def test_mrfo_hyperparams():
hyperparams = {
'S': 2.0
}
new_mrfo = mrfo.MRFO(hyperparams=hyperparams)
assert new_mrfo.S == 2.0
def test_mrfo_chain_foraging():
new_mrfo = mrfo.MRFO()
search_space = search.SearchSpace(n_agents=5, n_iterations=20,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
chain = new_mrfo._chain_foraging(search_space.agents, search_space.best_agent.position, 1)
assert chain[0] != 0
def test_mrfo_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(
n_agents=5, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
new_mrfo = mrfo.MRFO()
new_mrfo.update(search_space, square, 1, 10)
def test_mrfo_cyclone_foraging():
new_mrfo = mrfo.MRFO()
search_space = search.SearchSpace(
n_agents=5, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]
)
cyclone = new_mrfo._cyclone_foraging(
search_space.agents, search_space.best_agent.position, 1, 1, 20
)
assert cyclone[0] != 0
def test_mrfo_params_setter():
new_mrfo = mrfo.MRFO()
try:
new_mrfo.S = 'a'
except:
new_mrfo.S = 2.0
try:
new_mrfo.S = -1
except:
new_mrfo.S = 2.0
assert new_mrfo.S == 2.0
def test_mrfo_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_mrfo = mrfo.MRFO()
search_space = search.SearchSpace(n_agents=5, n_iterations=20,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_mrfo.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 mrfo failed to converge.'
def test_mrfo_params():
params = {'S': 2.0}
new_mrfo = mrfo.MRFO(params=params)
assert new_mrfo.S == 2.0
def test_mrfo_somersault_foraging():
new_mrfo = mrfo.MRFO()
somersault = new_mrfo._somersault_foraging(1, 1)
assert somersault != 0
def test_mrfo_build():
new_mrfo = mrfo.MRFO()
assert new_mrfo.built == True
