.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/svm/plot_linearsvc_support_vectors.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_svm_plot_linearsvc_support_vectors.py>`
        to download the full example code. or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py:


=====================================
在 LinearSVC 中绘制支持向量
=====================================

与基于 LIBSVM 的 SVC 不同，基于 LIBLINEAR 的 LinearSVC 不提供支持向量。此示例演示了如何在 LinearSVC 中获取支持向量。

.. GENERATED FROM PYTHON SOURCE LINES 9-56



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_linearsvc_support_vectors_001.png
   :alt: C=1, C=100
   :srcset: /auto_examples/svm/images/sphx_glr_plot_linearsvc_support_vectors_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import make_blobs
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.svm import LinearSVC

    X, y = make_blobs(n_samples=40, centers=2, random_state=0)

    plt.figure(figsize=(10, 5))
    for i, C in enumerate([1, 100]):
        # “hinge” 是标准的 SVM 损失函数
        clf = LinearSVC(C=C, loss="hinge", random_state=42).fit(X, y)
        # 通过决策函数获得支持向量
        decision_function = clf.decision_function(X)
        # 我们也可以手动计算决策函数
        # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
        # 支持向量是位于边界内的样本，其大小通常限制为1
        support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]
        support_vectors = X[support_vector_indices]

        plt.subplot(1, 2, i + 1)
        plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
        ax = plt.gca()
        DecisionBoundaryDisplay.from_estimator(
            clf,
            X,
            ax=ax,
            grid_resolution=50,
            plot_method="contour",
            colors="k",
            levels=[-1, 0, 1],
            alpha=0.5,
            linestyles=["--", "-", "--"],
        )
        plt.scatter(
            support_vectors[:, 0],
            support_vectors[:, 1],
            s=100,
            linewidth=1,
            facecolors="none",
            edgecolors="k",
        )
        plt.title("C=" + str(C))
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.076 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_linearsvc_support_vectors.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/svm/plot_linearsvc_support_vectors.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_linearsvc_support_vectors.recommendations


.. only:: html

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

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