def test_gco_hyperparams_setter(): new_gco = gco.GCO() try: new_gco.CR = 'a' except: new_gco.CR = 0.75 try: new_gco.CR = -1 except: new_gco.CR = 0.75 assert new_gco.CR == 0.75 try: new_gco.F = 'b' except: new_gco.F = 1.5 try: new_gco.F = -1 except: new_gco.F = 1.5 assert new_gco.F == 1.5
def test_gco_params_setter(): new_gco = gco.GCO() try: new_gco.CR = "a" except: new_gco.CR = 0.75 try: new_gco.CR = -1 except: new_gco.CR = 0.75 assert new_gco.CR == 0.75 try: new_gco.F = "b" except: new_gco.F = 1.5 try: new_gco.F = -1 except: new_gco.F = 1.5 assert new_gco.F == 1.5
def test_gco_params(): params = {"CR": 0.7, "F": 1.25} new_gco = gco.GCO(params=params) assert new_gco.CR == 0.7 assert new_gco.F == 1.25
def test_gco_params(): params = {'CR': 0.7, 'F': 1.25} new_gco = gco.GCO(params=params) assert new_gco.CR == 0.7 assert new_gco.F == 1.25
def test_gco_light_zone(): search_space = search.SearchSpace(n_agents=4, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_gco = gco.GCO() new_gco.compile(search_space) new_gco._light_zone(search_space.agents)
def test_gco_hyperparams(): hyperparams = { 'CR': 0.7, 'F': 1.25 } new_gco = gco.GCO(hyperparams=hyperparams) assert new_gco.CR == 0.7 assert new_gco.F == 1.25
def test_gco_mutate_cell(): new_gco = gco.GCO() search_space = search.SearchSpace(n_agents=4, n_iterations=10, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) cell = new_gco._mutate_cell( search_space.agents[0], search_space.agents[1], search_space.agents[2], search_space.agents[3]) assert cell.position[0][0] != 0
def test_gco_dark_zone(): def square(x): return np.sum(x**2) search_space = search.SearchSpace(n_agents=4, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_gco = gco.GCO() new_gco.compile(search_space) new_gco._dark_zone(search_space.agents, square)
def test_gco_update(): def square(x): return np.sum(x**2) new_function = function.Function(pointer=square) new_gco = gco.GCO() search_space = search.SearchSpace(n_agents=4, n_iterations=10, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_gco._update(search_space.agents, new_function, np.array([70, 70, 70, 70]), np.array([1, 1, 1, 1])) assert search_space.agents[0].position[0] != 0
def test_gco_update(): def square(x): return np.sum(x**2) search_space = search.SearchSpace(n_agents=4, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_gco = gco.GCO() new_gco.compile(search_space) new_gco.update(search_space, square) assert search_space.agents[0].position[0] != 0
def test_gco_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_gco = gco.GCO() search_space = search.SearchSpace(n_agents=10, n_iterations=30, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_gco.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 gco failed to converge.'
def test_gco_compile(): search_space = search.SearchSpace(n_agents=4, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_gco = gco.GCO() new_gco.compile(search_space) try: new_gco.life = 1 except: new_gco.life = np.array([1]) assert new_gco.life == np.array([1]) try: new_gco.counter = 1 except: new_gco.counter = np.array([1]) assert new_gco.counter == np.array([1])
def test_gco_build(): new_gco = gco.GCO() assert new_gco.built == True