.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/miscellaneous/plot_multioutput_face_completion.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_miscellaneous_plot_multioutput_face_completion.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_miscellaneous_plot_multioutput_face_completion.py:


===================================================
اكتمال الوجه باستخدام المُقدرات متعددة المخرجات
===================================================

يُظهر هذا المثال استخدام المُقدر متعدد المخرجات لإكمال الصور.
الهدف هو التنبؤ بالنصف السفلي للوجه بناءً على النصف العلوي.

يُظهر العمود الأول من الصور الوجوه الحقيقية. توضح الأعمدة التالية كيف تقوم الأشجار العشوائية للغاية، وأقرب جيران، والانحدار الخطي، والانحدار الالتفافي بإكمال النصف السفلي من هذه الوجوه.

.. GENERATED FROM PYTHON SOURCE LINES 11-94



.. image-sg:: /auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png
   :alt: Face completion with multi-output estimators, true faces, Extra trees, K-nn, Linear regression, Ridge
   :srcset: /auto_examples/miscellaneous/images/sphx_glr_plot_multioutput_face_completion_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    downloading Olivetti faces from https://ndownloader.figshare.com/files/5976027 to /root/scikit_learn_data






|

.. code-block:: Python

    # المؤلفون: مطوري سكايلرن
    # معرف الترخيص: BSD-3-Clause

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import fetch_olivetti_faces
    from sklearn.ensemble import ExtraTreesRegressor
    from sklearn.linear_model import LinearRegression, RidgeCV
    from sklearn.neighbors import KNeighborsRegressor
    from sklearn.utils.validation import check_random_state

    # تحميل مجموعات بيانات الوجوه
    data, targets = fetch_olivetti_faces(return_X_y=True)

    train = data[targets < 30]
    test = data[targets >= 30] # الاختبار على أشخاص مستقلين

    # الاختبار على مجموعة فرعية من الأشخاص
    n_faces = 5
    rng = check_random_state(4)
    face_ids = rng.randint(test.shape[0], size=(n_faces,))
    test = test[face_ids, :]

    n_pixels = data.shape[1]
    # النصف العلوي من الوجوه
    X_train = train[:, : (n_pixels + 1) // 2]
    # النصف السفلي من الوجوه
    y_train = train[:, n_pixels // 2 :]
    X_test = test[:, : (n_pixels + 1) // 2]
    y_test = test[:, n_pixels // 2 :]

    # ملاءمة المُقدرات
    ESTIMATORS = {
        "Extra trees": ExtraTreesRegressor(
            n_estimators=10, max_features=32, random_state=0
        ),
        "K-nn": KNeighborsRegressor(),
        "Linear regression": LinearRegression(),
        "Ridge": RidgeCV(),
    }

    y_test_predict = dict()
    for name, estimator in ESTIMATORS.items():
        estimator.fit(X_train, y_train)
        y_test_predict[name] = estimator.predict(X_test)

    # رسم الوجوه المكتملة
    image_shape = (64, 64)

    n_cols = 1 + len(ESTIMATORS)
    plt.figure(figsize=(2.0 * n_cols, 2.26 * n_faces))
    plt.suptitle("Face completion with multi-output estimators", size=16)

    for i in range(n_faces):
        true_face = np.hstack((X_test[i], y_test[i]))

        if i:
            sub = plt.subplot(n_faces, n_cols, i * n_cols + 1)
        else:
            sub = plt.subplot(n_faces, n_cols, i * n_cols + 1, title="true faces")

        sub.axis("off")
        sub.imshow(
            true_face.reshape(image_shape), cmap=plt.cm.gray, interpolation="nearest"
        )

        for j, est in enumerate(sorted(ESTIMATORS)):
            completed_face = np.hstack((X_test[i], y_test_predict[est][i]))

            if i:
                sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j)

            else:
                sub = plt.subplot(n_faces, n_cols, i * n_cols + 2 + j, title=est)

            sub.axis("off")
            sub.imshow(
                completed_face.reshape(image_shape),
                cmap=plt.cm.gray,
                interpolation="nearest",
            )

    plt.show()

.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 3.242 seconds)


.. _sphx_glr_download_auto_examples_miscellaneous_plot_multioutput_face_completion.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/miscellaneous/plot_multioutput_face_completion.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_multioutput_face_completion.ipynb <plot_multioutput_face_completion.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_multioutput_face_completion.py <plot_multioutput_face_completion.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_multioutput_face_completion.zip <plot_multioutput_face_completion.zip>`


.. include:: plot_multioutput_face_completion.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_