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