def fetch_olivetti_faces(data_home=None, shuffle=False, random_state=0,
                         download_if_missing=True):
    """Loader for the Olivetti faces data-set from AT&T.

    Read more in the :ref:`User Guide <olivetti_faces>`.

    Parameters
    ----------
    data_home : optional, default: None
        Specify another download and cache folder for the datasets. By default
        all scikit learn data is stored in '~/scikit_learn_data' subfolders.

    shuffle : boolean, optional
        If True the order of the dataset is shuffled to avoid having
        images of the same person grouped.

    download_if_missing: optional, True by default
        If False, raise a IOError if the data is not locally available
        instead of trying to download the data from the source site.

    random_state : optional, integer or RandomState object
        The seed or the random number generator used to shuffle the
        data.

    Returns
    -------
    An object with the following attributes:

    data : numpy array of shape (400, 4096)
        Each row corresponds to a ravelled face image of original size 64 x 64 pixels.

    images : numpy array of shape (400, 64, 64)
        Each row is a face image corresponding to one of the 40 subjects of the dataset.

    target : numpy array of shape (400, )
        Labels associated to each face image. Those labels are ranging from
        0-39 and correspond to the Subject IDs.

    DESCR : string
        Description of the modified Olivetti Faces Dataset.

    Notes
    ------

    This dataset consists of 10 pictures each of 40 individuals. The original
    database was available from (now defunct)

        http://www.uk.research.att.com/facedatabase.html

    The version retrieved here comes in MATLAB format from the personal
    web page of Sam Roweis:

        http://www.cs.nyu.edu/~roweis/

    """
    data_home = get_data_home(data_home=data_home)
    if not exists(data_home):
        makedirs(data_home)
    filepath = _pkl_filepath(data_home, TARGET_FILENAME)
    if not exists(filepath):
        print('downloading Olivetti faces from %s to %s'
              % (DATA_URL, data_home))
        fhandle = urlopen(DATA_URL)
        buf = BytesIO(fhandle.read())
        mfile = loadmat(buf)
        faces = mfile['faces'].T.copy()
        joblib.dump(faces, filepath, compress=6)
        del mfile
    else:
        faces = joblib.load(filepath)
    # We want floating point data, but float32 is enough (there is only
    # one byte of precision in the original uint8s anyway)
    faces = np.float32(faces)
    faces = faces.min() - faces
    faces /= faces.max()
    faces = faces.reshape((400, 64, 64)).transpose(0, 2, 1)
    # 10 images per class, 400 images total, each class is contiguous.
    target = np.array([i // 10 for i in range(400)])
    if shuffle:
        random_state = check_random_state(random_state)
        order = random_state.permutation(len(faces))
        faces = faces[order]
        target = target[order]
    return Bunch(data=faces.reshape(len(faces), -1),
                 images=faces,
                 target=target,
                 DESCR=MODULE_DOCS)
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/datasets/olivetti_faces.py/fetch_olivetti_faces/swapped operands of `-`