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


=================================
高斯混合模型椭圆体
=================================

绘制通过期望最大化（ ``GaussianMixture`` 类）和变分推断（具有狄利克雷过程先验的 ``BayesianGaussianMixture`` 类模型）获得的两个高斯混合模型的置信椭圆体。

两个模型都可以使用五个组件来拟合数据。请注意，期望最大化模型将必然使用所有五个组件，而变分推断模型实际上只会使用所需的组件数量以获得良好的拟合。在这里我们可以看到，期望最大化模型会任意地分割一些组件，因为它试图拟合过多的组件，而狄利克雷过程模型会自动调整其状态数量。

这个例子没有展示出来，因为我们处于低维空间中，但狄利克雷过程模型的另一个优点是，即使每个簇的示例数量少于数据的维度，由于推断算法的正则化特性，它也可以有效地拟合完整的协方差矩阵。

.. GENERATED FROM PYTHON SOURCE LINES 13-81



.. image-sg:: /auto_examples/mixture/images/sphx_glr_plot_gmm_001.png
   :alt: Gaussian Mixture, Bayesian Gaussian Mixture with a Dirichlet process prior
   :srcset: /auto_examples/mixture/images/sphx_glr_plot_gmm_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    /app/scikit-learn-main-origin/sklearn/mixture/_base.py:251: ConvergenceWarning:

    Best performing initialization did not converge. Try different init parameters, or increase max_iter, tol, or check for degenerate data.







|

.. code-block:: Python


    import itertools

    import matplotlib as mpl
    import matplotlib.pyplot as plt
    import numpy as np
    from scipy import linalg

    from sklearn import mixture

    color_iter = itertools.cycle(["navy", "c", "cornflowerblue", "gold", "darkorange"])


    def plot_results(X, Y_, means, covariances, index, title):
        splot = plt.subplot(2, 1, 1 + index)
        for i, (mean, covar, color) in enumerate(zip(means, covariances, color_iter)):
            v, w = linalg.eigh(covar)
            v = 2.0 * np.sqrt(2.0) * np.sqrt(v)
            u = w[0] / linalg.norm(w[0])
            # 由于动态规划不会使用它能访问的每个组件，除非它需要它们，我们不应该绘制冗余组件。
            if not np.any(Y_ == i):
                continue
            plt.scatter(X[Y_ == i, 0], X[Y_ == i, 1], 0.8, color=color)

            # 绘制椭圆以显示高斯成分
            angle = np.arctan(u[1] / u[0])
            angle = 180.0 * angle / np.pi  # convert to degrees
            ell = mpl.patches.Ellipse(mean, v[0], v[1], angle=180.0 + angle, color=color)
            ell.set_clip_box(splot.bbox)
            ell.set_alpha(0.5)
            splot.add_artist(ell)

        plt.xlim(-9.0, 5.0)
        plt.ylim(-3.0, 6.0)
        plt.xticks(())
        plt.yticks(())
        plt.title(title)


    # Number of samples per component
    # 
    # 
    n_samples = 500

    # 生成随机样本，两个组成部分
    np.random.seed(0)
    C = np.array([[0.0, -0.1], [1.7, 0.4]])
    X = np.r_[
        np.dot(np.random.randn(n_samples, 2), C),
        0.7 * np.random.randn(n_samples, 2) + np.array([-6, 3]),
    ]

    # 使用五个成分通过EM拟合高斯混合模型
    gmm = mixture.GaussianMixture(n_components=5, covariance_type="full").fit(X)
    plot_results(X, gmm.predict(X), gmm.means_, gmm.covariances_, 0, "Gaussian Mixture")

    # 使用五个成分拟合狄利克雷过程高斯混合模型
    dpgmm = mixture.BayesianGaussianMixture(n_components=5, covariance_type="full").fit(X)
    plot_results(
        X,
        dpgmm.predict(X),
        dpgmm.means_,
        dpgmm.covariances_,
        1,
        "Bayesian Gaussian Mixture with a Dirichlet process prior",
    )

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.091 seconds)


.. _sphx_glr_download_auto_examples_mixture_plot_gmm.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/mixture/plot_gmm.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_gmm.recommendations


.. only:: html

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

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