python类shuffle()的实例源码

def perform_priority_moves(state, interactive):
  to_move = [pos for cell, pos in state.board.traverse() if cell.content and cell.content[TYPE] == ROBOT
                and cell.content['move'] in PRIORITY_MOVES]
  random.shuffle(to_move)
  while len(to_move) > 0:
    pos = to_move.pop()
    robot = state.board.get_item(pos).content
    if robot['move'] == FORWARD_TWO:
      direction = robot[FACING]
    elif robot['move'] == SIDESTEP_LEFT:
      direction = turn_direction(robot[FACING], False)
    elif robot['move'] == SIDESTEP_RIGHT:
      direction = turn_direction(robot[FACING], True)
    pos = perform_move_in_direction(state, pos, direction, to_move, interactive)
    if pos != None and robot['move'] == FORWARD_TWO:
      pos = perform_move_in_direction(state, pos, direction, to_move, interactive)
    if pos != None:
      if robot[CHARGES] > 0:
        robot[CHARGES] -= 1
      else:
        robot[LIFE] -= 1
        if robot[LIFE] == 0:
          record_death(robot, 'malfunction', interactive)

def perform_moves(state, interactive):
  to_move = [cell.content for cell, pos in state.board.traverse() if cell.content and cell.content[TYPE] == ROBOT
                and cell.content['move'] in [TURN_LEFT, TURN_RIGHT, U_TURN]]
  for robot in to_move:
    perform_turn(robot, robot['move'])
  to_move = [pos for cell, pos in state.board.traverse() if cell.content and cell.content[TYPE] == ROBOT
                and cell.content['move'] in [FORWARD, REVERSE]]
  random.shuffle(to_move)
  while len(to_move) > 0:
    pos = to_move.pop()
    robot = state.board.get_item(pos).content
    if robot['move'] == FORWARD:
      direction = robot[FACING]
    elif robot['move'] == REVERSE:
      direction = opposite_direction(robot[FACING])
    perform_move_in_direction(state, pos, direction, to_move, interactive)

def process_train_data(self, input_file, onto_aware):
        print >>sys.stderr, "Reading training data"
        label_ind = []
        tagged_sentences = []
        for line in open(input_file):
            lnstrp = line.strip()
            label, tagged_sentence = lnstrp.split("\t")
            if label not in self.label_map:
                self.label_map[label] = len(self.label_map)
            label_ind.append(self.label_map[label])
            tagged_sentences.append(tagged_sentence)
        # Shuffling so that when Keras does validation split, it is not always at the end.
        sentences_and_labels = zip(tagged_sentences, label_ind)
        random.shuffle(sentences_and_labels)
        tagged_sentences, label_ind = zip(*sentences_and_labels)
        print >>sys.stderr, "Indexing training data"
        train_inputs = self.data_processor.prepare_paired_input(tagged_sentences, onto_aware=onto_aware,
                                                                for_test=False, remove_singletons=True)
        train_labels = self.data_processor.make_one_hot(label_ind)
        return train_inputs, train_labels

def split_dataset(dataset, split_ratio, mode):
    if mode=='SPLIT_CLASSES':
        nrof_classes = len(dataset)
        class_indices = np.arange(nrof_classes)
        np.random.shuffle(class_indices)
        split = int(round(nrof_classes*split_ratio))
        train_set = [dataset[i] for i in class_indices[0:split]]
        test_set = [dataset[i] for i in class_indices[split:-1]]
    elif mode=='SPLIT_IMAGES':
        train_set = []
        test_set = []
        min_nrof_images = 2
        for cls in dataset:
            paths = cls.image_paths
            np.random.shuffle(paths)
            split = int(round(len(paths)*split_ratio))
            if split<min_nrof_images:
                continue  # Not enough images for test set. Skip class...
            train_set.append(ImageClass(cls.name, paths[0:split]))
            test_set.append(ImageClass(cls.name, paths[split:-1]))
    else:
        raise ValueError('Invalid train/test split mode "%s"' % mode)
    return train_set, test_set

def get_neighbors(words, word, window_size=2):
  if type(word) == str:
    idx = words.index(word)
  elif type(word) == int:
    idx = word
  else:
    raise Exception(" [!] Invalid type for word: %s" % type(word))

