def test_ba_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) hyperparams = { 'f_min': 0, 'f_max': 2, 'A': 1, 'r': 0.5 } new_ba = ba.BA(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_ba.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 ba failed to converge.'
def test_ba_hyperparams_setter(): new_ba = ba.BA() try: new_ba.f_min = 'a' except: new_ba.f_min = 0.1 try: new_ba.f_min = -1 except: new_ba.f_min = 0.1 assert new_ba.f_min == 0.1 try: new_ba.f_max = 'b' except: new_ba.f_max = 2 try: new_ba.f_max = -1 except: new_ba.f_max = 2 try: new_ba.f_max = 0 except: new_ba.f_max = 2 assert new_ba.f_max == 2 try: new_ba.A = 'c' except: new_ba.A = 0.5 try: new_ba.A = -1 except: new_ba.A = 0.5 assert new_ba.A == 0.5 try: new_ba.r = 'd' except: new_ba.r = 0.5 try: new_ba.r = -1 except: new_ba.r = 0.5 assert new_ba.r == 0.5
def test_ba_params_setter(): new_ba = ba.BA() try: new_ba.f_min = "a" except: new_ba.f_min = 0.1 try: new_ba.f_min = -1 except: new_ba.f_min = 0.1 assert new_ba.f_min == 0.1 try: new_ba.f_max = "b" except: new_ba.f_max = 2 try: new_ba.f_max = -1 except: new_ba.f_max = 2 try: new_ba.f_max = 0 except: new_ba.f_max = 2 assert new_ba.f_max == 2 try: new_ba.A = "c" except: new_ba.A = 0.5 try: new_ba.A = -1 except: new_ba.A = 0.5 assert new_ba.A == 0.5 try: new_ba.r = "d" except: new_ba.r = 0.5 try: new_ba.r = -1 except: new_ba.r = 0.5 assert new_ba.r == 0.5
def test_ba_params(): params = {'f_min': 0, 'f_max': 2, 'A': 0.5, 'r': 0.5} new_ba = ba.BA(params=params) assert new_ba.f_min == 0 assert new_ba.f_max == 2 assert new_ba.A == 0.5 assert new_ba.r == 0.5
def test_ba_params(): params = {"f_min": 0, "f_max": 2, "A": 0.5, "r": 0.5} new_ba = ba.BA(params=params) assert new_ba.f_min == 0 assert new_ba.f_max == 2 assert new_ba.A == 0.5 assert new_ba.r == 0.5
def test_ba_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_ba = ba.BA() new_ba.compile(search_space) new_ba.update(search_space, square, 1)
def test_ba_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_ba = ba.BA() new_ba.compile(search_space) try: new_ba.frequency = 1 except: new_ba.frequency = np.array([1]) assert new_ba.frequency == np.array([1]) try: new_ba.velocity = 1 except: new_ba.velocity = np.array([1]) assert new_ba.velocity == np.array([1]) try: new_ba.loudness = 1 except: new_ba.loudness = np.array([1]) assert new_ba.loudness == np.array([1]) try: new_ba.pulse_rate = 1 except: new_ba.pulse_rate = np.array([1]) assert new_ba.pulse_rate == np.array([1])
def test_ba_update_frequency(): new_ba = ba.BA() frequency = new_ba._update_frequency(0, 2) assert frequency != 0
def test_ba_build(): new_ba = ba.BA() assert new_ba.built == True
def test_ba_update_position(): new_ba = ba.BA() position = new_ba._update_position(1, 1) assert position == 2
def test_ba_update_velocity(): new_ba = ba.BA() velocity = new_ba._update_velocity(1, 1, 1, 1) assert velocity != 0