sklearn-ml/example/plot_separating_hyperplane.ipynb

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        "\n=========================================\nSVM: Maximum margin separating hyperplane\n=========================================\n\nPlot the maximum margin separating hyperplane within a two-class\nseparable dataset using a Support Vector Machine classifier with\nlinear kernel.\n\n"
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        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import svm\nfrom sklearn.datasets import make_blobs\n\n\n# we create 40 separable points\nX, y = make_blobs(n_samples=40, centers=2, random_state=6)\n\n# fit the model, don't regularize for illustration purposes\nclf = svm.SVC(kernel='linear', C=1000)\nclf.fit(X, y)\n\nplt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)\n\n# plot the decision function\nax = plt.gca()\nxlim = ax.get_xlim()\nylim = ax.get_ylim()\n\n# create grid to evaluate model\nxx = np.linspace(xlim[0], xlim[1], 30)\nyy = np.linspace(ylim[0], ylim[1], 30)\nYY, XX = np.meshgrid(yy, xx)\nxy = np.vstack([XX.ravel(), YY.ravel()]).T\nZ = clf.decision_function(xy).reshape(XX.shape)\n\n# plot decision boundary and margins\nax.contour(XX, YY, Z, colors='k', levels=[-1, 0, 1], alpha=0.5,\n           linestyles=['--', '-', '--'])\n# plot support vectors\nax.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100,\n           linewidth=1, facecolors='none')\nplt.show()"
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