  if idx < window_size:
    ans = words[-(window_size - idx):] + words[:idx + window_size + 1]
  elif idx >= len(words) - window_size:
    ans = words[idx-window_size:] + words[:window_size + idx - len(words) + 1]
  else:
    ans = words[idx-window_size:idx+window_size+1]

  for _ in xrange(15):
    if random.random() < 0.1:
      ans.append(random.choice(ans))

  random.shuffle(ans)
  return ans

def split(self, *splits):
        """docstring for Batcher"""

        data_length = len(self.x)

        indexes = range(data_length)
        random.shuffle(indexes)

        splits = [0] + list(splits)
        splits_total = sum(splits)

        return (
            query(splits)
            .scan()
            .select(lambda n: int(data_length * n / splits_total))
            .then(_window, n=2)
            .select(lambda (start, end): np.array(indexes[start:end]))
            .select(lambda split: Data(**{k: source[split,:] for (k, source) in self.sources.iteritems()}))
            .to_list()
        )

def batch_iter(data, batch_size, num_epochs, shuffle=True):
    """
    Generates a batch iterator for a dataset.
    """
    data = np.array(data)
    data_size = len(data)
    num_batches_per_epoch = int(len(data)/batch_size) + 1
    for epoch in range(num_epochs):
        # Shuffle the data at each epoch
        if shuffle:
            shuffle_indices = np.random.permutation(np.arange(data_size))
            shuffled_data = data[shuffle_indices]
        else:
            shuffled_data = data
        for batch_num in range(num_batches_per_epoch):
            start_index = batch_num * batch_size
            end_index = min((batch_num + 1) * batch_size, data_size)
            yield shuffled_data[start_index:end_index]

def train(self, sentences):

        random.shuffle(sentences)
        for s, a_sentence in enumerate(sentences):
            words_and_tags = a_sentence.split('\n')
            words = [wt.split(' ')[0] for wt in words_and_tags]
            tags = [wt.split(' ')[1] for wt in words_and_tags]

            # ?????0 1 2 3 4 5 6 7 8 9
            for i in range(len(tags)):
                if tags[i][0] == 'I':
                    if i == len(tags)-1 or tags[i+1] != tags[i]:
                        tags[i] = 'E-' + tags[i][-3:]
                tags[i] = int(self.tags_dict[tags[i]])

            self.model.train(words, tags)
            if s % 5000 == 0:
                print('       -----> ' + str(s // 5000) + '/5')

def make_list(args):
    image_list = list_image(args.root, args.recursive, args.exts)
    image_list = list(image_list)
    if args.shuffle is True:
        random.seed(100)
        random.shuffle(image_list)
    N = len(image_list)
    chunk_size = (N + args.chunks - 1) / args.chunks
    for i in xrange(args.chunks):
        chunk = image_list[i * chunk_size:(i + 1) * chunk_size]
        if args.chunks > 1:
            str_chunk = '_%d' % i
        else:
            str_chunk = ''
        sep = int(chunk_size * args.train_ratio)
        sep_test = int(chunk_size * args.test_ratio)
        if args.train_ratio == 1.0:
            write_list(args.prefix + str_chunk + '.lst', chunk)
        else:
            if args.test_ratio:
                write_list(args.prefix + str_chunk + '_test.lst', chunk[:sep_test])
            if args.train_ratio + args.test_ratio < 1.0:
                write_list(args.prefix + str_chunk + '_val.lst', chunk[sep_test + sep:])
            write_list(args.prefix + str_chunk + '_train.lst', chunk[sep_test:sep_test + sep])

def _compute_max_qval_action_pair(self, state):
        '''
        Args:
            state (State)

        Returns:
            (tuple) --> (float, str): where the float is the Qval, str is the action.
        '''
        # Grab random initial action in case all equal
        best_action = random.choice(self.actions)
        max_q_val = float("-inf")
        shuffled_action_list = self.actions[:]
        random.shuffle(shuffled_action_list)

        # Find best action (action w/ current max predicted Q value)
        for action in shuffled_action_list:
            q_s_a = self.get_q_value(state, action)
            if q_s_a > max_q_val:
                max_q_val = q_s_a
                best_action = action

        return max_q_val, best_action

def _compute_max_qval_action_pair(self, state, q_func_id=None):
        '''
        Args:
            state (State)
            q_func_id (str): either "A", "B", or None. If None, computes avg of A and B.

