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


===========================================
使用RANSAC进行稳健的线性模型估计
===========================================

在这个例子中，我们将看到如何使用:ref:`RANSAC <ransac_regression>` 算法稳健地拟合一个线性模型到有缺陷的数据。

普通的线性回归对异常值很敏感，拟合的直线很容易偏离数据的真实底层关系。

RANSAC回归器会自动将数据分为内点和外点，拟合的直线仅由识别出的内点决定。

.. GENERATED FROM PYTHON SOURCE LINES 14-78



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ransac_001.png
   :alt: plot ransac
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ransac_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Estimated coefficients (true, linear regression, RANSAC):
    82.1903908407869 [54.17236387] [82.08533159]






|

.. code-block:: Python


    import numpy as np
    from matplotlib import pyplot as plt

    from sklearn import datasets, linear_model

    n_samples = 1000
    n_outliers = 50


    X, y, coef = datasets.make_regression(
        n_samples=n_samples,
        n_features=1,
        n_informative=1,
        noise=10,
        coef=True,
        random_state=0,
    )

    # 添加异常数据
    np.random.seed(0)
    X[:n_outliers] = 3 + 0.5 * np.random.normal(size=(n_outliers, 1))
    y[:n_outliers] = -3 + 10 * np.random.normal(size=n_outliers)

    # 使用所有数据拟合直线
    # 
    # 
    lr = linear_model.LinearRegression()
    lr.fit(X, y)

    # 使用RANSAC算法稳健地拟合线性模型
    ransac = linear_model.RANSACRegressor()
    ransac.fit(X, y)
    inlier_mask = ransac.inlier_mask_
    outlier_mask = np.logical_not(inlier_mask)

    # 预测估计模型的数据
    line_X = np.arange(X.min(), X.max())[:, np.newaxis]
    line_y = lr.predict(line_X)
    line_y_ransac = ransac.predict(line_X)

    # 比较估计系数
    print("Estimated coefficients (true, linear regression, RANSAC):")
    print(coef, lr.coef_, ransac.estimator_.coef_)

    lw = 2
    plt.scatter(
        X[inlier_mask], y[inlier_mask], color="yellowgreen", marker=".", label="Inliers"
    )
    plt.scatter(
        X[outlier_mask], y[outlier_mask], color="gold", marker=".", label="Outliers"
    )
    plt.plot(line_X, line_y, color="navy", linewidth=lw, label="Linear regressor")
    plt.plot(
        line_X,
        line_y_ransac,
        color="cornflowerblue",
        linewidth=lw,
        label="RANSAC regressor",
    )
    plt.legend(loc="lower right")
    plt.xlabel("Input")
    plt.ylabel("Response")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.046 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_ransac.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/linear_model/plot_ransac.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_ransac.recommendations


.. only:: html

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

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