def test_fpa_hyperparams():
hyperparams = {'beta': 1.0, 'eta': 0.5, 'p': 0.5}
new_fpa = fpa.FPA(hyperparams=hyperparams)
assert new_fpa.beta == 1.0
assert new_fpa.eta == 0.5
assert new_fpa.p == 0.5
def test_fpa_params():
params = {'beta': 1.0, 'eta': 0.5, 'p': 0.5}
new_fpa = fpa.FPA(params=params)
assert new_fpa.beta == 1.0
assert new_fpa.eta == 0.5
assert new_fpa.p == 0.5
def test_fpa_params():
params = {"beta": 1.0, "eta": 0.5, "p": 0.5}
new_fpa = fpa.FPA(params=params)
assert new_fpa.beta == 1.0
assert new_fpa.eta == 0.5
assert new_fpa.p == 0.5
def test_fpa_update():
def square(x):
return np.sum(x**2)
new_fpa = fpa.FPA()
search_space = search.SearchSpace(n_agents=2,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_fpa.update(search_space, square)
new_fpa.p = 0.01
new_fpa.update(search_space, square)
def test_fpa_update():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_fpa = fpa.FPA()
search_space = search.SearchSpace(n_agents=2,
n_iterations=10,
n_variables=2,
lower_bound=[1, 1],
upper_bound=[10, 10])
new_fpa._update(search_space.agents, search_space.best_agent, new_function)
assert search_space.agents[0].position[0] != 0
def test_fpa_params_setter():
new_fpa = fpa.FPA()
try:
new_fpa.beta = 'a'
except:
new_fpa.beta = 0.75
try:
new_fpa.beta = -1
except:
new_fpa.beta = 0.75
assert new_fpa.beta == 0.75
try:
new_fpa.eta = 'b'
except:
new_fpa.eta = 1.5
try:
new_fpa.eta = -1
except:
new_fpa.eta = 1.5
assert new_fpa.eta == 1.5
try:
new_fpa.p = 'c'
except:
new_fpa.p = 0.25
try:
new_fpa.p = -1
except:
new_fpa.p = 0.25
assert new_fpa.p == 0.25
def test_fpa_params_setter():
new_fpa = fpa.FPA()
try:
new_fpa.beta = "a"
except:
new_fpa.beta = 0.75
try:
new_fpa.beta = -1
except:
new_fpa.beta = 0.75
assert new_fpa.beta == 0.75
try:
new_fpa.eta = "b"
except:
new_fpa.eta = 1.5
try:
new_fpa.eta = -1
except:
new_fpa.eta = 1.5
assert new_fpa.eta == 1.5
try:
new_fpa.p = "c"
except:
new_fpa.p = 0.25
try:
new_fpa.p = -1
except:
new_fpa.p = 0.25
assert new_fpa.p == 0.25
def test_fpa_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_fpa = fpa.FPA()
search_space = search.SearchSpace(n_agents=10,
n_iterations=30,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_fpa.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 fpa failed to converge.'
def test_fpa_local_pollination():
new_fpa = fpa.FPA()
position = new_fpa._local_pollination(1, 2, 1, 0.5)
assert position == 1.5
def test_fpa_global_pollination():
new_fpa = fpa.FPA()
position = new_fpa._global_pollination(1, 2)
assert position != 0
def test_fpa_build():
new_fpa = fpa.FPA()
assert new_fpa.built == True