        Returns:
            (tuple) --> (float, str): where the float is the Qval, str is the action.
        '''
        # Grab random initial action in case all equal
        best_action = random.choice(self.actions)
        max_q_val = float("-inf")
        shuffled_action_list = self.actions[:]
        random.shuffle(shuffled_action_list)

        # Find best action (action w/ current max predicted Q value)
        for action in shuffled_action_list:
            q_s_a = self.get_q_value(state, action, q_func_id)
            if q_s_a > max_q_val:
                max_q_val = q_s_a
                best_action = action

        return max_q_val, best_action

def _batch_samples(self, sample):
    """Batch several samples together."""

    # Batch and shuffle
    if self.shuffle:
      samples = tf.train.shuffle_batch(
          sample,
          batch_size=self.batch_size,
          num_threads=self.nthreads,
          capacity=self.capacity,
          min_after_dequeue=self.min_after_dequeue)
    else:
      samples = tf.train.batch(
          sample,
          batch_size=self.batch_size,
          num_threads=self.nthreads,
          capacity=self.capacity)
    return samples

def handle_other_peers(self, swarm, data):
        """Handle other_peers message when not using ALTO"""

        # Shuffle the received members list
        mem_copy = data['details']
        random.shuffle(mem_copy)

        for member in mem_copy:
            if swarm._args.tcp:
                self.add_tcp_member(swarm, member[0], member[1])
            else:
                # This is UDP
                m = swarm.AddMember(member[0], member[1])
                if isinstance(m, str):
                    pass
                else:
                    m.SendHandshake()

def split_keys(profiles, bin_sites, random_state=1234):
    """Balanced split over binding/non-binding sequences."""
    random.seed(random_state)
    pos_keys = bin_sites.keys()
    neg_keys = list(set(profiles.keys()) - set(pos_keys))
    random.shuffle(pos_keys)
    random.shuffle(neg_keys)

    len_pos = len(pos_keys)
    pos_keys1 = pos_keys[:len_pos / 2]
    pos_keys2 = pos_keys[len_pos / 2:]

    len_neg = len(neg_keys)
    neg_keys1 = neg_keys[:len_neg / 2]
    neg_keys2 = neg_keys[len_neg / 2:]

    return [pos_keys1, pos_keys2, neg_keys1, neg_keys2]

def runmoore(x=78, y=18, m=225):
    field = anonymine_fields.generic_field([x, y])
    field.set_callback('input', output, None)
    print(field)
    mines = field.all_cells()
    random.shuffle(mines)
    field.fill(mines[:m])

    for mine in mines[m:]:
        for neighbour in field.get_neighbours(mine):
            if neighbour in mines[:m]:
                break
        else:
            field.reveal(mine)
            break

    solver = anonymine_solver.solver()
    solver.field = field

    print(solver.solve())

def runneumann(x=78, y=18, m=225):
    field = anonymine_fields.generic_field([x, y], False)
    field.set_callback('input', output, None)

    mines = field.all_cells()
    random.shuffle(mines)
    field.fill(mines[:m])

    for mine in mines[m:]:
        for neighbour in field.get_neighbours(mine):
            if neighbour in mines[:m]:
                break
        else:
            field.reveal(mine)
            break

    solver = anonymine_solver.solver()
    solver.field = field
    print(solver.solve())

def runhex(x=39, y=18, m=112):
    field = anonymine_fields.hexagonal_field(x, y)
    field.set_callback('input', output, None)

    mines = field.all_cells()
    random.shuffle(mines)
    field.fill(mines[:m])

    for mine in mines[m:]:
        for neighbour in field.get_neighbours(mine):
            if neighbour in mines[:m]:
                break
        else:
            field.reveal(mine)
            break

    solver = anonymine_solver.solver()
    solver.field = field
    print(solver.solve())

def gen_values(self, n, reversed = False, shuffled = False, gen_dupes = False):
        if reversed:
            keys = xrange(n-1,-1,-1)
        else:
            keys = xrange(n)
        if shuffled:
            keys = list(keys)
            r = random.Random(1234827)
            r.shuffle(keys)
        if gen_dupes:
            return itertools.chain(
                itertools.izip(keys, xrange(0, 2*n, 2)),
                itertools.islice(itertools.izip(keys, xrange(0, 2*n, 2)), 10, None),
            )
        else:
            return itertools.izip(keys, xrange(0, 2*n, 2))

