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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_multivariate_plot_weasel_muse.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_multivariate_plot_weasel_muse.py:


===========
WEASEL+MUSE
===========

WEASELMUSE stand for Word ExtrAction for time SEries cLassification plus
Multivariate Unsupervised Symbols and dErivatives.
This example shows how the WEASELMUSE algorithm transforms multivariate time
series of real numbers into a sequence of frequencies of words, and
illustrates the features obtained for two time series.
It is implemented as :class:`pyts.multivariate.transformation.WEASELMUSE`.

.. GENERATED FROM PYTHON SOURCE LINES 13-55



.. image-sg:: /auto_examples/multivariate/images/sphx_glr_plot_weasel_muse_001.png
   :alt: WEASEL+MUSE transformation
   :srcset: /auto_examples/multivariate/images/sphx_glr_plot_weasel_muse_001.png
   :class: sphx-glr-single-img





.. code-block:: default


    # Author: Johann Faouzi <johann.faouzi@gmail.com>
    # License: BSD-3-Clause

    import numpy as np
    import matplotlib.pyplot as plt
    from pyts.datasets import load_basic_motions
    from pyts.multivariate.transformation import WEASELMUSE
    from sklearn.preprocessing import LabelEncoder

    # Toy dataset
    X_train, _, y_train, _ = load_basic_motions(return_X_y=True)
    y_train = LabelEncoder().fit_transform(y_train)

    # WEASEL+MUSE transformation
    transformer = WEASELMUSE(word_size=2, n_bins=2, window_sizes=[12, 36],
                             chi2_threshold=15, sparse=False)
    X_weasel = transformer.fit_transform(X_train, y_train)

    # Visualize the transformation for the first time series
    plt.figure(figsize=(6, 4))
    vocabulary_length = len(transformer.vocabulary_)
    width = 0.3
    plt.bar(np.arange(vocabulary_length) - width / 2, X_weasel[y_train == 0][0],
            width=width, label='First time series in class 0')
    plt.bar(np.arange(vocabulary_length) + width / 2, X_weasel[y_train == 1][0],
            width=width, label='First time series in class 1')
    plt.xticks(np.arange(vocabulary_length),
               np.vectorize(transformer.vocabulary_.get)(
                   np.arange(X_weasel[0].size)),
               fontsize=12, rotation=60, ha='right')
    y_max = np.max(np.concatenate([X_weasel[y_train == 0][0],
                                   X_weasel[y_train == 1][0]]))
    plt.yticks(np.arange(y_max + 1), fontsize=12)
    plt.xlabel("Words", fontsize=14)
    plt.ylabel("Frequencies", fontsize=14)
    plt.title("WEASEL+MUSE transformation", fontsize=16)
    plt.legend(loc='best', fontsize=10)

    plt.subplots_adjust(bottom=0.27)
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.276 seconds)


.. _sphx_glr_download_auto_examples_multivariate_plot_weasel_muse.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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