Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`.
177208
def split_subsets(self, train_size:float, valid_size:float, seed=None) -> 'ItemLists':
        ""
        assert 0 < train_size < 1
        assert 0 < valid_size < 1
        assert train_size + valid_size <= 1.
        if seed is not None: np.random.seed(seed)
        n = len(self.items)
        rand_idx = np.random.permutation(range(n))
        train_cut, valid_cut = int(train_size * n), int(valid_size * n)
        return self.split_by_idxs(rand_idx[:train_cut], rand_idx[-valid_cut:])
929204
def split(self, bits_count):
		
		result = []
		array = WBinArray(self.__value, self.__size)

		if (len(array) % bits_count) > 0:
			array.resize(len(array) + (bits_count - (len(array) % bits_count)))

		while len(array):
			result.append(WBinArray(array[:bits_count], bits_count))
			array = array[bits_count:]
		return result
1160111
def split_len(s, length):
    """"""
    return [s[i:i+length] for i in range(0, len(s), length)]
490934
def split_list(alist, wanted_parts=1):
    
    length = len(alist)
    return [
        alist[i * length // wanted_parts:(i + 1) * length // wanted_parts]
        for i in range(wanted_parts)
    ]
241517
def split_every(n, iterable):
    """"""
    items = iter(iterable)
    return itertools.takewhile(bool, (list(itertools.islice(items, n)) for _ in itertools.count()))
995243
def stash_split(fqdn, result, *argl, **argd):
    
    global _splits
    if fqdn == "sklearn.cross_validation.train_test_split":
        key = id(result[1])
        _splits[key] = result

    #We don't actually want to return anything for the analysis; we are using it
    #as a hook to save pointers to the dataset split so that we can easily
    #analyze performance later on.
    return None
435581
def dataset_splits(self):
    """"""
    return [{
        "split": problem.DatasetSplit.TRAIN,
        "shards": _TRAIN_SHARDS,
    }, {
        "split": problem.DatasetSplit.EVAL,
        "shards": _DEV_SHARDS,
    }]
558703
def _create_dataset(
            self, group, chunk_size, compression, compression_opts):
        """"""
        if chunk_size == 'auto':
            chunks = True
        else:
            # if dtype is a variable str, guess representative size is 20 bytes
            per_chunk = (
                nb_per_chunk(20, 1, chunk_size) if self.dtype == np.dtype('O')
                else nb_per_chunk(
                        np.dtype(self.dtype).itemsize, 1, chunk_size))
            chunks = (per_chunk,)

        shape = (0,)
        maxshape = (None,)

        # raise if per_chunk >= 4 Gb, this is requested by h5py
        group.create_dataset(
            self.name, shape, dtype=self.dtype,
            chunks=chunks, maxshape=maxshape, compression=compression,
            compression_opts=compression_opts)
739995
def split(self, box_size):
        
        w, h = box_size
        rows = round(self.height / h)
        cols = round(self.width / w)

        boxes = []
        for row in range(rows):
            for col in range(cols):
                box = Box(self.x + (col * w), self.y + (row * h), w, h)
                boxes.append(box)
        return boxes
450083
function split(arr, size) {
  var out = [];
  var sizes = [];
  var where = size - 1;
  var ptr = 0;
  var i, j;

  for (i = arr.length - 1; i >= 0; i--) {
    sizes[where] = (sizes[where] || 0) + 1;
    if (--where === -1) where = size - 1;
  }

  for (i = 0; i < size; i++) {
    for (j = 0; j < sizes[i]; j++) {
      if (j === 0) out[i] = [arr[ptr]];
      else out[i].push(arr[ptr]);
      ptr++;
    }
  }

  return out;
}
961002
def split_seq(iterable, size):
    """"""
    elm = iter(iterable)
    item = list(itertools.islice(elm, size))
    while item:
        yield item
        item = list(itertools.islice(elm, size))
44630
def split_pks(cols):
  """"""
  pks = []
  others = []
  for name, col in cols.items():
    if col["is_primary_key"] == "t":
      pks.append(name)
    else:
      others.append(name)

  return (tuple(pks), tuple(others))
1055191
def chunks(iterable, size):
    
    it = iter(iterable)
    item = list(islice(it, size))
    while item:
        yield item
        item = list(islice(it, size))
751603
def _split_batches(self, data, batch_size):
        """"""
        for i in range(0, len(data), batch_size):
            yield data[i : i + batch_size]
1311355
def random_split(valid_pct:float, *arrs:NPArrayableList)->SplitArrayList:
    ""
    assert (valid_pct>=0 and valid_pct<=1), 'Validation set percentage should be between 0 and 1'
    is_train = np.random.uniform(size=(len(arrs[0]),)) > valid_pct
    return arrays_split(is_train, *arrs)
520143
def dataset_splits(self):
    """"""
    return [{
        "split": problem.DatasetSplit.TRAIN,
        "shards": self.num_train_shards,
    }, {
        "split": problem.DatasetSplit.EVAL,
        "shards": self.num_eval_shards,
    }, {
        "split": problem.DatasetSplit.TEST,
        "shards": self.num_test_shards,
    }]
903009
def chunks(items, size):
    
    return [items[i:i+size] for i in range(0, len(items), size)]