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

.. only:: html

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

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

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

.. _sphx_glr_getting_started_auto_examples_plot_HOO_Garland.py:


1-D Example
================
In this example, we run the T_HOO algorithm on the Garland objective. First, import all the functions needed

.. GENERATED FROM PYTHON SOURCE LINES 7-19

.. code-block:: default


    from PyXAB.synthetic_obj.Garland import Garland                 # the objective
    from PyXAB.algos.HOO import T_HOO                               # the algorithm
    from PyXAB.partition.BinaryPartition import BinaryPartition     # the partition

    # the other useful packages/functions
    import numpy as np
    from PyXAB.utils.plot import plot_regret











.. GENERATED FROM PYTHON SOURCE LINES 20-21

Define the number of rounds, the target, the domain, the partition, and the algorithm for the learning process

.. GENERATED FROM PYTHON SOURCE LINES 21-28

.. code-block:: default

    T = 1000                                            # the number of rounds is 1000
    target = Garland()                                  # the objective to optimize is Garland
    domain = [[0, 1]]                                   # the domain is [[0, 1]]
    partition = BinaryPartition                         # the partition chosen is BinaryPartition
    algo = T_HOO(rounds=1000, domain=domain, partition=partition)    # the algorithm is T_HOO









.. GENERATED FROM PYTHON SOURCE LINES 29-30

To plot the regret, we can initialize the cumulative regret and the cumulative regret list

.. GENERATED FROM PYTHON SOURCE LINES 30-35

.. code-block:: default


    cumulative_regret = 0
    cumulative_regret_list = []









.. GENERATED FROM PYTHON SOURCE LINES 36-39

In each iteration of the learning process, the algorithm calls the ``pull(t)`` function to obtain a point, and then
the reward for the point is returned to the algorithm by calling ``receive_reward(t, reward)``.
For a stochastic learning process, uniform noise is added to the reward.

.. GENERATED FROM PYTHON SOURCE LINES 39-59

.. code-block:: default


    for t in range(1, T+1):

        point = algo.pull(t)
        reward = target.f(point) + np.random.uniform(-0.1, 0.1)     # uniform noise
        algo.receive_reward(t, reward)

        # the following lines are for the regret
        inst_regret = target.fmax - target.f(point)
        cumulative_regret += inst_regret
        cumulative_regret_list.append(cumulative_regret)


    # plot the regret
    plot_regret(np.array(cumulative_regret_list), name='HOO')








.. image-sg:: /getting_started/auto_examples/images/sphx_glr_plot_HOO_Garland_001.png
   :alt: plot HOO Garland
   :srcset: /getting_started/auto_examples/images/sphx_glr_plot_HOO_Garland_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 60-61

The following lines of code are only for creating thumbnails and do not need to be used

.. GENERATED FROM PYTHON SOURCE LINES 61-72

.. code-block:: default


    # sphinx_gallery_thumbnail_number = 2
    import matplotlib.pyplot as plt

    fig = plt.figure()
    ax = fig.add_subplot(111)

    x = np.linspace(domain[0][0], domain[0][1], 1000)
    z = x * (1-x) * (4 - np.sqrt(np.abs(np.sin(60 * x))))
    ax.plot(x, z, alpha=0.9)
    fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)



.. image-sg:: /getting_started/auto_examples/images/sphx_glr_plot_HOO_Garland_002.png
   :alt: plot HOO Garland
   :srcset: /getting_started/auto_examples/images/sphx_glr_plot_HOO_Garland_002.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 2.101 seconds)


.. _sphx_glr_download_getting_started_auto_examples_plot_HOO_Garland.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_HOO_Garland.py <plot_HOO_Garland.py>`

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

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


.. only:: html

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

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