.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/05_customization/plot_01_activations.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_05_customization_plot_01_activations.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_05_customization_plot_01_activations.py:


====================
Activation Functions
====================

.. GENERATED FROM PYTHON SOURCE LINES 6-18

.. code-block:: Python


    from typing import Union

    import numpy as np
    from sklearn.datasets import load_iris
    from sklearn.metrics import classification_report

    from sklvq import GLVQ
    from sklvq.activations import ActivationBaseClass

    data, labels = load_iris(return_X_y=True)








.. GENERATED FROM PYTHON SOURCE LINES 19-24

The sklvq contains already a few activation function. Please see the API reference
under Documentation. However, it is fairly easy to create your own. The package
works with callable classes and provides a base class for convenience. The base class
for the activation functions is sklvq.activations.ActivationBaseClass` and does
nothing more then tell you to implement a `__call__()` and `gradient()` method.

.. GENERATED FROM PYTHON SOURCE LINES 24-44

.. code-block:: Python



    # This is the implementation of sklvq.activations.Sigmoid with some additional comments
    class CustomSigmoid(ActivationBaseClass):
        # Activation callables can have a custom init of which the parameters can be passed
        # through the `activation_params (Dict)' parameter of the LVQ algorithms. Or the
        # object can just be initialized before hand.
        def __init__(self, beta: Union[int, float] = 1):
            self.beta = beta

        # The activation call function needs to apply the activation elementwise on x.
        def __call__(self, x: np.ndarray) -> np.ndarray:
            return np.asarray(1 / (np.exp(-self.beta * x) + 1))

        # The gradient is the elementwise derivative of the activation function.
        def gradient(self, x: np.ndarray) -> np.ndarray:
            exp = np.exp(self.beta * x)
            return np.asarray((self.beta * exp) / (exp + 1) ** 2)









.. GENERATED FROM PYTHON SOURCE LINES 45-48

The CustomSigmoid above, accompanied with some tests and documentation, would make a
great addition to the sklvq package. However, it can also directly be passed to
the algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 48-58

.. code-block:: Python


    model = GLVQ(activation_type=CustomSigmoid, activation_params={"beta": 2})

    model.fit(data, labels)

    # Predict the labels using the trained model
    predicted_labels = model.predict(data)

    # Print a classification report (sklearn)
    print(classification_report(labels, predicted_labels))




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

 .. code-block:: none

                  precision    recall  f1-score   support

               0       1.00      1.00      1.00        50
               1       0.94      0.94      0.94        50
               2       0.94      0.94      0.94        50

        accuracy                           0.96       150
       macro avg       0.96      0.96      0.96       150
    weighted avg       0.96      0.96      0.96       150






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

   **Total running time of the script:** (0 minutes 0.146 seconds)


.. _sphx_glr_download_auto_examples_05_customization_plot_01_activations.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

      :download:`Download Jupyter notebook: plot_01_activations.ipynb <plot_01_activations.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_01_activations.py <plot_01_activations.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_01_activations.zip <plot_01_activations.zip>`


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

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