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

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

.. _sphx_glr_auto_examples_plot_decision_thresholds.py:


===================================================
Decision Boundaries & Margin Scores
===================================================

Visualizes the discrete decision boundaries generated by ternary weights
:math:`w_j \in \{-1, 0, 1\}` on a 2D synthetic dataset.

.. GENERATED FROM PYTHON SOURCE LINES 9-47



.. image-sg:: /auto_examples/images/sphx_glr_plot_decision_thresholds_001.png
   :alt: TLM Decision Surface (coef=[np.float64(1.0), np.float64(0.0)])
   :srcset: /auto_examples/images/sphx_glr_plot_decision_thresholds_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    Fitted Coefficients: [1. 0.]






|

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.datasets import make_classification
    from sklearn.preprocessing import StandardScaler
    from tlmnet import TlmMilpClassifier

    X, y = make_classification(
        n_samples=100,
        n_features=2,
        n_redundant=0,
        n_informative=2,
        random_state=42,
        class_sep=1.2,
    )
    X = StandardScaler().fit_transform(X)

    clf = TlmMilpClassifier(C=1.0)
    clf.fit(X, y)

    print(f"Fitted Coefficients: {clf.coef_}")

    # Plot Decision Boundary
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 200), np.linspace(y_min, y_max, 200))

    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.figure(figsize=(7, 6))
    plt.contourf(xx, yy, Z, alpha=0.3, cmap=plt.cm.coolwarm)
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.coolwarm, edgecolors="k", linewidths=0.5)
    plt.xlabel("Standardized Feature 1")
    plt.ylabel("Standardized Feature 2")
    plt.title(f"TLM Decision Surface (coef={list(clf.coef_)})")
    plt.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.118 seconds)


.. _sphx_glr_download_auto_examples_plot_decision_thresholds.py:

.. only:: html

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

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

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

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

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

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

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


.. only:: html

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

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