.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/05_customization/plot_03_discriminants.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_03_discriminants.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_05_customization_plot_03_discriminants.py:


======================
Discriminant Functions
======================

.. GENERATED FROM PYTHON SOURCE LINES 6-16

.. code-block:: Python


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

    from sklvq import GLVQ
    from sklvq.discriminants import DiscriminantBaseClass

    data, labels = load_iris(return_X_y=True)








.. GENERATED FROM PYTHON SOURCE LINES 17-20

The sklvq package contains a single discriminant function and additions are very welcome. Note
that they should work with the sklvq.objectives.GeneralizedLearningObjective, i.e.,
passing additional or different arguments is not possible.

.. GENERATED FROM PYTHON SOURCE LINES 20-48

.. code-block:: Python



    # The discriminative function is depended on the objective function. This determines the
    # parameters of the call and gradient. See sklvq.objective.GeneralizedLearningObjective.
    class CustomRelativeDistance(DiscriminantBaseClass):
        def __call__(self, dist_same: np.ndarray, dist_diff: np.ndarray) -> np.ndarray:
            # dist_same = distance to prototype with same label as X.
            # dist_diff = distance to prototype with different label as X.
            return (dist_same - dist_diff) / (dist_same + dist_diff)

        def gradient(self, dist_same: np.ndarray, dist_diff: np.ndarray, winner_same: bool) -> np.ndarray:
            # Winner_same is an boolean flag to indicate if the considered prototype has the same or
            # a different label compared to the considered X.
            if winner_same:
                return _gradient_same(dist_same, dist_diff)
            return _gradient_diff(dist_same, dist_diff)


    # Gradient depends on if the label is the same or different
    def _gradient_same(dist_same: np.ndarray, dist_diff: np.ndarray) -> np.ndarray:
        return 2 * dist_diff / (dist_same + dist_diff) ** 2


    # Gradient depends on if the label is the same or different
    def _gradient_diff(dist_same: np.ndarray, dist_diff: np.ndarray) -> np.ndarray:
        return -2 * dist_same / (dist_same + dist_diff) ** 2









.. GENERATED FROM PYTHON SOURCE LINES 49-51

The CustomRelativeDistance 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 51-66

.. code-block:: Python


    model = GLVQ(
        discriminant_type=CustomRelativeDistance,
        distance_type="squared-euclidean",
        activation_type="sigmoid",
        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       0.92      0.94      0.93        50
               2       0.94      0.92      0.93        50

        accuracy                           0.95       150
       macro avg       0.95      0.95      0.95       150
    weighted avg       0.95      0.95      0.95       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_03_discriminants.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_03_discriminants.ipynb <plot_03_discriminants.ipynb>`

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

      :download:`Download Python source code: plot_03_discriminants.py <plot_03_discriminants.py>`

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

      :download:`Download zipped: plot_03_discriminants.zip <plot_03_discriminants.zip>`


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