Plot the maximum margin separating hyperplane within a two-class separable dataset using a linear Support Vector Machines classifier trained using SGD.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | print(__doc__)import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import SGDClassifierfrom sklearn.datasets.samples_generator import make_blobs# we create 50 separable pointsX, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)# fit the modelclf = SGDClassifier(loss="hinge", alpha=0.01, n_iter=200, fit_intercept=True)clf.fit(X, Y)# plot the line, the points, and the nearest vectors to the planexx = 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)plt.axis('tight')plt.show() |
Total running time of the script: (0 minutes 0.067 seconds)
Download Python source code:
plot_sgd_separating_hyperplane.py
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plot_sgd_separating_hyperplane.ipynb
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