.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/approximation/plot_mcb.py"
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.. only:: html

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

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

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

.. _sphx_glr_auto_examples_approximation_plot_mcb.py:


============================
Multiple Coefficient Binning
============================

Binning continuous data into intervals can be seen as an approximation that
reduces noise and captures the trend of a time series. The Multiple Coefficient
Binning (MCB) algorithm bins continuous time series into intervals,
transforming each time point of all the time series (a sequence of floats) into
a sequence of symbols, usually letters. Contrary to SAX which bins each time
series independently, MCB bins each time point independently.
This example shows how to use this algorithm and illustrates the
transformation.
It is implemented as :class:`pyts.approximation.MultipleCoefficientBinning`.

.. GENERATED FROM PYTHON SOURCE LINES 16-59



.. image-sg:: /auto_examples/approximation/images/sphx_glr_plot_mcb_001.png
   :alt: Multiple Coefficient Binning
   :srcset: /auto_examples/approximation/images/sphx_glr_plot_mcb_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.approximation import MultipleCoefficientBinning

    # Parameters
    n_samples, n_timestamps = 100, 12

    # Toy dataset
    rng = np.random.RandomState(41)
    X = rng.randn(n_samples, n_timestamps)

    # MCB transformation
    n_bins = 3
    mcb = MultipleCoefficientBinning(n_bins=n_bins, strategy='quantile')
    X_mcb = mcb.fit_transform(X)

    # Show the results for two time series
    plt.figure(figsize=(6, 4))

    plt.plot(X[0], 'o--', ms=4, label='First time series')
    for x, y, s in zip(range(n_timestamps), X[0], X_mcb[0]):
        plt.text(x, y, s, ha='center', va='bottom', fontsize=14, color='C0')

    plt.plot(X[5], 'o--', ms=4, label='Second time series')
    for x, y, s in zip(range(n_timestamps), X[5], X_mcb[5]):
        plt.text(x, y, s, ha='center', va='bottom', fontsize=14, color='C1')

    # Plot the bin edges
    for i in range(n_bins - 1):
        plt.hlines(mcb.bin_edges_.T[i], np.arange(n_timestamps) - 0.5,
                   np.arange(n_timestamps) + 0.5, color='g',
                   linestyles='--', linewidth=0.7)
    plt.vlines(np.arange(n_timestamps + 1) - 0.5, X.min(), X.max(),
               linestyles='--', linewidth=0.5)

    plt.legend(loc='best', fontsize=10)
    plt.xlabel('Time', fontsize=14)
    plt.title("Multiple Coefficient Binning", fontsize=16)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.450 seconds)


.. _sphx_glr_download_auto_examples_approximation_plot_mcb.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_mcb.py <plot_mcb.py>`

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

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


.. only:: html

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

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