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_bmrfo_cyclone_foraging():
new_bmrfo = bmrfo.BMRFO()
boolean_space = boolean.BooleanSpace(n_agents=100, n_variables=2)
cyclone = new_bmrfo._cyclone_foraging(boolean_space.agents,
boolean_space.best_agent.position, 0,
1, 100)
assert cyclone[0].item() is False or cyclone[0].item() is True
cyclone = new_bmrfo._cyclone_foraging(boolean_space.agents,
boolean_space.best_agent.position, 1,
1, 100)
assert cyclone[0].item() is False or cyclone[0].item() is True
cyclone = new_bmrfo._cyclone_foraging(boolean_space.agents,
boolean_space.best_agent.position, 0,
1, 1)
assert cyclone[0].item() is False or cyclone[0].item() is True
cyclone = new_bmrfo._cyclone_foraging(boolean_space.agents,
boolean_space.best_agent.position, 1,
1, 1)
assert cyclone[0].item() is False or cyclone[0].item() is True
def test_boolean_initialize_agents():
new_boolean_space = boolean.BooleanSpace(1, 1)
assert (
new_boolean_space.agents[0].position[0][0] == 0
or new_boolean_space.agents[0].position[0][0] == 1
)
def test_boolean_clip_limits():
new_boolean_space = boolean.BooleanSpace()
new_boolean_space.agents[0].position[0][0] = 20
new_boolean_space.clip_limits()
assert new_boolean_space.agents[0].position[0][0] == 1
def test_umda_calculate_probability():
new_umda = umda.UMDA()
boolean_space = boolean.BooleanSpace(n_agents=5, n_variables=2)
probs = new_umda._calculate_probability(boolean_space.agents)
assert probs.shape == (2, 1)
def test_boolean_clip_by_bound():
new_boolean_space = boolean.BooleanSpace(1, 1)
new_boolean_space.agents[0].position[0][0] = 20
new_boolean_space.clip_by_bound()
assert new_boolean_space.agents[0].position[0][0] == 1
def test_bmrfo_somersault_foraging():
new_bmrfo = bmrfo.BMRFO()
boolean_space = boolean.BooleanSpace(n_agents=100, n_variables=2)
somersault = new_bmrfo._somersault_foraging(
boolean_space.agents[0].position, boolean_space.best_agent.position)
assert somersault[0].item() is False or somersault[0].item() is True
def test_bmrfo_update():
new_function = function.Function(pointer=Knapsack(
values=(55, 10, 47, 5, 4), weights=(95, 4, 60, 32, 23), max_capacity=100))
new_bmrfo = bmrfo.BMRFO()
boolean_space = boolean.BooleanSpace(n_agents=100, n_variables=5)
new_bmrfo.update(boolean_space, new_function, 1, 20)
def test_bmrfo_chain_foraging():
new_bmrfo = bmrfo.BMRFO()
boolean_space = boolean.BooleanSpace(n_agents=100, n_variables=2)
chain = new_bmrfo._chain_foraging(boolean_space.agents,
boolean_space.best_agent.position, 0)
assert chain[0].item() is False or chain[0].item() is True
def test_udma_calculate_probability():
new_udma = udma.UDMA()
boolean_space = boolean.BooleanSpace(n_agents=5,
n_iterations=20,
n_variables=2)
probs = new_udma._calculate_probability(boolean_space.agents)
assert probs.shape == (2, 1)
def test_bmrfo_chain_foraging():
new_bmrfo = bmrfo.BMRFO()
boolean_space = boolean.BooleanSpace(n_agents=5,
n_iterations=20,
n_variables=2)
chain = new_bmrfo._chain_foraging(boolean_space.agents,
boolean_space.best_agent.position, 1)
assert chain[0] == False or chain[0] == True
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_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_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_umda_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_umda = umda.UMDA()
boolean_space = boolean.BooleanSpace(
n_agents=2, n_iterations=10, n_variables=5)
history = new_umda.run(boolean_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 umda failed to converge.'
def test_udma_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_udma = udma.UDMA()
boolean_space = boolean.BooleanSpace(n_agents=5,
n_iterations=20,
n_variables=2)
history = new_udma.run(boolean_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 udma failed to converge.'
def test_umda_update():
new_umda = umda.UMDA()
boolean_space = boolean.BooleanSpace(n_agents=2, n_variables=5)
new_umda.update(boolean_space)
