def test_find_max_fitness_and_child(self): ga = GASolver(max_generations, population_size, antibody_cnt, nc_cnt, c_cnt, markers, cell_cnt, mu_list, sigma_list) ga.create_random_population() # don't include measured. All the population is measured so return -1 val = ga.find_max_fitness_and_child(0, False) self.assertEqual(val['fitness'], -1) self.assertEqual(val['child'].tolist(), ga.population[0][0].tolist()) # include measured val = ga.find_max_fitness_and_child(0, True) self.assertGreaterEqual(val['fitness'], 0) ga.cross_over_generation(0, ga.population_size / 2) # don't include measured. Some are unmeasured so must be > 0 val = ga.find_max_fitness_and_child(0, False) self.assertGreaterEqual(val['fitness'], 0) val = ga.find_max_fitness_and_child(0, True) self.assertGreaterEqual(val['fitness'], 0)
def test_cross_over_generation(self): ga = GASolver(max_generations, population_size, antibody_cnt, nc_cnt, c_cnt, markers, cell_cnt, mu_list, sigma_list) ga.create_random_population() ga.survive_n_fittest(0, population_size / 2) start_ind = population_size / 2 new_start_ind = ga.cross_over_generation(1, start_ind) self.assertEqual(ga.total_population, population_size * 5 / 4) self.assertEqual(new_start_ind, start_ind * 3 / 2) # half is zero self.assertTrue([0, 0, 0, 0] not in ga.population[0].tolist()) x = [el for el in ga.population[1] if el.tolist() == [0, 0, 0, 0]] self.assertEqual(len(x), population_size / 4)