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)