.. _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.




.. image:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_separating_hyperplane_001.png
    :align: center





.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.linear_model import SGDClassifier
    from sklearn.datasets.samples_generator import make_blobs

    # 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, fit_intercept=True)
    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()

**Total running time of the script:** ( 0 minutes  0.029 seconds)



.. container:: sphx-glr-footer


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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

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

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