def test_bsa_params(): params = {"F": 3.0, "mix_rate": 1} new_bsa = bsa.BSA(params=params) assert new_bsa.F == 3.0 assert new_bsa.mix_rate == 1
def test_bsa_hyperparams(): hyperparams = {'F': 3.0, 'mix_rate': 1} new_bsa = bsa.BSA(hyperparams=hyperparams) assert new_bsa.F == 3.0 assert new_bsa.mix_rate == 1
def test_bsa_params(): params = {'F': 3.0, 'mix_rate': 1} new_bsa = bsa.BSA(params=params) assert new_bsa.F == 3.0 assert new_bsa.mix_rate == 1
def test_bsa_permute(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_bsa = bsa.BSA() new_bsa.compile(search_space) new_bsa._permute(search_space.agents)
def test_bsa_crossover(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_bsa = bsa.BSA() new_bsa.compile(search_space) trial_agents = new_bsa._mutate(search_space.agents) new_bsa._crossover(search_space.agents, trial_agents)
def test_bsa_mutate(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_bsa = bsa.BSA() new_bsa.compile(search_space) trial_agents = new_bsa._mutate(search_space.agents) assert len(trial_agents) == 10
def test_bsa_update(): def square(x): return np.sum(x**2) search_space = search.SearchSpace(n_agents=75, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_bsa = bsa.BSA() new_bsa.compile(search_space) new_bsa.update(search_space, square)
def test_bsa_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_bsa = bsa.BSA() new_bsa.compile(search_space) try: new_bsa.old_agents = 1 except: new_bsa.old_agents = [] assert new_bsa.old_agents == []
def test_bsa_params_setter(): new_bsa = bsa.BSA() try: new_bsa.F = "a" except: new_bsa.F = 3.0 try: new_bsa.mix_rate = "b" except: new_bsa.mix_rate = 1 try: new_bsa.mix_rate = -1 except: new_bsa.mix_rate = 1 assert new_bsa.mix_rate == 1
def test_bsa_hyperparams_setter(): new_bsa = bsa.BSA() try: new_bsa.F = 'a' except: new_bsa.F = 3.0 try: new_bsa.mix_rate = 'b' except: new_bsa.mix_rate = 1 try: new_bsa.mix_rate = -1 except: new_bsa.mix_rate = 1 assert new_bsa.mix_rate == 1
def test_bsa_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_bsa = bsa.BSA() search_space = search.SearchSpace(n_agents=10, n_iterations=100, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_bsa.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 bsa failed to converge.'
def test_bsa_build(): new_bsa = bsa.BSA() assert new_bsa.built == True