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


===========================================================
An example of K-Means++ initialization
===========================================================

An example to show the output of the :func:`sklearn.cluster.kmeans_plusplus`
function for generating initial seeds for clustering.

K-Means++ is used as the default initialization for :ref:`k_means`.

.. GENERATED FROM PYTHON SOURCE LINES 12-46



.. image-sg:: /auto_examples/cluster/images/sphx_glr_plot_kmeans_plusplus_001.png
   :alt: K-Means++ Initialization
   :srcset: /auto_examples/cluster/images/sphx_glr_plot_kmeans_plusplus_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt

    from sklearn.cluster import kmeans_plusplus
    from sklearn.datasets import make_blobs

    # Generate sample data
    n_samples = 4000
    n_components = 4

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

    # Calculate seeds from k-means++
    centers_init, indices = kmeans_plusplus(X, n_clusters=4, random_state=0)

    # Plot init seeds along side sample data
    plt.figure(1)
    colors = ["#4EACC5", "#FF9C34", "#4E9A06", "m"]

    for k, col in enumerate(colors):
        cluster_data = y_true == k
        plt.scatter(X[cluster_data, 0], X[cluster_data, 1], c=col, marker=".", s=10)

    plt.scatter(centers_init[:, 0], centers_init[:, 1], c="b", s=50)
    plt.title("K-Means++ Initialization")
    plt.xticks([])
    plt.yticks([])
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.055 seconds)


.. _sphx_glr_download_auto_examples_cluster_plot_kmeans_plusplus.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_kmeans_plusplus.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_kmeans_plusplus.recommendations


.. only:: html

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

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