.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/svm/plot_weighted_samples.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_weighted_samples.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_weighted_samples.py:


=====================
SVM：加权样本
=====================

绘制加权数据集的决策函数，其中点的大小与其权重成正比。

样本加权会重新调整参数C，这意味着分类器会更加重视正确分类这些点。这个效果通常可能比较微妙。
为了强调这里的效果，我们特别对离群点进行加权，使决策边界的变形非常明显。

.. GENERATED FROM PYTHON SOURCE LINES 12-69



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_weighted_samples_001.png
   :alt: Constant weights, Modified weights
   :srcset: /auto_examples/svm/images/sphx_glr_plot_weighted_samples_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import svm


    def plot_decision_function(classifier, sample_weight, axis, title):
        # 绘制决策函数
        xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))

        Z = classifier.decision_function(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)

        # 绘制直线、点和最近的向量到平面
        axis.contourf(xx, yy, Z, alpha=0.75, cmap=plt.cm.bone)
        axis.scatter(
            X[:, 0],
            X[:, 1],
            c=y,
            s=100 * sample_weight,
            alpha=0.9,
            cmap=plt.cm.bone,
            edgecolors="black",
        )

        axis.axis("off")
        axis.set_title(title)


    # 我们创建了20个点
    np.random.seed(0)
    X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
    y = [1] * 10 + [-1] * 10
    sample_weight_last_ten = abs(np.random.randn(len(X)))
    sample_weight_constant = np.ones(len(X))
    # 并且对一些异常值赋予更大的权重
    sample_weight_last_ten[15:] *= 5
    sample_weight_last_ten[9] *= 15

    # 拟合模型。

    # 此模型未考虑样本权重。
    clf_no_weights = svm.SVC(gamma=1)
    clf_no_weights.fit(X, y)

    # 这个其他模型考虑了一些专用的样本权重。
    clf_weights = svm.SVC(gamma=1)
    clf_weights.fit(X, y, sample_weight=sample_weight_last_ten)

    fig, axes = plt.subplots(1, 2, figsize=(14, 6))
    plot_decision_function(
        clf_no_weights, sample_weight_constant, axes[0], "Constant weights"
    )
    plot_decision_function(clf_weights, sample_weight_last_ten, axes[1], "Modified weights")

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.260 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_weighted_samples.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_weighted_samples.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_weighted_samples.recommendations


.. only:: html

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

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