def test_boa_update(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_boa = boa.BOA() new_boa.compile(search_space) new_boa.update(search_space)
def test_boa_local_movement(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_boa = boa.BOA() new_boa.compile(search_space) new_boa._local_movement(search_space.agents[0].position, search_space.agents[1].position, search_space.agents[2].position, new_boa.fragrance[0], 0.5)
def test_boa_params(): params = {"c": 0.01, "a": 0.1, "p": 0.8} new_boa = boa.BOA(params=params) assert new_boa.c == 0.01 assert new_boa.a == 0.1 assert new_boa.p == 0.8
def test_boa_hyperparams(): hyperparams = {'c': 0.01, 'a': 0.1, 'p': 0.8} new_boa = boa.BOA(hyperparams=hyperparams) assert new_boa.c == 0.01 assert new_boa.a == 0.1 assert new_boa.p == 0.8
def test_boa_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_boa = boa.BOA() new_boa.compile(search_space) try: new_boa.fragrance = 1 except: new_boa.fragrance = np.array([1]) assert new_boa.fragrance == np.array([1])
def test_boa_params(): params = { 'c': 0.01, 'a': 0.1, 'p': 0.8 } new_boa = boa.BOA(params=params) assert new_boa.c == 0.01 assert new_boa.a == 0.1 assert new_boa.p == 0.8
def test_boa_params_setter(): new_boa = boa.BOA() try: new_boa.c = 'a' except: new_boa.c = 0.01 try: new_boa.c = -1 except: new_boa.c = 0.01 assert new_boa.c == 0.01 try: new_boa.a = 'b' except: new_boa.a = 0.1 try: new_boa.a = -1 except: new_boa.a = 0.1 assert new_boa.a == 0.1 try: new_boa.p = 'c' except: new_boa.p = 0.8 try: new_boa.p = -1 except: new_boa.p = 0.8 assert new_boa.p == 0.8
def test_boa_params_setter(): new_boa = boa.BOA() try: new_boa.c = "a" except: new_boa.c = 0.01 try: new_boa.c = -1 except: new_boa.c = 0.01 assert new_boa.c == 0.01 try: new_boa.a = "b" except: new_boa.a = 0.1 try: new_boa.a = -1 except: new_boa.a = 0.1 assert new_boa.a == 0.1 try: new_boa.p = "c" except: new_boa.p = 0.8 try: new_boa.p = -1 except: new_boa.p = 0.8 assert new_boa.p == 0.8
def test_boa_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_boa = boa.BOA() search_space = search.SearchSpace(n_agents=10, n_iterations=100, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_boa.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 boa failed to converge.'
def test_boa_build(): new_boa = boa.BOA() assert new_boa.built == True