def test_ihs_hyperparams_setter(): new_ihs = hs.IHS() try: new_ihs.PAR_min = 'a' except: new_ihs.PAR_min = 0.5 try: new_ihs.PAR_min = -1 except: new_ihs.PAR_min = 0.5 assert new_ihs.PAR_min == 0.5 try: new_ihs.PAR_max = 'b' except: new_ihs.PAR_max = 1.0 try: new_ihs.PAR_max = -1 except: new_ihs.PAR_max = 1.0 try: new_ihs.PAR_max = 0 except: new_ihs.PAR_max = 1.0 assert new_ihs.PAR_max == 1.0 try: new_ihs.bw_min = 'c' except: new_ihs.bw_min = 1.0 try: new_ihs.bw_min = -1 except: new_ihs.bw_min = 1.0 assert new_ihs.bw_min == 1.0 try: new_ihs.bw_max = 'd' except: new_ihs.bw_max = 10.0 try: new_ihs.bw_max = -1 except: new_ihs.bw_max = 10.0 try: new_ihs.bw_max = 0 except: new_ihs.bw_max = 10.0 assert new_ihs.bw_max == 10.0
def test_ihs_params_setter(): new_ihs = hs.IHS() try: new_ihs.PAR_min = "a" except: new_ihs.PAR_min = 0.5 try: new_ihs.PAR_min = -1 except: new_ihs.PAR_min = 0.5 assert new_ihs.PAR_min == 0.5 try: new_ihs.PAR_max = "b" except: new_ihs.PAR_max = 1.0 try: new_ihs.PAR_max = -1 except: new_ihs.PAR_max = 1.0 try: new_ihs.PAR_max = 0 except: new_ihs.PAR_max = 1.0 assert new_ihs.PAR_max == 1.0 try: new_ihs.bw_min = "c" except: new_ihs.bw_min = 1.0 try: new_ihs.bw_min = -1 except: new_ihs.bw_min = 1.0 assert new_ihs.bw_min == 1.0 try: new_ihs.bw_max = "d" except: new_ihs.bw_max = 10.0 try: new_ihs.bw_max = -1 except: new_ihs.bw_max = 10.0 try: new_ihs.bw_max = 0 except: new_ihs.bw_max = 10.0 assert new_ihs.bw_max == 10.0
def test_ihs_update(): def square(x): return np.sum(x**2) new_ihs = hs.IHS() search_space = search.SearchSpace(n_agents=20, n_variables=2, lower_bound=[0, 0], upper_bound=[5, 5]) new_ihs.update(search_space, square, 1, 10)
def test_ihs_hyperparams(): hyperparams = {'PAR_min': 0.5, 'PAR_max': 1, 'bw_min': 2, 'bw_max': 5} new_ihs = hs.IHS(hyperparams=hyperparams) assert new_ihs.PAR_min == 0.5 assert new_ihs.PAR_max == 1 assert new_ihs.bw_min == 2 assert new_ihs.bw_max == 5
def test_ihs_params(): params = {"PAR_min": 0.5, "PAR_max": 1, "bw_min": 2, "bw_max": 5} new_ihs = hs.IHS(params=params) assert new_ihs.PAR_min == 0.5 assert new_ihs.PAR_max == 1 assert new_ihs.bw_min == 2 assert new_ihs.bw_max == 5
def test_ihs_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_ihs = hs.IHS() search_space = search.SearchSpace(n_agents=20, n_iterations=50, n_variables=2, lower_bound=[0, 0], upper_bound=[5, 5]) history = new_ihs.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 ihs failed to converge.'
def test_ihs_rebuild(): new_ihs = hs.IHS() assert new_ihs.built == True