def testBsearch(self, dtype=dtype):
            testarray = range(1,101)
            random.shuffle(testarray)
            a = numpy.array(testarray[:50], dtype)
            b = numpy.array([0] + testarray[50:] + range(101,103), dtype)
            a = numpy.sort(a)
            self.assertEqual(mapped_struct.bsearch(a, 0), 0)
            self.assertEqual(mapped_struct.bsearch(a, 101), len(a))
            self.assertEqual(mapped_struct.bsearch(a, 102), len(a))
            for x in a:
                ix = mapped_struct.bsearch(a, x)
                self.assertLess(ix, len(a))
                self.assertEqual(a[ix], x)
                self.assertTrue(mapped_struct.sorted_contains(a, x))
            for x in b:
                ix = mapped_struct.bsearch(a, x)
                self.assertTrue(ix >= len(a) or a[ix] != x)
                self.assertFalse(mapped_struct.sorted_contains(a, x))

def test_send_receive(self):
        random.shuffle(self.swarm)
        senders = self.swarm[:len(self.swarm)/2]
        receivers = self.swarm[len(self.swarm)/2:]
        for sender, receiver in zip(senders, receivers):
            message = binascii.hexlify(os.urandom(64))

            # check queue previously empty
            self.assertFalse(bool(receiver.message_list()))

            # send message
            self.assertTrue(sender.message_send(receiver.dht_id(), message))

            # check received
            received = receiver.message_list()
            self.assertTrue(sender.dht_id() in received)
            messages = received[sender.dht_id()]
            self.assertTrue(len(messages) == 1)
            self.assertEqual(messages[0], message)

            # check queue empty after call to message_list
            self.assertFalse(bool(receiver.message_list()))

def test_ordering(self):
        random.shuffle(self.swarm)
        sender = self.swarm[0]
        receiver = self.swarm[-1]

        # send messages
        message_alpha = binascii.hexlify(os.urandom(64))
        message_beta = binascii.hexlify(os.urandom(64))
        message_gamma = binascii.hexlify(os.urandom(64))
        self.assertTrue(sender.message_send(receiver.dht_id(), message_alpha))
        self.assertTrue(sender.message_send(receiver.dht_id(), message_beta))
        self.assertTrue(sender.message_send(receiver.dht_id(), message_gamma))

        # check received in order
        received = receiver.message_list()
        self.assertTrue(sender.dht_id() in received)
        messages = received[sender.dht_id()]
        self.assertEqual(messages[0], message_alpha)
        self.assertEqual(messages[1], message_beta)
        self.assertEqual(messages[2], message_gamma)

def test_json(self):
        random.shuffle(self.swarm)
        sender = self.swarm[0]
        receiver = self.swarm[-1]
        message = {
            "test_object": {"foo": "bar"},
            "test_array": [0, 1, 2, 3, 4, 5],
            "test_integer": 42,
            "test_float": 3.14,
            "test_bool": True,
            "test_null": None,
        }

        # send message
        self.assertTrue(sender.message_send(receiver.dht_id(), message))

        # check received
        received = receiver.message_list()
        self.assertTrue(sender.dht_id() in received)
        messages = received[sender.dht_id()]
        self.assertTrue(len(messages) == 1)
        self.assertEqual(messages[0], message)

def test_multihop(self):
        random.shuffle(self.swarm)
        senders = self.swarm[:len(self.swarm) / 2]
        receivers = self.swarm[len(self.swarm) / 2:]
        for sender, receiver in zip(senders, receivers):

            # receiver subscribes to topic
            topic = "test_miltihop_{0}".format(binascii.hexlify(os.urandom(32)))
            receiver.pubsub_subscribe(topic)

