def test_cs_hyperparams(): hyperparams = {'alpha': 1.0, 'beta': 1.5, 'p': 0.2} new_cs = cs.CS(hyperparams=hyperparams) assert new_cs.alpha == 1.0 assert new_cs.beta == 1.5 assert new_cs.p == 0.2
def test_cs_params(): params = {"alpha": 1.0, "beta": 1.5, "p": 0.2} new_cs = cs.CS(params=params) assert new_cs.alpha == 1.0 assert new_cs.beta == 1.5 assert new_cs.p == 0.2
def test_cs_generate_abandoned_nests(): search_space = search.SearchSpace(n_agents=20, n_variables=2, lower_bound=[-10, -10], upper_bound=[10, 10]) new_cs = cs.CS() new_agents = new_cs._generate_abandoned_nests(search_space.agents, 0.5) assert len(new_agents) == 20
def test_cs_update(): def square(x): return np.sum(x**2) search_space = search.SearchSpace(n_agents=20, n_variables=2, lower_bound=[-10, -10], upper_bound=[10, 10]) new_cs = cs.CS() new_cs.update(search_space, square)
def test_cs_evaluate_nests(): def square(x): return np.sum(x**2) search_space = search.SearchSpace(n_agents=20, n_variables=2, lower_bound=[-10, -10], upper_bound=[10, 10]) new_cs = cs.CS() new_agents = new_cs._generate_abandoned_nests(search_space.agents, 0.5) new_cs._evaluate_nests(search_space.agents, new_agents, square)
def test_cs_update(): def square(x): return np.sum(x**2) new_function = function.Function(pointer=square) new_cs = cs.CS() search_space = search.SearchSpace(n_agents=20, n_iterations=100, n_variables=2, lower_bound=[-10, -10], upper_bound=[10, 10]) new_cs._update(search_space.agents, search_space.best_agent, new_function) assert search_space.agents[0].position[0] != 0
def test_cs_params_setter(): new_cs = cs.CS() try: new_cs.alpha = "a" except: new_cs.alpha = 0.001 try: new_cs.alpha = -1 except: new_cs.alpha = 0.001 assert new_cs.alpha == 0.001 try: new_cs.beta = "b" except: new_cs.beta = 0.75 try: new_cs.beta = -1 except: new_cs.beta = 0.75 assert new_cs.beta == 0.75 try: new_cs.p = "c" except: new_cs.p = 0.25 try: new_cs.p = -1 except: new_cs.p = 0.25 assert new_cs.p == 0.25
def test_cs_hyperparams_setter(): new_cs = cs.CS() try: new_cs.alpha = 'a' except: new_cs.alpha = 0.001 try: new_cs.alpha = -1 except: new_cs.alpha = 0.001 assert new_cs.alpha == 0.001 try: new_cs.beta = 'b' except: new_cs.beta = 0.75 try: new_cs.beta = -1 except: new_cs.beta = 0.75 assert new_cs.beta == 0.75 try: new_cs.p = 'c' except: new_cs.p = 0.25 try: new_cs.p = -1 except: new_cs.p = 0.25 assert new_cs.p == 0.25
def test_cs_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_cs = cs.CS() search_space = search.SearchSpace(n_agents=25, n_iterations=30, n_variables=2, lower_bound=[-10, -10], upper_bound=[10, 10]) history = new_cs.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 abc failed to converge.'
def test_cs_build(): new_cs = cs.CS() assert new_cs.built == True