def test_cem_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) hyperparams = {'n_updates': 5, 'alpha': 0.7} new_cem = cem.CEM(hyperparams=hyperparams) search_space = search.SearchSpace(n_agents=10, n_iterations=100, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_cem.run(search_space, new_function, pre_evaluation=hook) history = new_cem.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 cem failed to converge.'
def test_cem_params_setter(): new_cem = cem.CEM() try: new_cem.n_updates = 'a' except: new_cem.n_updates = 10 try: new_cem.n_updates = -1 except: new_cem.n_updates = 10 assert new_cem.n_updates == 10 try: new_cem.alpha = 'b' except: new_cem.alpha = 0.5 try: new_cem.alpha = -1 except: new_cem.alpha = 0.5 assert new_cem.alpha == 0.5
def test_cem_update_std(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_cem = cem.CEM() new_cem.compile(search_space) new_cem._update_std(np.array([1, 1])) assert new_cem.std[0] != 0
def test_cem_create_new_samples(): 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_cem = cem.CEM() new_cem.compile(search_space) new_cem._create_new_samples(search_space.agents, square)
def test_cem_params(): params = { 'n_updates': 5, 'alpha': 0.7, } new_cem = cem.CEM(params=params) assert new_cem.n_updates == 5 assert new_cem.alpha == 0.7
def test_cem_hyperparams(): hyperparams = { 'n_updates': 5, 'alpha': 0.7, } new_cem = cem.CEM(hyperparams=hyperparams) assert new_cem.n_updates == 5 assert new_cem.alpha == 0.7
def test_cem_params(): params = { "n_updates": 5, "alpha": 0.7, } new_cem = cem.CEM(params=params) assert new_cem.n_updates == 5 assert new_cem.alpha == 0.7
def test_cem_update(): def square(x): return np.sum(x**2) new_function = function.Function(pointer=square) search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_cem = cem.CEM() new_cem.compile(search_space) new_cem.update(search_space, new_function)
def test_cem_create_new_samples(): def square(x): return np.sum(x**2) new_cem = cem.CEM() search_space = search.SearchSpace(n_agents=10, n_iterations=100, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_cem._create_new_samples(search_space.agents, square, np.array([1, 1]), np.array([1, 1]))
def test_cem_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_cem = cem.CEM() new_cem.compile(search_space) try: new_cem.mean = 1 except: new_cem.mean = np.array([1]) assert new_cem.mean == 1 try: new_cem.std = 1 except: new_cem.std = np.array([1]) assert new_cem.std == 1
def test_cem_update_std(): new_cem = cem.CEM() std = new_cem._update_std(np.array([1, 1]), 1, 0.25) assert std != 0
def test_cem_update_mean(): new_cem = cem.CEM() mean = new_cem._update_mean(np.array([1, 1]), 1) assert mean != 0
def test_cem_build(): new_cem = cem.CEM() assert new_cem.built == True