.. _sphx_glr_auto_examples_plot_mcb.py:


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

This example shows how the MCB algorithm transforms a dataset of time series of
real numbers into a list of sequences of letters. It is implemented as
:class:`pyts.quantization.MCB`.




.. image:: /auto_examples/images/sphx_glr_plot_mcb_001.png
    :align: center





.. code-block:: python


    import numpy as np
    import matplotlib.lines as mlines
    import matplotlib.pyplot as plt
    from pyts.quantization import MCB

    # Parameters
    n_samples, n_features = 6, 12

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

    # MCB transformation
    n_bins = 3
    quantiles = 'empirical'
    mcb = MCB(n_bins=n_bins, quantiles=quantiles)
    X_mcb = mcb.fit_transform(X)

    # Compute bins
    bins = mcb._bins

    # Show the results for the first time series
    plt.figure(figsize=(12, 8))
    # First time series
    plt.plot(X[0], 'o-', label='TS 1')
    for x, y, s in zip(range(n_features), X[0], X_mcb[0]):
        plt.text(x, y, s, ha='center', va='bottom', fontsize=20, color='#1f77b4')
    # Second time series
    plt.plot(X[5], 'o-', label='TS 2')
    for x, y, s in zip(range(n_features), X[5], X_mcb[5]):
        plt.text(x, y, s, ha='center', va='bottom', fontsize=20, color='#ff7f0e')
    plt.hlines(bins, np.arange(n_features) - 0.5, np.arange(n_features) + 0.5,
               color='g', linestyles='--', linewidth=0.7)
    plt.vlines(np.arange(n_features + 1) - 0.5, X.min(), X.max(),
               linestyles='--', linewidth=0.5)
    mcb_legend_1 = mlines.Line2D([], [], color='#1f77b4', marker='*',
                                 label='MCB TS 1 - {0} bins'.format(n_bins))
    mcb_legend_2 = mlines.Line2D([], [], color='#ff7f0e', marker='*',
                                 label='MCB TS 2 - {0} bins'.format(n_bins))
    first_legend = plt.legend(handles=[mcb_legend_1, mcb_legend_2],
                              fontsize=14, loc=4)
    ax = plt.gca().add_artist(first_legend)
    plt.legend(loc='best', fontsize=14)
    plt.title("Multiple Coefficient Binning - TS = Time Series", fontsize=18)
    plt.show()

**Total running time of the script:** ( 0 minutes  0.043 seconds)



.. only :: html

 .. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :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.readthedocs.io>`_