.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "11_demos\python_packages\scikit-learn\demo_kMeansClustering.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        Click :ref:`here <sphx_glr_download_11_demos_python_packages_scikit-learn_demo_kMeansClustering.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_11_demos_python_packages_scikit-learn_demo_kMeansClustering.py:

K-Means clustering
=====================

An example of ``scikit-learn``, copied from
`scikit-learn.org <https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_plusplus.html#sphx-glr-auto-examples-cluster-plot-kmeans-plusplus-py>`_.

.. GENERATED FROM PYTHON SOURCE LINES 7-36



.. image-sg:: /11_demos/python_packages/scikit-learn/images/sphx_glr_demo_kMeansClustering_001.png
   :alt: K-Means++ Initialization
   :srcset: /11_demos/python_packages/scikit-learn/images/sphx_glr_demo_kMeansClustering_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    from sklearn.cluster import kmeans_plusplus
    from sklearn.datasets import make_blobs
    import matplotlib.pyplot as plt

    # 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 kmeans++
    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  0.228 seconds)


.. _sphx_glr_download_11_demos_python_packages_scikit-learn_demo_kMeansClustering.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


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

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

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

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