def test_wca_hyperparams_setter():
new_wca = wca.WCA()
try:
new_wca.nsr = 0.0
except:
new_wca.nsr = 10
try:
new_wca.nsr = 0
except:
new_wca.nsr = 10
assert new_wca.nsr == 10
try:
new_wca.d_max = 'a'
except:
new_wca.d_max = 0.1
try:
new_wca.d_max = -1
except:
new_wca.d_max = 0.1
assert new_wca.d_max == 0.1
def test_wca_hyperparams():
hyperparams = {'nsr': 5, 'd_max': 0.25}
new_wca = wca.WCA(hyperparams=hyperparams)
assert new_wca.nsr == 5
assert new_wca.d_max == 0.25
def test_wca_params():
params = {'nsr': 5, 'd_max': 0.25}
new_wca = wca.WCA(params=params)
assert new_wca.nsr == 5
assert new_wca.d_max == 0.25
def test_wca_flow_intensity():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_wca = wca.WCA()
new_wca.compile(search_space)
def test_wca_params():
params = {"nsr": 5, "d_max": 0.25}
new_wca = wca.WCA(params=params)
assert new_wca.nsr == 5
assert new_wca.d_max == 0.25
def test_wca_update_river():
new_wca = wca.WCA()
search_space = search.SearchSpace(n_agents=20, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
new_wca._update_river(search_space.agents, search_space.best_agent)
assert search_space.agents[1].position[0] != 0
def test_wca_flow_intensity():
new_wca = wca.WCA()
search_space = search.SearchSpace(n_agents=2, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
flows = new_wca._flow_intensity(search_space.agents)
assert flows.shape[0] == new_wca.nsr
def test_wca_raining_process():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_wca = wca.WCA()
new_wca.compile(search_space)
new_wca.flows[0] = 5
new_wca.d_max = 100
new_wca._raining_process(search_space.agents, search_space.best_agent)
def test_wca_update_stream():
new_wca = wca.WCA()
search_space = search.SearchSpace(n_agents=20, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
flows = new_wca._flow_intensity(search_space.agents)
new_wca._update_stream(search_space.agents, flows)
assert search_space.agents[-1].position[0] != 0
def test_wca_raining_process():
new_wca = wca.WCA()
search_space = search.SearchSpace(n_agents=20, n_iterations=100,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
flows = new_wca._flow_intensity(search_space.agents)
new_wca._raining_process(search_space.agents, search_space.best_agent)
assert search_space.agents[-1].position[0] != 0
def test_wca_update():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_wca = wca.WCA()
new_wca.compile(search_space)
new_wca.update(search_space, square, 1)
def test_wca_compile():
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_wca = wca.WCA()
new_wca.compile(search_space)
try:
new_wca.flows = 1
except:
new_wca.flows = np.array([1])
assert new_wca.flows == np.array([1])
def test_wca_update_river():
def square(x):
return np.sum(x**2)
search_space = search.SearchSpace(n_agents=10,
n_variables=2,
lower_bound=[0, 0],
upper_bound=[10, 10])
new_wca = wca.WCA()
new_wca.compile(search_space)
new_wca._update_river(search_space.agents, search_space.best_agent, square)
assert search_space.agents[1].position[0][0] != 0
def test_wca_run():
def square(x):
return np.sum(x**2)
def hook(optimizer, space, function):
return
new_function = function.Function(pointer=square)
new_wca = wca.WCA()
search_space = search.SearchSpace(n_agents=20, n_iterations=10,
n_variables=2, lower_bound=[0, 0],
upper_bound=[10, 10])
history = new_wca.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 wca failed to converge.'
def test_wca_build():
new_wca = wca.WCA()
assert new_wca.built == True
