.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/semi_supervised/plot_label_propagation_structure.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` 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_semi_supervised_plot_label_propagation_structure.py: ============================================== تعلم انتشار العلامات لهيكل معقد ============================================== مثال على تعلم انتشار العلامات لهيكل داخلي معقد لتوضيح "تعلم المنحنى". يجب أن تكون الدائرة الخارجية مُعَلَّمة باللون "الأحمر" والدائرة الداخلية "باللون الأزرق". لأن كل مجموعة من العلامات تقع داخل شكلها المميز، يمكننا أن نرى أن العلامات تنتشر بشكل صحيح حول الدائرة. .. GENERATED FROM PYTHON SOURCE LINES 13-17 .. code-block:: Python # المؤلفون: مطوري سكايلرن # معرف الترخيص: BSD-3-Clause .. GENERATED FROM PYTHON SOURCE LINES 18-22 نقوم بتوليد مجموعة بيانات بدائرتين متحدتي المركز. بالإضافة إلى ذلك، يتم ربط علامة بكل عينة من مجموعة البيانات وهي: 0 (تابعة للدائرة الخارجية)، 1 (تابعة للدائرة الداخلية)، و -1 (غير معروفة). هنا، جميع العلامات ما عدا اثنتين تم وسمها على أنها غير معروفة. .. GENERATED FROM PYTHON SOURCE LINES 22-34 .. code-block:: Python import numpy as np from sklearn.datasets import make_circles n_samples = 200 X, y = make_circles(n_samples=n_samples, shuffle=False) outer, inner = 0, 1 labels = np.full(n_samples, -1.0) labels[0] = outer labels[-1] = inner .. GENERATED FROM PYTHON SOURCE LINES 35-36 رسم البيانات الخام .. GENERATED FROM PYTHON SOURCE LINES 36-67 .. code-block:: Python import matplotlib.pyplot as plt plt.figure(figsize=(4, 4)) plt.scatter( X[labels == outer, 0], X[labels == outer, 1], color="navy", marker="s", lw=0, label="outer labeled", s=10, ) plt.scatter( X[labels == inner, 0], X[labels == inner, 1], color="c", marker="s", lw=0, label="inner labeled", s=10, ) plt.scatter( X[labels == -1, 0], X[labels == -1, 1], color="darkorange", marker=".", label="unlabeled", ) plt.legend(scatterpoints=1, shadow=False, loc="center") _ = plt.title("Raw data (2 classes=outer and inner)") .. image-sg:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_001.png :alt: Raw data (2 classes=outer and inner) :srcset: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 68-70 الهدف من :class:`~sklearn.semi_supervised.LabelSpreading` هو ربط علامة بعينة حيث تكون العلامة غير معروفة في البداية. .. GENERATED FROM PYTHON SOURCE LINES 71-76 .. code-block:: Python from sklearn.semi_supervised import LabelSpreading label_spread = LabelSpreading(kernel="knn", alpha=0.8) label_spread.fit(X, labels) .. raw:: html
LabelSpreading(alpha=0.8, kernel='knn')
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.. GENERATED FROM PYTHON SOURCE LINES 77-79 الآن، يمكننا التحقق من العلامات التي تم ربطها بكل عينة عندما كانت العلامة غير معروفة. .. GENERATED FROM PYTHON SOURCE LINES 79-105 .. code-block:: Python output_labels = label_spread.transduction_ output_label_array = np.asarray(output_labels) outer_numbers = np.where(output_label_array == outer)[0] inner_numbers = np.where(output_label_array == inner)[0] plt.figure(figsize=(4, 4)) plt.scatter( X[outer_numbers, 0], X[outer_numbers, 1], color="navy", marker="s", lw=0, s=10, label="outer learned", ) plt.scatter( X[inner_numbers, 0], X[inner_numbers, 1], color="c", marker="s", lw=0, s=10, label="inner learned", ) plt.legend(scatterpoints=1, shadow=False, loc="center") plt.title("Labels learned with Label Spreading (KNN)") plt.show() .. image-sg:: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_002.png :alt: Labels learned with Label Spreading (KNN) :srcset: /auto_examples/semi_supervised/images/sphx_glr_plot_label_propagation_structure_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.214 seconds) .. _sphx_glr_download_auto_examples_semi_supervised_plot_label_propagation_structure.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/semi_supervised/plot_label_propagation_structure.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/semi_supervised/plot_label_propagation_structure.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_label_propagation_structure.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_label_propagation_structure.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_label_propagation_structure.zip ` .. include:: plot_label_propagation_structure.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_