def test_lsa_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_lsa = lsa.LSA() new_lsa.compile(search_space) try: new_lsa.time = 'a' except: new_lsa.time = 0 assert new_lsa.time == 0 try: new_lsa.time = -1 except: new_lsa.time = 0 assert new_lsa.time == 0 try: new_lsa.direction = 1 except: new_lsa.direction = np.array([1]) assert new_lsa.direction == np.array([1])
def test_lsa_params(): params = {"max_time": 10, "E": 2.05, "p_fork": 0.01} new_lsa = lsa.LSA(params=params) assert new_lsa.max_time == 10 assert new_lsa.E == 2.05 assert new_lsa.p_fork == 0.01
def test_lsa_update_direction(): 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_lsa = lsa.LSA() new_lsa.compile(search_space) new_lsa._update_direction(search_space.agents[0], square)
def test_lsa_params(): params = { 'max_time': 10, 'E': 2.05, 'p_fork': 0.01 } new_lsa = lsa.LSA(params=params) assert new_lsa.max_time == 10 assert new_lsa.E == 2.05 assert new_lsa.p_fork == 0.01
def test_lsa_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_lsa = lsa.LSA() new_lsa.compile(search_space) new_lsa.p_fork = 1 new_lsa.update(search_space, square, 1, 10) new_lsa.time = 11 new_lsa.update(search_space, square, 1, 10)
def test_lsa_params_setter(): new_lsa = lsa.LSA() try: new_lsa.max_time = 'a' except: new_lsa.max_time = 10 assert new_lsa.max_time == 10 try: new_lsa.max_time = -1 except: new_lsa.max_time = 10 assert new_lsa.max_time == 10 try: new_lsa.E = 'b' except: new_lsa.E = 2.05 assert new_lsa.E == 2.05 try: new_lsa.E = -1 except: new_lsa.E = 2.05 assert new_lsa.E == 2.05 try: new_lsa.p_fork = 'c' except: new_lsa.p_fork = 0.01 assert new_lsa.p_fork == 0.01 try: new_lsa.p_fork = -1 except: new_lsa.p_fork = 0.01 assert new_lsa.p_fork == 0.01