code: 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.cl.cam.ac.uk/research/dtg/attarchive/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 - faces.min()
      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,
      )
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/datasets/olivetti_faces.py
imports: from io import BytesIO
  from os.path import exists
  from os import makedirs
  import numpy
  from scipy.io.matlab import loadmat
  from .base import get_data_home, Bunch
  from .base import _pkl_filepath
  from ..utils import check_random_state
  from ..externals import joblib
label_desc: Use image node_id 1 for calling a global function with example usage: fetch_olivetti_faces(data_home, shuffle, random_state, download_if_missing) and returns: Bunch