.. note::
    :class: sphx-glr-download-link-note

    Click :ref:`here <sphx_glr_download_auto_examples_svm_plot_oneclass.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_svm_plot_oneclass.py:


==========================================
One-class SVM with non-linear kernel (RBF)
==========================================

An example using a one-class SVM for novelty detection.

:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
algorithm that learns a decision function for novelty detection:
classifying new data as similar or different to the training set.



.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    import matplotlib.font_manager
    from sklearn import svm

    xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))
    # Generate train data
    X = 0.3 * np.random.randn(100, 2)
    X_train = np.r_[X + 2, X - 2]
    # Generate some regular novel observations
    X = 0.3 * np.random.randn(20, 2)
    X_test = np.r_[X + 2, X - 2]
    # Generate some abnormal novel observations
    X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))

    # fit the model
    clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
    clf.fit(X_train)
    y_pred_train = clf.predict(X_train)
    y_pred_test = clf.predict(X_test)
    y_pred_outliers = clf.predict(X_outliers)
    n_error_train = y_pred_train[y_pred_train == -1].size
    n_error_test = y_pred_test[y_pred_test == -1].size
    n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size

    # plot the line, the points, and the nearest vectors to the plane
    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.title("Novelty Detection")
    plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)
    a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')
    plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')

    s = 40
    b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s, edgecolors='k')
    b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='blueviolet', s=s,
                     edgecolors='k')
    c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s,
                    edgecolors='k')
    plt.axis('tight')
    plt.xlim((-5, 5))
    plt.ylim((-5, 5))
    plt.legend([a.collections[0], b1, b2, c],
               ["learned frontier", "training observations",
                "new regular observations", "new abnormal observations"],
               loc="upper left",
               prop=matplotlib.font_manager.FontProperties(size=11))
    plt.xlabel(
        "error train: %d/200 ; errors novel regular: %d/40 ; "
        "errors novel abnormal: %d/40"
        % (n_error_train, n_error_test, n_error_outliers))
    plt.show()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_oneclass.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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