def test_bpso_update_velocity():
new_bpso = bpso.BPSO()
velocity = new_bpso._update_velocity(np.array([1]), np.array([1]),
np.array([1]))
assert velocity == 0 or velocity == 1
def test_bpso_update():
boolean_space = boolean.BooleanSpace(n_agents=2, n_variables=5)
new_bpso = bpso.BPSO()
new_bpso.compile(boolean_space)
new_bpso.update(boolean_space)
def test_bpso_hyperparams():
hyperparams = {
'c1': r.generate_binary_random_number(size=(1, 1)),
'c2': r.generate_binary_random_number(size=(1, 1))
}
new_bpso = bpso.BPSO(hyperparams=hyperparams)
assert new_bpso.c1 == 0 or new_bpso.c1 == 1
assert new_bpso.c2 == 0 or new_bpso.c2 == 1
def test_bpso_params():
params = {
"c1": r.generate_binary_random_number(size=(1, 1)),
"c2": r.generate_binary_random_number(size=(1, 1)),
}
new_bpso = bpso.BPSO(params=params)
assert new_bpso.c1 == 0 or new_bpso.c1 == 1
assert new_bpso.c2 == 0 or new_bpso.c2 == 1
def test_bpso_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
boolean_space = boolean.BooleanSpace(n_agents=2, n_variables=5)
new_bpso = bpso.BPSO()
new_bpso.compile(boolean_space)
new_bpso.evaluate(boolean_space, new_function)
assert boolean_space.best_agent.fit < sys.float_info.max
def test_bpso_hyperparams_setter():
new_bpso = bpso.BPSO()
try:
new_bpso.c1 = 'a'
except:
new_bpso.c1 = r.generate_binary_random_number(size=(1, 1))
assert new_bpso.c1 == 0 or new_bpso.c1 == 1
try:
new_bpso.c2 = 'b'
except:
new_bpso.c2 = r.generate_binary_random_number(size=(1, 1))
assert new_bpso.c2 == 0 or new_bpso.c2 == 1
def test_bpso_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
boolean_space = boolean.BooleanSpace(n_agents=2,
n_iterations=10,
n_variables=2)
new_bpso = bpso.BPSO()
local_position = np.zeros((2, 2, 1))
new_bpso._evaluate(boolean_space, new_function, local_position)
assert boolean_space.best_agent.fit < sys.float_info.max
def test_bpso_evaluate():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
search_space = search.SearchSpace(n_agents=2, n_iterations=10,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
new_bpso = bpso.BPSO()
local_position = np.zeros((2, 2, 1))
new_bpso._evaluate(search_space, new_function, local_position)
assert search_space.best_agent.fit < sys.float_info.max
def test_bpso_compile():
boolean_space = boolean.BooleanSpace(n_agents=2, n_variables=5)
new_bpso = bpso.BPSO()
new_bpso.compile(boolean_space)
try:
new_bpso.local_position = 1
except:
new_bpso.local_position = np.array([1])
assert new_bpso.local_position == 1
try:
new_bpso.velocity = 1
except:
new_bpso.velocity = np.array([1])
assert new_bpso.velocity == 1
def test_bpso_run():
def hook(optimizer, space, function):
return
new_function = function.Function(
pointer=Knapsack(values=[55, 10, 47, 5, 4],
weights=[95, 4, 60, 32, 23],
max_capacity=100))
new_bpso = bpso.BPSO()
boolean_space = boolean.BooleanSpace(n_agents=2,
n_iterations=10,
n_variables=5)
history = new_bpso.run(boolean_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
assert len(history.local) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm bpso failed to converge.'
def test_bpso_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_bpso = bpso.BPSO()
boolean_space = boolean.BooleanSpace(n_agents=5,
n_iterations=20,
n_variables=2)
history = new_bpso.run(boolean_space, new_function, pre_evaluation=hook)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
assert len(history.local) > 0
best_fitness = history.best_agent[-1][1]
assert best_fitness <= constants.TEST_EPSILON, 'The algorithm bpso failed to converge.'
def test_bpso_update_position():
new_bpso = bpso.BPSO()
position = new_bpso._update_position(1, 1)
assert position == 0 or position == 1
def test_bpso_build():
new_bpso = bpso.BPSO()
assert new_bpso.built == True
