def test_ga_roulette_selection(): new_ga = ga.GA() fitness = [10, 20, 30, 40, 50] idx = new_ga._roulette_selection(len(fitness), fitness) assert len(idx) == 4
def test_ga_update(): def square(x): return np.sum(x**2) new_ga = ga.GA() search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_ga.update(search_space, square)
def test_ga_mutation(): search_space = search.SearchSpace(n_agents=10, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_ga = ga.GA() alpha, beta = new_ga._mutation( search_space.agents[0], search_space.agents[1]) assert type(alpha).__name__ == 'Agent' assert type(beta).__name__ == 'Agent'
def test_ga_hyperparams(): hyperparams = { 'p_selection': 0.75, 'p_mutation': 0.25, 'p_crossover': 0.5, } new_ga = ga.GA(hyperparams=hyperparams) assert new_ga.p_selection == 0.75 assert new_ga.p_mutation == 0.25 assert new_ga.p_crossover == 0.5
def test_ga_params(): params = { "p_selection": 0.75, "p_mutation": 0.25, "p_crossover": 0.5, } new_ga = ga.GA(params=params) assert new_ga.p_selection == 0.75 assert new_ga.p_mutation == 0.25 assert new_ga.p_crossover == 0.5
def test_ga_update(): def square(x): return np.sum(x**2) new_function = function.Function(pointer=square) new_ga = ga.GA() search_space = search.SearchSpace(n_agents=10, n_iterations=10, n_variables=2, lower_bound=[1, 1], upper_bound=[10, 10]) new_ga._evaluate(search_space, new_function) new_ga._update(search_space.agents, new_function) assert search_space.agents[0].position[0] != 0
def test_ga_hyperparams_setter(): new_ga = ga.GA() try: new_ga.p_selection = 'a' except: new_ga.p_selection = 0.75 try: new_ga.p_selection = -1 except: new_ga.p_selection = 0.75 assert new_ga.p_selection == 0.75 try: new_ga.p_mutation = 'b' except: new_ga.p_mutation = 0.25 try: new_ga.p_mutation = -1 except: new_ga.p_mutation = 0.25 assert new_ga.p_mutation == 0.25 try: new_ga.p_crossover = 'c' except: new_ga.p_crossover = 0.5 try: new_ga.p_crossover = -1 except: new_ga.p_crossover = 0.5 assert new_ga.p_crossover == 0.5
def test_ga_params_setter(): new_ga = ga.GA() try: new_ga.p_selection = "a" except: new_ga.p_selection = 0.75 try: new_ga.p_selection = -1 except: new_ga.p_selection = 0.75 assert new_ga.p_selection == 0.75 try: new_ga.p_mutation = "b" except: new_ga.p_mutation = 0.25 try: new_ga.p_mutation = -1 except: new_ga.p_mutation = 0.25 assert new_ga.p_mutation == 0.25 try: new_ga.p_crossover = "c" except: new_ga.p_crossover = 0.5 try: new_ga.p_crossover = -1 except: new_ga.p_crossover = 0.5 assert new_ga.p_crossover == 0.5
def test_ga_run(): def square(x): return np.sum(x**2) def hook(optimizer, space, function): return new_function = function.Function(pointer=square) new_ga = ga.GA() search_space = search.SearchSpace(n_agents=10, n_iterations=30, n_variables=2, lower_bound=[0, 0], upper_bound=[10, 10]) history = new_ga.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 ga failed to converge.'
def test_ga_build(): new_ga = ga.GA() assert new_ga.built == True