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

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

.. _sphx_glr_auto_examples_svm_plot_custom_kernel.py:


======================
SVM with custom kernel
======================

Simple usage of Support Vector Machines to classify a sample. It will
plot the decision surface and the support vectors.




.. image:: /auto_examples/svm/images/sphx_glr_plot_custom_kernel_001.png
    :class: sphx-glr-single-img


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

 Out:

 .. code-block:: none







|


.. code-block:: default

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn import svm, datasets

    # import some data to play with
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features. We could
                          # avoid this ugly slicing by using a two-dim dataset
    Y = iris.target


    def my_kernel(X, Y):
        """
        We create a custom kernel:

                     (2  0)
        k(X, Y) = X  (    ) Y.T
                     (0  1)
        """
        M = np.array([[2, 0], [0, 1.0]])
        return np.dot(np.dot(X, M), Y.T)


    h = .02  # step size in the mesh

    # we create an instance of SVM and fit out data.
    clf = svm.SVC(kernel=my_kernel)
    clf.fit(X, Y)

    # Plot the decision boundary. For that, we will assign a color to each
    # point in the mesh [x_min, x_max]x[y_min, y_max].
    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.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired, edgecolors='k')
    plt.title('3-Class classification using Support Vector Machine with custom'
              ' kernel')
    plt.axis('tight')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.371 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_custom_kernel.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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