Python random.shuffle方法代码示例

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def make_train_test_sets(pos_graphs, neg_graphs,
                         test_proportion=.3, random_state=2):
    """make_train_test_sets."""
    random.seed(random_state)
    random.shuffle(pos_graphs)
    random.shuffle(neg_graphs)
    pos_dim = len(pos_graphs)
    neg_dim = len(neg_graphs)
    tr_pos_graphs = pos_graphs[:-int(pos_dim * test_proportion)]
    te_pos_graphs = pos_graphs[-int(pos_dim * test_proportion):]
    tr_neg_graphs = neg_graphs[:-int(neg_dim * test_proportion)]
    te_neg_graphs = neg_graphs[-int(neg_dim * test_proportion):]
    tr_graphs = tr_pos_graphs + tr_neg_graphs
    te_graphs = te_pos_graphs + te_neg_graphs
    tr_targets = [1] * len(tr_pos_graphs) + [0] * len(tr_neg_graphs)
    te_targets = [1] * len(te_pos_graphs) + [0] * len(te_neg_graphs)
    tr_graphs, tr_targets = paired_shuffle(tr_graphs, tr_targets)
    te_graphs, te_targets = paired_shuffle(te_graphs, te_targets)
    return (tr_graphs, np.array(tr_targets)), (te_graphs, np.array(te_targets)) 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def setup(self, bottom, top):
        layer_params = yaml.load(self.param_str)
        self._layer_params = layer_params
        # default batch_size = 256
        self._batch_size = int(layer_params.get('batch_size', 256))
        self._resize = layer_params.get('resize', -1)
        self._mean_file = layer_params.get('mean_file', None)
        self._source_type = layer_params.get('source_type', 'CSV')
        self._shuffle = layer_params.get('shuffle', False)
        # read image_mean from file and preload all data into memory
        # will read either file or array into self._mean
        self.set_mean()
        self.preload_db()
        self._compressed = self._layer_params.get('compressed', True)
        if not self._compressed:
            self.decompress_data() 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def preload_db(self):
        """Read all images in and all labels

        Implemenation relies on DataManager Classes
        """
        print("Preloading Data...")
        if self._source_type == 'BCF':
            self._data_manager = BCFDataManager(self._layer_params)
        elif self._source_type == 'CSV':
            self._data_manager = CSVDataManager(self._layer_params)
        elif self._source_type == 'LMDB':
            self._data_manager = LMDBDataManager(self._layer_params)
        # read all data
        self._data, self._label = self._data_manager.load_all()
        self._sample_count = len(self._data)
        if self._shuffle:
            self.shuffle() 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def shuffle_sequences(self, split="train", keys=None):
        if keys is None:
            # print(type(self.data))
            # print(type(self.data.keys()))
            keys = self.data[split].keys()

        for key in keys:
            if key in ["positive", "negative"]:
                continue
            idxs = list(range(len(self.data[split][key])))

            random.shuffle(idxs)

            self.sequences[split][key] = \
                self.sequences[split][key].index_select(
                    0, torch.LongTensor(idxs))

            temp = [self.data[split][key][i] for i in idxs]
            self.data[split][key] = temp

            temp = [self.masks[split][key][i] for i in idxs]
            self.masks[split][key] = temp 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def shuffle_sequences(self, split="train", keys=None):
        if keys is None:
            # print(type(self.data))
            # print(type(self.data.keys()))
            keys = self.data[split].keys()

        for key in keys:
            idxs = list(range(len(self.data[split][key])))

            random.shuffle(idxs)

            self.sequences[split][key] = \
                self.sequences[split][key].index_select(
                    0, torch.LongTensor(idxs))

            temp = [self.data[split][key][i] for i in idxs]
            self.data[split][key] = temp
            temp = [self.masks[split][key][i] for i in idxs]
            self.masks[split][key] = temp 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def encode_turing_machine_rules(rules, starting_state=None, story=None):
    if story is None:
        story = graph_tools.Story()
    graph = story.graph
    if starting_state is None:
        starting_state = random.choice(len(rules))
    the_edges = [(cstate, read, write, nstate, direc)
                    for (cstate, stuff) in enumerate(rules)
                    for (read, (write, nstate, direc)) in enumerate(stuff)]
    random.shuffle(the_edges)
    for cstate, read, write, nstate, direc in the_edges:
        source = graph.make_unique('state_{}'.format(cstate))
        dest = graph.make_unique('state_{}'.format(nstate))
        edge_type = "rule_{}_{}_{}".format(read,write,direc)
        source[edge_type] = dest
        story.add_line("rule {} {} {} {} {}".format(source.type, read, write, dest.type, direc))
    head = graph.make_unique('head')

    head.state = graph.make_unique('state_{}'.format(starting_state))
    story.add_line("start {}".format(head.state.type))
    return story 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def show(limit, shuffle=True):
    td = TrainingData(LABEL_FILE, img_root=IMAGES_ROOT, mean_image_file=MEAN_IMAGE_FILE, image_property=IMAGE_PROP)
    _limit = limit if limit > 0 else 5
    iterator = td.generate()
    if shuffle:
        import random
        shuffled = list(iterator)
        random.shuffle(shuffled)
        iterator = iter(shuffled)

