1
except:
new_iwo.final_sigma = 1.5
assert new_iwo.final_sigma == 1.5
try:
new_iwo.init_sigma = "e"
except:
new_iwo.init_sigma = 2.0
try:
new_iwo.init_sigma = -1
except:
new_iwo.init_sigma = 2.0
try:
new_iwo.init_sigma = 1.3
except:
new_iwo.init_sigma = 2.0
assert new_iwo.init_sigma == 2.0
try:
new_iwo.sigma = "f"
except:
new_iwo.sigma = 1
assert new_iwo.sigma == 1