.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_plot_lda.py>`     to download the full example code
    .. rst-class:: sphx-glr-example-title

    .. _sphx_glr_auto_examples_plot_lda.py:


============================
Linear Discriminant Analysis
============================

This example demonstrates how the pipeline can be used to perform transformation of time series data, such as linear discriminant analysis for visualization purposes



.. image:: /auto_examples/images/sphx_glr_plot_lda_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none

    /home/david/Code/seglearn/examples/plot_lda.py:51: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
      plt.show()






|


.. code-block:: default


    # Author: David Burns
    # License: BSD

    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.discriminant_analysis import LinearDiscriminantAnalysis

    import seglearn as sgl


    def plot_embedding(emb, y, y_labels):
        # plot a 2D feature map embedding
        x_min, x_max = np.min(emb, 0), np.max(emb, 0)
        emb = (emb - x_min) / (x_max - x_min)

        NC = len(y_labels)
        markers = ['.', '+', 'x', '|', '_', '*', 'o']

        fig = plt.figure()
        fig.set_size_inches(6, 6)

        for c in range(NC):
            i = y == c
            plt.scatter(emb[i, 0], emb[i, 1], marker=markers[c], label=y_labels[c])

        plt.xticks([]), plt.yticks([])
        plt.legend()
        plt.tight_layout()


    # load the data
    data = sgl.load_watch()
    X = data['X']
    y = data['y']

    # create a pipeline for LDA transformation of the feature representation
    clf = sgl.Pype([('segment', sgl.Segment()),
                    ('ftr', sgl.FeatureRep()),
                    ('lda', LinearDiscriminantAnalysis(n_components=2))])

    X2, y2 = clf.fit_transform(X, y)
    plot_embedding(X2, y2.astype(int), data['y_labels'])
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.697 seconds)


.. _sphx_glr_download_auto_examples_plot_lda.py:


.. only :: html

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



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

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



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

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


.. only:: html

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

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