How to use the cameo.util.RandomGenerator function in cameo

def test_set_generator(self):
        random = Random(SEED)
        representation = ["a", "b", "c", "d", "e", "f"]
        max_size = 5
        variable_size = False
        expected = [[0, 1, 2, 4, 5],
                    [0, 2, 3, 4, 5],
                    [0, 1, 2, 3, 5],
                    [1, 2, 3, 4, 5],
                    [0, 2, 3, 4, 5]]

        for i in range(len(expected)):
            candidate = set_generator(random, dict(representation=representation,
                                                   max_size=max_size,
                                                   variable_size=variable_size))
            assert candidate == expected[i]

def test_multiple_chromosome_set_indel(self):
        genome = MultipleChromosomeGenome(["A", "B"])
        genome["A"] = [1, 2, 3, 4]
        genome["B"] = [1, 5, 7, 10]
        representation = list(range(10))
        args = {
            "A_representation": representation,
            "B_representation": representation,
            "A_indel_rate": 1,
            "B_indel_rate": 1
        }

        random = Random(SEED)
        new_individuals = multiple_chromosome_set_indel(random, [genome for _ in range(5)], args)
        assert new_individuals[0]["A"] == OrderedSet([1, 2, 3, 4, 7])
        assert new_individuals[0]["B"] == OrderedSet([1, 5, 10])
        assert new_individuals[1]["A"] == OrderedSet([2, 3, 4])
        assert new_individuals[1]["B"] == OrderedSet([1, 5, 7, 8, 10])
        assert new_individuals[2]["A"] == OrderedSet([1, 2, 3, 4, 6])
        assert new_individuals[2]["B"] == OrderedSet([1, 5, 7])
        assert new_individuals[3]["A"] == OrderedSet([1, 2, 3, 4, 8])
        assert new_individuals[3]["B"] == OrderedSet([0, 1, 5, 7, 10])
        assert new_individuals[4]["A"] == OrderedSet([1, 2, 3, 4, 7])
        assert new_individuals[4]["B"] == OrderedSet([1, 5, 7, 8, 10])

def test_random(self):
        random = RandomGenerator()
        for _ in range(1000):
            assert random.random() >= 0
            assert random.random() <= 1

def test_reaction_result(self, model):
        representation = [r.id for r in model.reactions]
        random = Random(SEED)
        args = {"representation": representation}

        solutions = BestSolutionArchive()
        for _ in range(10000):
            solutions.add(set_generator(random, args), random.random(), None, True, 100)

        decoder = ReactionSetDecoder(representation, model)

        result = TargetOptimizationResult(
            model=model,
            heuristic_method=None,
            simulation_method=fba,
            simulation_kwargs=None,
            solutions=solutions,
            objective_function=None,
            target_type="reaction",

def test_set_n_point_crossover(self):
        mom = OrderedSet([1, 3, 5, 9, 10])
        dad = OrderedSet([2, 3, 7, 8])
        args = {
            "crossover_rate": 1.0,
            "num_crossover_points": 1,
            "candidate_size": 10
        }
        children = set_n_point_crossover(Random(SEED), [mom, dad], args)
        bro = OrderedSet([1, 3, 5, 8])
        sis = OrderedSet([2, 3, 7, 9, 10])
        assert bro == children[0]
        assert sis == children[1]

def test_multiple_chromosome_set_generator(self):
        random = Random(SEED)
        args = dict(keys=["test_key_1", "test_key_2"],
                    test_key_1_representation=["a1", "a2", "a3", "a4", "a5"],
                    test_key_2_representation=["b1", "b2", "b3", "b4", "b5", "b6", "b7"],
                    test_key_1_max_size=3,
                    test_key_2_max_size=5,
                    variable_size=False)
        candidate = multiple_chromosome_set_generator(random, args)
        assert len(candidate['test_key_1']) == 3
        assert len(candidate['test_key_2']) == 5

def test_migrate_individuals_without_evaluation(self):
        population = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
        random = Random(SEED)
        migrator = MultiprocessingMigrator(max_migrants=1, host=REDIS_HOST)
        assert isinstance(migrator.migrants, RedisQueue)
        assert migrator.max_migrants == 1

        migrator(random, population, {})
        assert len(migrator.migrants) == 1

        migrator(random, population, {})
        assert len(migrator.migrants) == 1

def test_set_mutation(self):
        individual = OrderedSet([1, 3, 5, 9, 10])
        representation = list(range(10))
        args = {
            "representation": representation,
            "mutation_rate": 1.0
        }
        new_individuals = set_mutation(Random(SEED), [individual], args)
        assert len(new_individuals[0]) == len(individual)
        assert new_individuals[0] != individual
        assert new_individuals[0] == [0, 2, 4, 6, 7]

def test_seeded_methods(self):
        random = RandomGenerator()

        random.seed(SEED)
        value = random.random()
        random.seed(SEED)
        assert value == random.random()

        random.seed(SEED)
        value = random.randint(1, 10)
        random.seed(SEED)
        assert value == random.randint(1, 10)

        random.seed(SEED)
        population = [1, 2, 3, 4, 5]
        value = random.sample(population, 2)
        random.seed(SEED)
        assert value == random.sample(population, 2)

individual = [1, 3, 5, 9, 10]
        representation = list(range(10))
        args = {
            "representation": representation,
            "indel_rate": 0.0
        }
        new_individuals = set_indel(Random(SEED), [individual], args)
        assert len(new_individuals[0]) == len(individual)
        assert new_individuals[0] == individual

        args = {
            "representation": representation,
            "indel_rate": 1.0,
            "variable_size": False
        }
        new_individuals = set_indel(Random(SEED), [individual], args)
        assert len(new_individuals[0]) == len(individual)
        assert new_individuals[0] == individual