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


================================================================
对比不同层次聚类方法在玩具数据集上的表现
================================================================

这个示例展示了不同层次聚类方法在“有趣”但仍然是二维的数据集上的特性。

主要的观察点是：

- 单链接法速度快，并且在非球状数据上表现良好，但在有噪声的情况下表现较差。
- 平均链接法和完全链接法在干净分离的球状簇上表现良好，但在其他情况下结果不一。
- Ward方法在有噪声的数据上最为有效。

虽然这些示例提供了一些关于算法的直观理解，但这种直观理解可能不适用于非常高维的数据。

.. GENERATED FROM PYTHON SOURCE LINES 17-28

.. code-block:: Python


    import time
    import warnings
    from itertools import cycle, islice

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import cluster, datasets
    from sklearn.preprocessing import StandardScaler








.. GENERATED FROM PYTHON SOURCE LINES 29-30

生成数据集。我们选择足够大的规模来观察算法的可扩展性，但不会太大以避免运行时间过长。

.. GENERATED FROM PYTHON SOURCE LINES 30-52

.. code-block:: Python



    n_samples = 1500
    noisy_circles = datasets.make_circles(
        n_samples=n_samples, factor=0.5, noise=0.05, random_state=170
    )
    noisy_moons = datasets.make_moons(n_samples=n_samples, noise=0.05, random_state=170)
    blobs = datasets.make_blobs(n_samples=n_samples, random_state=170)
    rng = np.random.RandomState(170)
    no_structure = rng.rand(n_samples, 2), None

    # 各向异性分布的数据
    X, y = datasets.make_blobs(n_samples=n_samples, random_state=170)
    transformation = [[0.6, -0.6], [-0.4, 0.8]]
    X_aniso = np.dot(X, transformation)
    aniso = (X_aniso, y)

    # 方差不同的斑点
    varied = datasets.make_blobs(
        n_samples=n_samples, cluster_std=[1.0, 2.5, 0.5], random_state=170
    )








.. GENERATED FROM PYTHON SOURCE LINES 53-54

运行聚类并绘图

.. GENERATED FROM PYTHON SOURCE LINES 54-169

.. code-block:: Python



    # 设置集群参数
    plt.figure(figsize=(9 * 1.3 + 2, 14.5))
    plt.subplots_adjust(
        left=0.02, right=0.98, bottom=0.001, top=0.96, wspace=0.05, hspace=0.01
    )

    plot_num = 1

    default_base = {"n_neighbors": 10, "n_clusters": 3}

    datasets = [
        (noisy_circles, {"n_clusters": 2}),
        (noisy_moons, {"n_clusters": 2}),
        (varied, {"n_neighbors": 2}),
        (aniso, {"n_neighbors": 2}),
        (blobs, {}),
        (no_structure, {}),
    ]

    for i_dataset, (dataset, algo_params) in enumerate(datasets):
        # 使用数据集特定的值更新参数
        params = default_base.copy()
        params.update(algo_params)

        X, y = dataset

        # 规范化数据集以便于参数选择
        X = StandardScaler().fit_transform(X)

        # ============
        # 创建集群对象
        # ============
        ward = cluster.AgglomerativeClustering(
            n_clusters=params["n_clusters"], linkage="ward"
        )
        complete = cluster.AgglomerativeClustering(
            n_clusters=params["n_clusters"], linkage="complete"
        )
        average = cluster.AgglomerativeClustering(
            n_clusters=params["n_clusters"], linkage="average"
        )
        single = cluster.AgglomerativeClustering(
            n_clusters=params["n_clusters"], linkage="single"
        )

        clustering_algorithms = (
            ("Single Linkage", single),
            ("Average Linkage", average),
            ("Complete Linkage", complete),
            ("Ward Linkage", ward),
        )

        for name, algorithm in clustering_algorithms:
            t0 = time.time()

            # 捕捉与 kneighbors_graph 相关的警告
            with warnings.catch_warnings():
                warnings.filterwarnings(
                    "ignore",
                    message="the number of connected components of the "
                    + "connectivity matrix is [0-9]{1,2}"
                    + " > 1. Completing it to avoid stopping the tree early.",
                    category=UserWarning,
                )
                algorithm.fit(X)

            t1 = time.time()
            if hasattr(algorithm, "labels_"):
                y_pred = algorithm.labels_.astype(int)
            else:
                y_pred = algorithm.predict(X)

            plt.subplot(len(datasets), len(clustering_algorithms), plot_num)
            if i_dataset == 0:
                plt.title(name, size=18)

            colors = np.array(
                list(
                    islice(
                        cycle(
                            [
                                "#377eb8",
                                "#ff7f00",
                                "#4daf4a",
                                "#f781bf",
                                "#a65628",
                                "#984ea3",
                                "#999999",
                                "#e41a1c",
                                "#dede00",
                            ]
                        ),
                        int(max(y_pred) + 1),
                    )
                )
            )
            plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[y_pred])

            plt.xlim(-2.5, 2.5)
            plt.ylim(-2.5, 2.5)
            plt.xticks(())
            plt.yticks(())
            plt.text(
                0.99,
                0.01,
                ("%.2fs" % (t1 - t0)).lstrip("0"),
                transform=plt.gca().transAxes,
                size=15,
                horizontalalignment="right",
            )
            plot_num += 1

    plt.show()



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_linkage_comparison_001.png
   :alt: Single Linkage, Average Linkage, Complete Linkage, Ward Linkage
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_linkage_comparison_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 1.012 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_linkage_comparison.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/cluster/plot_linkage_comparison.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_linkage_comparison.recommendations


.. only:: html

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

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