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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_mixture_plot_gmm_init.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_init.py:


==========================
GMM 初始化方法
==========================

高斯混合模型中不同初始化方法的示例

有关估计器的更多信息，请参见 :ref:`gmm` 。

在这里，我们生成了一些具有四个易于识别的簇的样本数据。
此示例的目的是展示初始化参数 *init_param* 的四种不同方法。

这四种初始化方法是 *kmeans*（默认）、*random*、*random_from_data* 和 *k-means++*。

橙色菱形代表由 *init_param* 生成的 gmm 的初始化中心。其余数据表示为叉号，颜色表示 GMM 完成后的最终关联分类。

每个子图右上角的数字表示高斯混合模型收敛所需的迭代次数和算法运行初始化部分所需的相对时间。初始化时间较短的往往需要更多的迭代次数才能收敛。

初始化时间是该方法所需时间与默认 *kmeans* 方法所需时间的比率。如您所见，与 *kmeans* 相比，所有三种替代方法初始化所需时间都较少。

在此示例中，当使用 *random_from_data* 或 *random* 初始化时，模型需要更多的迭代次数才能收敛。这里 *k-means++* 在初始化时间短和高斯混合模型迭代次数少方面表现良好。

.. GENERATED FROM PYTHON SOURCE LINES 23-99



.. image-sg:: /auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
   :alt: GMM iterations and relative time taken to initialize, kmeans, Iter 8 | Init Time 1.00x, random_from_data, Iter 137 | Init Time 0.30x, k-means++, Iter 11 | Init Time 0.39x, random, Iter 47 | Init Time 0.28x
   :srcset: /auto_examples/mixture/images/sphx_glr_plot_gmm_init_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # 作者: Gordon Walsh <gordon.p.walsh@gmail.com>
    # 数据生成代码来自 Jake Vanderplas <vanderplas@astro.washington.edu>

    from timeit import default_timer as timer

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets._samples_generator import make_blobs
    from sklearn.mixture import GaussianMixture
    from sklearn.utils.extmath import row_norms

    print(__doc__)

    # 生成一些数据

    X, y_true = make_blobs(n_samples=4000, centers=4, cluster_std=0.60, random_state=0)
    X = X[ :, ::-1]

    n_samples = 4000
    n_components = 4
    x_squared_norms = row_norms(X, squared=True)


    def get_initial_means(X, init_params, r):
        # 运行一个 GaussianMixture 并设置 max_iter=0 以输出初始化均值
        gmm = GaussianMixture(
            n_components=4, init_params=init_params, tol=1e-9, max_iter=0, random_state=r
        ).fit(X)
        return gmm.means_


    methods = ["kmeans", "random_from_data", "k-means++", "random"]
    colors = ["navy", "turquoise", "cornflowerblue", "darkorange"]
    times_init = {}
    relative_times = {}

    plt.figure(figsize=(4 * len(methods) // 2, 6))
    plt.subplots_adjust(
        bottom=0.1, top=0.9, hspace=0.15, wspace=0.05, left=0.05, right=0.95
    )

    for n, method in enumerate(methods):
        r = np.random.RandomState(seed=1234)
        plt.subplot(2, len(methods) // 2, n + 1)

        start = timer()
        ini = get_initial_means(X, method, r)
        end = timer()
        init_time = end - start

        gmm = GaussianMixture(
            n_components=4, means_init=ini, tol=1e-9, max_iter=2000, random_state=r
        ).fit(X)

        times_init[method] = init_time
        for i, color in enumerate(colors):
            data = X[gmm.predict(X) == i]
            plt.scatter(data[:, 0], data[:, 1], color=color, marker="x")

        plt.scatter(
            ini[:, 0], ini[:, 1], s=75, marker="D", c="orange", lw=1.5, edgecolors="black"
        )
        relative_times[method] = times_init[method] / times_init[methods[0]]

        plt.xticks(())
        plt.yticks(())
        plt.title(method, loc="left", fontsize=12)
        plt.title(
            "Iter %i | Init Time %.2fx" % (gmm.n_iter_, relative_times[method]),
            loc="right",
            fontsize=10,
        )
    plt.suptitle("GMM iterations and relative time taken to initialize")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.365 seconds)


.. _sphx_glr_download_auto_examples_mixture_plot_gmm_init.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_init.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_gmm_init.recommendations


.. only:: html

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

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