            # wait until subscriptions propagate
            time.sleep(SLEEP_TIME)

            # send event
            event = binascii.hexlify(os.urandom(32))
            sender.pubsub_publish(topic, event)

            # wait until event propagates
            time.sleep(SLEEP_TIME)

            # check all peers received the event
            events = receiver.pubsub_events(topic)
            self.assertEqual(events, [event])

def get_batches(data, batch_size, vocabulary, pos_vocabulary):
    '''
    Get batches without any restrictions on number of antecedents and negative candidates.
    '''
    random.seed(24)
    random.shuffle(data)

    data_size = len(data)
    if data_size % float(batch_size) == 0:
        num_batches = int(data_size / float(batch_size))
    else:
        num_batches = int(data_size / float(batch_size)) + 1

    batches = []
    for batch_num in range(num_batches):
        start_index = batch_num * batch_size
        end_index = min((batch_num + 1) * batch_size, data_size)

        batch = pad_batch(data[start_index:end_index], vocabulary, pos_vocabulary)
        batches.append(batch)

    logging.info('Data size: %s' % len(data))
    logging.info('Number of batches: %s' % len(batches))

    return batches

def split(flags):
    if os.path.exists(flags.split_path):
        return np.load(flags.split_path).item()
    folds = flags.folds
    path = flags.input_path
    random.seed(6)
    img_list = ["%s/%s"%(path,img) for img in os.listdir(path)]
    random.shuffle(img_list)
    dic = {}
    n = len(img_list)
    num = (n+folds-1)//folds
    for i in range(folds):
        s,e = i*num,min(i*num+num,n)
        dic[i] = img_list[s:e]
    np.save(flags.split_path,dic)
    return dic

def __init__(self, reader, partition, discretizer, normalizer,
                 batch_size, steps, shuffle):
        self.reader = reader
        self.partition = partition
        self.discretizer = discretizer
        self.normalizer = normalizer
        self.batch_size = batch_size

        if steps is None:
            self.n_examples = reader.get_number_of_examples()
            self.steps = (self.n_examples + batch_size - 1) // batch_size
        else:
            self.n_examples = steps * batch_size
            self.steps = steps

        self.shuffle = shuffle
        self.chunk_size = min(1024, steps) * batch_size
        self.lock = threading.Lock()
        self.generator = self._generator()

def __init__(self, reader, discretizer, normalizer,
                 batch_size, steps, shuffle):
        self.reader = reader
        self.discretizer = discretizer
        self.normalizer = normalizer
        self.batch_size = batch_size

        if steps is None:
            self.n_examples = reader.get_number_of_examples()
            self.steps = (self.n_examples + batch_size - 1) // batch_size
        else:
            self.n_examples = steps * batch_size
            self.steps = steps

        self.shuffle = shuffle
        self.chunk_size = min(1024, steps) * batch_size
        self.lock = threading.Lock()
        self.generator = self._generator()

def _generator(self):
        B = self.batch_size
        while True:
            if self.shuffle:
                self.reader.random_shuffle()
            remaining = self.n_examples
            while remaining > 0:
                current_size = min(self.chunk_size, remaining)
                remaining -= current_size
                (data, ts, labels, header) = read_chunk(self.reader, current_size)
                data = preprocess_chunk(data, ts, self.discretizer, self.normalizer)
                data = (data, labels)
                data = common_utils.sort_and_shuffle(data, B)

                for i in range(0, current_size, B):
                    yield (nn_utils.pad_zeros(data[0][i:i + B]),
                           np.array(data[1][i:i + B]))

def justify_line(line, width):
    """Stretch a line to width by filling in spaces at word gaps.

    The gaps are picked randomly one-after-another, before it starts
    over again.

    """
    i = []
    while 1:
        # line not long enough already?
        if len(' '.join(line)) < width:
            if not i:
                # index list is exhausted
                # get list if indices excluding last word
                i = range(max(1, len(line)-1))
                # and shuffle it
                random.shuffle(i)
            # append space to a random word and remove its index
            line[i.pop(0)] += ' '
        else:
            # line has reached specified width or wider
            return ' '.join(line)

def botPick(self, ctx, bot, game):
        # Has the bot pick their card
        blackNum  = game['BlackCard']['Pick']
        if blackNum == 1:
            cardSpeak = 'card'
        else:
            cardSpeak = 'cards'
        i = 0
        cards = []
        while i < blackNum:
            randCard = random.randint(0, len(bot['Hand'])-1)
            cards.append(bot['Hand'].pop(randCard)['Text'])
            i += 1

        await self.typing(game)

        # Make sure we haven't laid any cards
        if bot['Laid'] == False and game['Judging'] == False:
            newSubmission = { 'By': bot, 'Cards': cards }
            game['Submitted'].append(newSubmission)
            # Shuffle cards
            shuffle(game['Submitted'])
            bot['Laid'] = True
            game['Time'] = currentTime = int(time.time())
            await self.checkSubmissions(ctx, game, bot)