def test_opytimizer_build():
def square(x):
return np.sum(x**2)
assert square(2) == 4
new_function = function.Function(pointer=square)
lb = [0, 0]
ub = [10, 10]
new_space = search.SearchSpace(lower_bound=lb, upper_bound=ub)
new_pso = pso.PSO()
try:
new_pso.built = False
new_opytimizer = opytimizer.Opytimizer(space=new_space,
optimizer=new_pso,
function=new_function)
except:
new_pso.built = True
new_opytimizer = opytimizer.Opytimizer(space=new_space,
optimizer=new_pso,
function=new_function)
def test_pso_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_pso = pso.PSO()
search_space = search.SearchSpace(n_agents=5,
n_iterations=20,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_pso.run(search_space, new_function, pre_evaluation_hook=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 pso failed to converge.'
def test_pso_hyperparams():
hyperparams = {'w': 2, 'c1': 1.7, 'c2': 1.7}
new_pso = pso.PSO(hyperparams=hyperparams)
assert new_pso.w == 2
assert new_pso.c1 == 1.7
assert new_pso.c2 == 1.7
def test_pso_hyperparams_setter():
new_pso = pso.PSO()
new_pso.w = 1
assert new_pso.w == 1
new_pso.c1 = 1.5
assert new_pso.c1 == 1.5
new_pso.c2 = 1.5
assert new_pso.c2 == 1.5
def test_pso_run():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
new_pso = pso.PSO()
search_space = search.SearchSpace(n_agents=2, n_iterations=10,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_pso.run(search_space, new_function)
assert len(history.agents) > 0
assert len(history.best_agent) > 0
def test_pso_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_pso = pso.PSO()
local_position = np.zeros((2, 2, 1))
new_pso._evaluate(search_space, new_function, local_position)
assert search_space.best_agent.fit < sys.float_info.max
def test_opytimizer_start():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
lb = [0, 0]
ub = [10, 10]
new_space = search.SearchSpace(lower_bound=lb, upper_bound=ub)
new_pso = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space=new_space,
optimizer=new_pso,
function=new_function)
new_opytimizer.start(history=True)
def test_opytimizer_start():
def square(x):
return np.sum(x**2)
new_function = function.Function(pointer=square)
lb = [0]
ub = [10]
new_space = search.SearchSpace(lower_bound=lb, upper_bound=ub)
new_pso = pso.PSO()
new_opytimizer = opytimizer.Opytimizer(space=new_space,
optimizer=new_pso,
function=new_function)
history = new_opytimizer.start()
assert isinstance(history, opytimizer.utils.history.History)
def test_pso_hyperparams_setter():
new_pso = pso.PSO()
try:
new_pso.w = 'a'
except:
new_pso.w = 1
try:
new_pso.w = -1
except:
new_pso.w = 1
assert new_pso.w == 1
try:
new_pso.c1 = 'b'
except:
new_pso.c1 = 1.5
try:
new_pso.c1 = -1
except:
new_pso.c1 = 1.5
assert new_pso.c1 == 1.5
try:
new_pso.c2 = 'c'
except:
new_pso.c2 = 1.5
try:
new_pso.c2 = -1
except:
new_pso.c2 = 1.5
assert new_pso.c2 == 1.5
def test_pso_update_position():
new_pso = pso.PSO()
position = new_pso._update_position(1, 1)
assert position == 2
def test_pso_update_velocity():
new_pso = pso.PSO()
velocity = new_pso._update_velocity(1, 1, 1, 1)
assert velocity != 0
def test_pso_build():
new_pso = pso.PSO()
assert new_pso.built == True
