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

.. only:: html

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

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

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

.. _sphx_glr__gallery_machine_learning_kmeans.py:


K-Means Clustering of MNIST Dataset
===================================

Example showing how you can perform K-Means clustering on the MNIST dataset.

.. GENERATED FROM PYTHON SOURCE LINES 7-118



.. image-sg:: /_gallery/machine_learning/images/sphx_glr_kmeans_001.webp
   :alt: kmeans
   :srcset: /_gallery/machine_learning/images/sphx_glr_kmeans_001.webp
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pygfx/objects/_ruler.py:267: RuntimeWarning: divide by zero encountered in divide
      screen_full = (ndc_full[:, :2] / ndc_full[:, 3:4]) * half_canvas_size
    /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pygfx/objects/_ruler.py:267: RuntimeWarning: invalid value encountered in divide
      screen_full = (ndc_full[:, :2] / ndc_full[:, 3:4]) * half_canvas_size
    /opt/hostedtoolcache/Python/3.12.9/x64/lib/python3.12/site-packages/pygfx/objects/_ruler.py:279: RuntimeWarning: invalid value encountered in divide
      screen_sel = (ndc_sel[:, :2] / ndc_sel[:, 3:4]) * half_canvas_size
    /home/runner/work/fastplotlib/fastplotlib/fastplotlib/graphics/_features/_base.py:18: UserWarning: casting float64 array to float32
      warn(f"casting {array.dtype} array to float32")







|

.. code-block:: Python


    # test_example = false

    import fastplotlib as fpl
    import numpy as np
    from sklearn.datasets import load_digits
    from sklearn.cluster import KMeans
    from sklearn.decomposition import PCA

    # load the data
    mnist = load_digits()

    # get the data and labels
    data = mnist['data'] # (1797, 64)
    labels = mnist['target'] # (1797,)

    # visualize the first 5 digits
    # NOTE: this is just to give a sense of the dataset if you are unfamiliar,
    # the more interesting visualization is below :D
    fig_data = fpl.Figure(shape=(1, 5), size=(900, 300))

    # iterate through each subplot
    for i, subplot in enumerate(fig_data):
        # reshape each image to (8, 8)
        subplot.add_image(data[i].reshape(8,8), cmap="gray", interpolation="linear")
        # add the label as a title
        subplot.set_title(f"Label: {labels[i]}")
        # turn off the axes and toolbar
        subplot.axes.visible = False
        subplot.toolbar  = False

    fig_data.show()

    # project the data from 64 dimensions down to the number of unique digits
    n_digits = len(np.unique(labels)) # 10

    reduced_data = PCA(n_components=n_digits).fit_transform(data) # (1797, 10)

    # performs K-Means clustering, take the best of 4 runs
    kmeans = KMeans(n_clusters=n_digits, n_init=4)
    # fit the lower-dimension data
    kmeans.fit(reduced_data)

    # get the centroids (center of the clusters)
    centroids = kmeans.cluster_centers_

    # plot the kmeans result and corresponding original image
    figure = fpl.Figure(
        shape=(1,2),
        size=(700, 400),
        cameras=["3d", "2d"],
        controller_types=[["fly", "panzoom"]]
    )

    # set the axes to False
    figure[0, 0].axes.visible = False
    figure[0, 1].axes.visible = False

    figure[0, 0].set_title(f"K-means clustering of PCA-reduced data")

    # plot the centroids
    figure[0, 0].add_scatter(
        data=np.vstack([centroids[:, 0], centroids[:, 1], centroids[:, 2]]).T,
        colors="white",
        sizes=15
    )
    # plot the down-projected data
    digit_scatter = figure[0,0].add_scatter(
        data=np.vstack([reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2]]).T,
        sizes=5,
        cmap="tab10", # use a qualitative cmap
        cmap_transform=kmeans.labels_, # color by the predicted cluster
    )

    # initial index
    ix = 0

    # plot the initial image
    digit_img = figure[0, 1].add_image(
        data=data[ix].reshape(8,8),
        cmap="gray",
        name="digit",
        interpolation="linear"
    )

    # change the color and size of the initial selected data point
    digit_scatter.colors[ix] = "magenta"
    digit_scatter.sizes[ix] = 10

    # define event handler to update the selected data point
    @digit_scatter.add_event_handler("pointer_enter")
    def update(ev):
        # reset colors and sizes
        digit_scatter.cmap = "tab10"
        digit_scatter.sizes = 5

        # update with new seleciton
        ix = ev.pick_info["vertex_index"]

        digit_scatter.colors[ix] = "magenta"
        digit_scatter.sizes[ix] = 10

        # update digit fig
        figure[0, 1]["digit"].data = data[ix].reshape(8, 8)

    figure.show()

    # NOTE: `if __name__ == "__main__"` is NOT how to use fastplotlib interactively
    # please see our docs for using fastplotlib interactively in ipython and jupyter
    if __name__ == "__main__":
        print(__doc__)
        fpl.loop.run()

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

   **Total running time of the script:** (0 minutes 0.828 seconds)


.. _sphx_glr_download__gallery_machine_learning_kmeans.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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