def test_aso_params(): params = {'alpha': 50.0, 'beta': 0.2} new_aso = aso.ASO(params=params) assert new_aso.alpha == 50.0 assert new_aso.beta == 0.2
def test_aso_hyperparams(): hyperparams = {'alpha': 50.0, 'beta': 0.2} new_aso = aso.ASO(hyperparams=hyperparams) assert new_aso.alpha == 50.0 assert new_aso.beta == 0.2
def test_aso_params(): params = {"alpha": 50.0, "beta": 0.2} new_aso = aso.ASO(params=params) assert new_aso.alpha == 50.0 assert new_aso.beta == 0.2
def test_aso_update(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_aso = aso.ASO() new_aso.compile(search_space) new_aso.update(search_space, 1, 10)
def test_aso_calculate_potential(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_aso = aso.ASO() new_aso.compile(search_space) new_aso._calculate_potential(search_space.agents[0], search_space.agents[1], np.array([1]), 1, 10)
def test_aso_calculate_acceleration(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_aso = aso.ASO() new_aso.compile(search_space) mass = new_aso._calculate_mass(search_space.agents) new_aso._calculate_acceleration(search_space.agents, search_space.best_agent, mass, 1, 10)
def test_aso_calculate_mass(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_aso = aso.ASO() new_aso.compile(search_space) mass = new_aso._calculate_mass(search_space.agents) assert mass[0] == 0.1
def test_aso_compile(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) new_aso = aso.ASO() new_aso.compile(search_space) try: new_aso.velocity = 1 except: new_aso.velocity = np.array([1]) assert new_aso.velocity == np.array([1])
def test_aso_hyperparams_setter(): new_aso = aso.ASO() try: new_aso.alpha = 'a' except: new_aso.alpha = 50.0 try: new_aso.beta = 'b' except: new_aso.beta = 0.2 try: new_aso.beta = -1 except: new_aso.beta = 0.2 assert new_aso.beta == 0.2
def test_aso_params_setter(): new_aso = aso.ASO() try: new_aso.alpha = "a" except: new_aso.alpha = 50.0 try: new_aso.beta = "b" except: new_aso.beta = 0.2 try: new_aso.beta = -1 except: new_aso.beta = 0.2 assert new_aso.beta == 0.2
def test_aso_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_aso = aso.ASO() search_space = search.SearchSpace(n_agents=10, n_iterations=100, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_aso.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 aso failed to converge.'
def test_aso_build(): new_aso = aso.ASO() assert new_aso.built == True