.. only:: html

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

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

    .. _sphx_glr_auto_examples_plot_feature_rep_mix_example.py:


============================
Simple FeatureRepMix Example
============================

This example demonstrates how to use the FeatureRepMix on segmented data.

Although not shown here, FeatureRepMix can be used with Pype in place of FeatureRep.
See API documentation for an example.




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

 Out:

 .. code-block:: none

    After segmentation:
    X: [[[ 0.  1.  2.  3.]
      [ 4.  5.  6.  7.]
      [ 8.  9. 10. 11.]]]
    y:  [0.]
    After column-wise feature extraction:
       a_min_0  b_min_1  c_min_2  c_min_3  d_max_0  d_max_1  e_max_2  e_max_3
    0      0.0      1.0      2.0      3.0      8.0      9.0     10.0     11.0






|


.. code-block:: default


    # Author: Matthias Gazzari
    # License: BSD

    from seglearn.transform import Segment, FeatureRep, FeatureRepMix
    from seglearn.feature_functions import minimum, maximum
    from seglearn.base import TS_Data

    import numpy as np
    import pandas as pd

    # Single multivariate time series with 3 samples of 4 variables
    X = [np.array([[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]])]
    # Time series target
    y = [np.array([True, False, False])]

    segment = Segment(width=3, overlap=1)
    X, y, _ = segment.fit_transform(X, y)

    print('After segmentation:')
    print("X:", X)
    print("y: ", y)

    union = FeatureRepMix([
        ('a', FeatureRep(features={'min': minimum}), 0),
        ('b', FeatureRep(features={'min': minimum}), 1),
        ('c', FeatureRep(features={'min': minimum}), [2, 3]),
        ('d', FeatureRep(features={'max': maximum}), slice(0, 2)),
        ('e', FeatureRep(features={'max': maximum}), [False, False, True, True]),
    ])

    X = union.fit_transform(X, y)
    print('After column-wise feature extraction:')
    df = pd.DataFrame(data=X, columns=union.f_labels)
    print(df)


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

   **Total running time of the script:** ( 0 minutes  1.887 seconds)


.. _sphx_glr_download_auto_examples_plot_feature_rep_mix_example.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_feature_rep_mix_example.py <plot_feature_rep_mix_example.py>`



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

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


.. only:: html

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

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