
    {Kg                     *   d Z ddlmZmZ ddlmZmZmZ ddlm	Z	 ddl
Z
ddlZddlmZ ddlmZmZ dd	lmZmZ d
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dlmZmZmZmZ  eddd      Z eeedgdgdgdgdg eed
dd      g eeddd      gdd      ddddddddd       Zy)a  Modified Olivetti faces dataset.

The original database was available from (now defunct)

    https://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:

    https://cs.nyu.edu/~roweis/
    )IntegralReal)PathLikemakedirsremove)existsN)loadmat   )Bunchcheck_random_state)Intervalvalidate_params   )get_data_home)RemoteFileMetadata_fetch_remote_pkl_filepath
load_descrzolivettifaces.matz.https://ndownloader.figshare.com/files/5976027@b612fb967f2dc77c9c62d3e1266e0c73d5fca46a4b8906c18e454d41af987794)filenameurlchecksumbooleanrandom_stateleft)closedg        neither)	data_homeshuffler   download_if_missing
return_X_y	n_retriesdelayT)prefer_skip_nested_validationF   g      ?c                    t        |       } t        |       st        |        t        | d      }t        |      s|st	        d      t        dt        j                  d|        t        t        | ||      }t        |      }	t        |       |	d   j                  j                         }
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t!        j,                  t/        d      D cg c]  }|dz  	 c}      }|r/t1        |      }|j3                  t5        |
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      d      }t7        d      }|r||fS t9        ||
||      S c c}w )a  Load the Olivetti faces data-set from AT&T (classification).

    Download it if necessary.

    =================   =====================
    Classes                                40
    Samples total                         400
    Dimensionality                       4096
    Features            real, between 0 and 1
    =================   =====================

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

    Parameters
    ----------
    data_home : str or path-like, 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 : bool, default=False
        If True the order of the dataset is shuffled to avoid having
        images of the same person grouped.

    random_state : int, RandomState instance or None, default=0
        Determines random number generation for dataset shuffling. Pass an int
        for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    download_if_missing : bool, default=True
        If False, raise an OSError if the data is not locally available
        instead of trying to download the data from the source site.

    return_X_y : bool, default=False
        If True, returns `(data, target)` instead of a `Bunch` object. See
        below for more information about the `data` and `target` object.

        .. versionadded:: 0.22

    n_retries : int, default=3
        Number of retries when HTTP errors are encountered.

        .. versionadded:: 1.5

    delay : float, default=1.0
        Number of seconds between retries.

        .. versionadded:: 1.5

    Returns
    -------
    data : :class:`~sklearn.utils.Bunch`
        Dictionary-like object, with the following attributes.

        data: ndarray, shape (400, 4096)
            Each row corresponds to a ravelled
            face image of original size 64 x 64 pixels.
        images : ndarray, shape (400, 64, 64)
            Each row is a face image
            corresponding to one of the 40 subjects of the dataset.
        target : ndarray, shape (400,)
            Labels associated to each face image.
            Those labels are ranging from 0-39 and correspond to the
            Subject IDs.
        DESCR : str
            Description of the modified Olivetti Faces Dataset.

    (data, target) : tuple if `return_X_y=True`
        Tuple with the `data` and `target` objects described above.

        .. versionadded:: 0.22

    Examples
    --------
    >>> from sklearn.datasets import fetch_olivetti_faces
    >>> olivetti_faces = fetch_olivetti_faces()
    >>> olivetti_faces.data.shape
    (400, 4096)
    >>> olivetti_faces.target.shape
    (400,)
    >>> olivetti_faces.images.shape
    (400, 64, 64)
    )r   zolivetti.pkzz1Data not found and `download_if_missing` is Falsez downloading Olivetti faces from z to )dirnamer"   r#   )	file_namefaces   )compress)  @   r-   r   r
   r   r,   
   zolivetti_faces.rst)dataimagestargetDESCR)r   r   r   r   OSErrorprintFACESr   r   r	   r   Tcopyjoblibdumploadnpfloat32minmaxreshape	transposearrayranger   permutationlenr   r   )r   r   r   r    r!   r"   r#   filepathmat_pathmfiler)   ir2   orderfaces_vectorizedfdescrs                   d/home/alanp/www/video.onchill/myenv/lib/python3.12/site-packages/sklearn/datasets/_olivetti_faces.pyfetch_olivetti_facesrN   &   s   P 	2I)Y7H("MNNEIIyQR 9	
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