EXA Add an example about how to obtain the support vectors in… (scikit-learn#14355)

Author: qinhanmin2014

examples/svm/plot_linearsvc_support_vectors.py

@@ -0,0 +1,45 @@
+"""
+=====================================
+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.
+
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_blobs
+from sklearn.svm import LinearSVC
+
+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]
+    support_vector_indices = np.where((2 * y - 1) * decision_function <= 1)[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()
+    xlim = ax.get_xlim()
+    ylim = ax.get_ylim()
+    xx, yy = np.meshgrid(np.linspace(xlim[0], xlim[1], 50),
+                         np.linspace(ylim[0], ylim[1], 50))
+    Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
+    Z = Z.reshape(xx.shape)
+    plt.contour(xx, yy, Z, 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()