def test_ep_params_setter(): new_ep = ep.EP() try: new_ep.bout_size = "a" except: new_ep.bout_size = 0.5 try: new_ep.bout_size = -1 except: new_ep.bout_size = 0.5 assert new_ep.bout_size == 0.5 try: new_ep.clip_ratio = "b" except: new_ep.clip_ratio = 0.5 try: new_ep.clip_ratio = -1 except: new_ep.clip_ratio = 0.5 assert new_ep.clip_ratio == 0.5
def test_ep_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) hyperparams = {'bout_size': 0.1, 'clip_ratio': 0.05} new_ep = ep.EP(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_ep.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 de failed to converge.'
def test_ep_hyperparams_setter(): new_ep = ep.EP() try: new_ep.bout_size = 'a' except: new_ep.bout_size = 0.5 try: new_ep.bout_size = -1 except: new_ep.bout_size = 0.5 assert new_ep.bout_size == 0.5 try: new_ep.clip_ratio = 'b' except: new_ep.clip_ratio = 0.5 try: new_ep.clip_ratio = -1 except: new_ep.clip_ratio = 0.5 assert new_ep.clip_ratio == 0.5
def test_ep_hyperparams(): hyperparams = {'bout_size': 0.1, 'clip_ratio': 0.05} new_ep = ep.EP(hyperparams=hyperparams) assert new_ep.bout_size == 0.1 assert new_ep.clip_ratio == 0.05
def test_ep_update_strategy(): new_ep = ep.EP() strategy = np.zeros((4, 1)) new_strategy = new_ep._update_strategy(strategy, [1], [2]) assert new_strategy[0][0] > 0
def test_ep_params(): params = {"bout_size": 0.1, "clip_ratio": 0.05} new_ep = ep.EP(params=params) assert new_ep.bout_size == 0.1 assert new_ep.clip_ratio == 0.05
def test_ep_update_strategy(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_ep = ep.EP() new_ep.compile(search_space) new_ep._update_strategy(0, [1], [2]) assert new_ep.strategy[0][0] > 0
def test_ep_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_ep = ep.EP() new_ep.compile(search_space) new_ep.update(search_space, square)
def test_ep_params(): params = { 'bout_size': 0.1, 'clip_ratio': 0.05 } new_ep = ep.EP(params=params) assert new_ep.bout_size == 0.1 assert new_ep.clip_ratio == 0.05
def test_ep_mutate_parent(): 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_ep = ep.EP() new_ep.compile(search_space) agent = new_ep._mutate_parent(search_space.agents[0], 0, square) assert agent.position[0][0] > 0
def test_ep_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_ep = ep.EP() new_ep.compile(search_space) try: new_ep.strategy = 1 except: new_ep.strategy = np.array([1]) assert new_ep.strategy == np.array([1])
def test_ep_mutate_parent(): def square(x): return np.sum(x**2) new_ep = ep.EP() search_space = search.SearchSpace(n_agents=4, n_iterations=100, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) strategy = np.zeros(4) agent = new_ep._mutate_parent(search_space.agents[0], square, strategy[0]) assert agent.position[0][0] > 0
def test_ep_build(): new_ep = ep.EP() assert new_ep.built == True