.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/linear_model/plot_sgd_separating_hyperplane.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_linear_model_plot_sgd_separating_hyperplane.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_linear_model_plot_sgd_separating_hyperplane.py:


=========================================
SGD: Maximum margin separating hyperplane
=========================================

Plot the maximum margin separating hyperplane within a two-class
separable dataset using a linear Support Vector Machines classifier
trained using SGD.

.. GENERATED FROM PYTHON SOURCE LINES 11-45



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_separating_hyperplane_001.png
   :alt: plot sgd separating hyperplane
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_sgd_separating_hyperplane_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import make_blobs
    from sklearn.linear_model import SGDClassifier

    # we create 50 separable points
    X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)

    # fit the model
    clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200)

    clf.fit(X, Y)

    # plot the line, the points, and the nearest vectors to the plane
    xx = np.linspace(-1, 5, 10)
    yy = np.linspace(-1, 5, 10)

    X1, X2 = np.meshgrid(xx, yy)
    Z = np.empty(X1.shape)
    for (i, j), val in np.ndenumerate(X1):
        x1 = val
        x2 = X2[i, j]
        p = clf.decision_function([[x1, x2]])
        Z[i, j] = p[0]
    levels = [-1.0, 0.0, 1.0]
    linestyles = ["dashed", "solid", "dashed"]
    colors = "k"
    plt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)
    plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolor="black", s=20)

    plt.axis("tight")
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.067 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_sgd_separating_hyperplane.py:

.. only:: html

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

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/linear_model/plot_sgd_separating_hyperplane.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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


.. include:: plot_sgd_separating_hyperplane.recommendations


.. only:: html

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

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