.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/svm/plot_linearsvc_support_vectors.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_svm_plot_linearsvc_support_vectors.py: ===================================== Plot the support vectors in LinearSVC ===================================== Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide the support vectors. This example demonstrates how to obtain the support vectors in LinearSVC. .. GENERATED FROM PYTHON SOURCE LINES 11-58 .. image-sg:: /auto_examples/svm/images/sphx_glr_plot_linearsvc_support_vectors_001.png :alt: C=1, C=100 :srcset: /auto_examples/svm/images/sphx_glr_plot_linearsvc_support_vectors_001.png :class: sphx-glr-single-img .. code-block:: default import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.svm import LinearSVC from sklearn.inspection import DecisionBoundaryDisplay X, y = make_blobs(n_samples=40, centers=2, random_state=0) plt.figure(figsize=(10, 5)) for i, C in enumerate([1, 100]): # "hinge" is the standard SVM loss clf = LinearSVC(C=C, loss="hinge", random_state=42).fit(X, y) # obtain the support vectors through the decision function decision_function = clf.decision_function(X) # we can also calculate the decision function manually # decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0] # The support vectors are the samples that lie within the margin # boundaries, whose size is conventionally constrained to 1 support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0] support_vectors = X[support_vector_indices] plt.subplot(1, 2, i + 1) plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired) ax = plt.gca() DecisionBoundaryDisplay.from_estimator( clf, X, ax=ax, grid_resolution=50, plot_method="contour", colors="k", levels=[-1, 0, 1], alpha=0.5, linestyles=["--", "-", "--"], ) plt.scatter( support_vectors[:, 0], support_vectors[:, 1], s=100, linewidth=1, facecolors="none", edgecolors="k", ) plt.title("C=" + str(C)) plt.tight_layout() plt.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.119 seconds) .. _sphx_glr_download_auto_examples_svm_plot_linearsvc_support_vectors.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.1.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_linearsvc_support_vectors.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_linearsvc_support_vectors.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_linearsvc_support_vectors.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_