    i = 0
    for arr, im in iterator:
        restored = td.data_to_image(arr, im.label, raw=True)
        print(im.path)
        restored.image.show()
        i += 1
        if i >= _limit:
            break 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def __init__(self, path, shuffle, config):
        """
        :param path: data path list
        :param shuffle:  shuffle bool
        :param config:  config
        """
        #
        print("Loading Data......")
        self.data_list = []
        self.max_count = config.max_count
        self.path = path
        self.shuffle = shuffle
        # char feature
        self.pad_char = [char_pad, char_pad]
        # self.pad_char = []
        self.max_char_len = config.max_char_len 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def dataLoader(self):
        """
        :return:
        """
        path = self.path
        shuffle = self.shuffle
        assert isinstance(path, list), "Path Must Be In List"
        print("Data Path {}".format(path))
        for id_data in range(len(path)):
            print("Loading Data Form {}".format(path[id_data]))
            insts = self._Load_Each_Data(path=path[id_data], shuffle=shuffle)
            random.shuffle(insts)
            self._write_shuffle_inst_to_file(insts, path=path[id_data])
            self.data_list.append(insts)
        # return train/dev/test data
        if len(self.data_list) == 3:
            return self.data_list[0], self.data_list[1], self.data_list[2]
        elif len(self.data_list) == 2:
            return self.data_list[0], self.data_list[1] 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def get_loader_single(data_name, split, root, json, vocab, transform,
                      batch_size=100, shuffle=True,
                      num_workers=2, ids=None, collate_fn=collate_fn):
    """Returns torch.utils.data.DataLoader for custom coco dataset."""
    if 'coco' in data_name:
        # COCO custom dataset
        dataset = CocoDataset(root=root,
                              json=json,
                              vocab=vocab,
                              transform=transform, ids=ids)
    elif 'f8k' in data_name or 'f30k' in data_name:
        dataset = FlickrDataset(root=root,
                                split=split,
                                json=json,
                                vocab=vocab,
                                transform=transform)

    # Data loader
    data_loader = torch.utils.data.DataLoader(dataset=dataset,
                                              batch_size=batch_size,
                                              shuffle=shuffle,
                                              pin_memory=True,
                                              num_workers=num_workers,
                                              collate_fn=collate_fn)
    return data_loader 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def load_data_fashion_mnist(batch_size, resize=None, root='./data'):
    """Download the fashion mnist dataset and then load into memory."""
    trans = []
    if resize:
        trans.append(torchvision.transforms.Resize(size=resize))
    trans.append(torchvision.transforms.ToTensor())

    transform = torchvision.transforms.Compose(trans)
    mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)
    mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform)
    if sys.platform.startswith('win'):
        num_workers = 0  # 0表示不用额外的进程来加速读取数据
    else:
        num_workers = 4
    train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
    test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)

    return train_iter, test_iter 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def data_iter_random(corpus_indices, batch_size, num_steps, device=None):
    # 减1是因为输出的索引x是相应输入的索引y加1
    num_examples = (len(corpus_indices) - 1) // num_steps
    epoch_size = num_examples // batch_size
    example_indices = list(range(num_examples))
    random.shuffle(example_indices)

    # 返回从pos开始的长为num_steps的序列
    def _data(pos):
        return corpus_indices[pos: pos + num_steps]

    if device is None:
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    for i in range(epoch_size):
        # 每次读取batch_size个随机样本
        i = i * batch_size
        batch_indices = example_indices[i: i + batch_size]
        X = [_data(j * num_steps) for j in batch_indices]
        Y = [_data(j * num_steps + 1) for j in batch_indices]
        yield torch.tensor(X, dtype=torch.float32, device=device), torch.tensor(Y, dtype=torch.float32, device=device) 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def data_iter_random(corpus_indices, batch_size, num_steps, device=None):
    # 减1是因为输出的索引x是相应输入的索引y加1
    num_examples = (len(corpus_indices) - 1) // num_steps
    epoch_size = num_examples // batch_size
    example_indices = list(range(num_examples))
    random.shuffle(example_indices)

    # 返回从pos开始的长为num_steps的序列
    def _data(pos):
        return corpus_indices[pos: pos + num_steps]
    if device is None:
        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    for i in range(epoch_size):
        # 每次读取batch_size个随机样本
        i = i * batch_size
        batch_indices = example_indices[i: i + batch_size]
        X = [_data(j * num_steps) for j in batch_indices]
        Y = [_data(j * num_steps + 1) for j in batch_indices]
        yield torch.tensor(X, dtype=torch.float32, device=device), torch.tensor(Y, dtype=torch.float32, device=device) 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def move(self, coordinates):
        """
        Move coordinates.

        :Parameters:
            #. coordinates (np.ndarray): The coordinates on which to apply
               the transformation.

        :Returns:
            #. coordinates (np.ndarray): The new coordinates after applying
               the transformation.
        """
        indexes = range(len(self.__combination))
        if self.__shuffle:
            shuffle( indexes )
        # create the move combination
        for idx in indexes:
            coordinates = self.__combination[idx].move(coordinates)
        return coordinates 

# 需要导入模块: import random [as 别名]
# 或者: from random import shuffle [as 别名]
def _load_image_set_index(self, shuffle):
        """
        get total number of images, init indices

        Parameters
        ----------
        shuffle : bool
            whether to shuffle the initial indices
        """
        self.num_images = 0
        for db in self.imdbs:
            self.num_images += db.num_images
        indices = list(range(self.num_images))
        if shuffle:
            random.shuffle(indices)
        return indices