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


=========================================================
PCA example with Iris Data-set
=========================================================

Principal Component Analysis applied to the Iris dataset.

See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
information on this dataset.

.. GENERATED FROM PYTHON SOURCE LINES 12-60



.. image-sg:: /auto_examples/decomposition/images/sphx_glr_plot_pca_iris_001.png
   :alt: plot pca iris
   :srcset: /auto_examples/decomposition/images/sphx_glr_plot_pca_iris_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    # Code source: Gaël Varoquaux
    # License: BSD 3 clause

    import matplotlib.pyplot as plt

    # unused but required import for doing 3d projections with matplotlib < 3.2
    import mpl_toolkits.mplot3d  # noqa: F401
    import numpy as np

    from sklearn import datasets, decomposition

    np.random.seed(5)

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    fig = plt.figure(1, figsize=(4, 3))
    plt.clf()

    ax = fig.add_subplot(111, projection="3d", elev=48, azim=134)
    ax.set_position([0, 0, 0.95, 1])


    plt.cla()
    pca = decomposition.PCA(n_components=3)
    pca.fit(X)
    X = pca.transform(X)

    for name, label in [("Setosa", 0), ("Versicolour", 1), ("Virginica", 2)]:
        ax.text3D(
            X[y == label, 0].mean(),
            X[y == label, 1].mean() + 1.5,
            X[y == label, 2].mean(),
            name,
            horizontalalignment="center",
            bbox=dict(alpha=0.5, edgecolor="w", facecolor="w"),
        )
    # Reorder the labels to have colors matching the cluster results
    y = np.choose(y, [1, 2, 0]).astype(float)
    ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.nipy_spectral, edgecolor="k")

    ax.xaxis.set_ticklabels([])
    ax.yaxis.set_ticklabels([])
    ax.zaxis.set_ticklabels([])

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.054 seconds)


.. _sphx_glr_download_auto_examples_decomposition_plot_pca_iris.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/1.3.X?urlpath=lab/tree/notebooks/auto_examples/decomposition/plot_pca_iris.ipynb
        :alt: Launch binder
        :width: 150 px



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

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

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

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


.. only:: html

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

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