.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/MachineLearning/k_means.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_examples_MachineLearning_k_means.py>`
        to download the full example code.

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

.. _sphx_glr_examples_MachineLearning_k_means.py:


k_means
=======

KMeans using scikit-learn according to https://www.youtube.com/watch?v=i-gxm_ofjBo.

.. GENERATED FROM PYTHON SOURCE LINES 12-69



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /examples/MachineLearning/images/sphx_glr_k_means_001.png
         :alt: data
         :srcset: /examples/MachineLearning/images/sphx_glr_k_means_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/MachineLearning/images/sphx_glr_k_means_002.png
         :alt: k means
         :srcset: /examples/MachineLearning/images/sphx_glr_k_means_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/MachineLearning/images/sphx_glr_k_means_003.png
         :alt: sse
         :srcset: /examples/MachineLearning/images/sphx_glr_k_means_003.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /examples/MachineLearning/images/sphx_glr_k_means_004.png
         :alt: silhouette score
         :srcset: /examples/MachineLearning/images/sphx_glr_k_means_004.png
         :class: sphx-glr-multi-img





.. code-block:: Python
   :lineno-start: 13


    import matplotlib.pyplot as plt
    import numpy as np

    from EasyFEA import Display

    from sklearn.cluster import KMeans
    from sklearn.datasets import make_blobs
    from sklearn.metrics import silhouette_score

    if __name__ == "__main__":
        # https://www.youtube.com/watch?v=i-gxm_ofjBo

        Display.Clear()

        N = 1000

        K = 5

        data = make_blobs(N, 2, centers=5, cluster_std=1, random_state=101)[0]

        kmeans = KMeans(K, n_init="auto")

        clusters = kmeans.fit_predict(data)

        ax = Display.Init_Axes()
        ax.plot(*data.T, "bo")
        ax.set_title("data")

        ax_c = Display.Init_Axes()
        for k in range(K):
            idx = np.where(clusters == k)
            ax_c.plot(*data[idx].T, "o")
            ax_c.plot(*np.mean(data[idx], 0), ls="", marker="+", c="black", zorder=10)

        sil_score = []
        sse = []

        array_k = np.arange(2, 20)

        for k in array_k:
            kmeans = KMeans(k, n_init="auto")

            clusters = kmeans.fit_predict(data)

            sse.append(kmeans.inertia_)
            sil_score.append(silhouette_score(data, clusters))

        a_sse = Display.Init_Axes()
        a_sse.plot(array_k, sse)
        a_sse.set_title("sse")

        a_sil = Display.Init_Axes()
        a_sil.plot(array_k, sil_score)
        a_sil.set_title("silhouette score")

        plt.show()


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

   **Total running time of the script:** (0 minutes 1.748 seconds)


.. _sphx_glr_download_examples_MachineLearning_k_means.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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