.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_cluster_dl85.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_cluster_dl85.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_plot_cluster_dl85.py:


===================================
DL8.5 default predictive clustering
===================================
This example illustrates how to use the DL85Cluster class for predictive clustering.
A second implementation of predictive clustering is provided in the plot_cluster_user.py
example. 

.. GENERATED FROM PYTHON SOURCE LINES 9-45




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

 .. code-block:: none

    ####################################################################
    #                      DL8.5 default clustering                    #
    ####################################################################
    Model building...
    Model built. Duration of building = 4.8635 



    ####################################################################
    #                DL8.5 default predictive clustering               #
    ####################################################################
    Model building...
    Model built. Duration of building = 2.6191
    X_desc is used to describe data while X_err is used to compute errors






|

.. code-block:: default

    import numpy as np
    from sklearn.model_selection import train_test_split
    import time
    from pydl85 import DL85Cluster

    dataset = np.genfromtxt("../datasets/anneal.txt", delimiter=' ')
    X = dataset[:, 1:]
    X_train, X_test = train_test_split(X, test_size=0.2, random_state=0)


    print("####################################################################\n"
          "#                      DL8.5 default clustering                    #\n"
          "####################################################################")
    clf = DL85Cluster(max_depth=2, time_limit=600)
    start = time.perf_counter()
    print("Model building...")
    clf.fit(X_train)
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4), "\n\n\n")
    predicted = clf.predict(X_test)


    print("####################################################################\n"
          "#                DL8.5 default predictive clustering               #\n"
          "####################################################################")
    X_desc = X_train[:X_test.shape[0], :]
    X_err = X_test
    clf = DL85Cluster(max_depth=2, time_limit=600)
    start = time.perf_counter()
    print("Model building...")
    clf.fit(X_desc, X_err)
    duration = time.perf_counter() - start
    print("Model built. Duration of building =", round(duration, 4))
    print("X_desc is used to describe data while X_err is used to compute errors")
    predicted = clf.predict(X_err)



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

   **Total running time of the script:** ( 0 minutes  7.523 seconds)


.. _sphx_glr_download_auto_examples_plot_cluster_dl85.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_cluster_dl85.py <plot_cluster_dl85.py>`

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

      :download:`Download Jupyter notebook: plot_cluster_dl85.ipynb <plot_cluster_dl85.ipynb>`


.. only:: html

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

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