print(__doc__)
    
    import numpy as np
    import pylab as pl
    from sklearn import svm
    
    # we create 40 separable points
    np.random.seed(0)
    X = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]
    Y = [0] * 20 + [1] * 20
    
    # fit the model
    clf = svm.SVC(kernel='linear')
    clf.fit(X, Y)
    
    # get the separating hyperplane
    w = clf.coef_[0]
    a = -w[0] / w[1]
    xx = np.linspace(-5, 5)
    yy = a * xx - (clf.intercept_[0]) / w[1]
    
    # plot the parallels to the separating hyperplane that pass through the
    # support vectors
    b = clf.support_vectors_[0]
    yy_down = a * xx + (b[1] - a * b[0])
    b = clf.support_vectors_[-1]
    yy_up = a * xx + (b[1] - a * b[0])
    
    # plot the line, the points, and the nearest vectors to the plane
    pl.plot(xx, yy, 'k-')
    pl.plot(xx, yy_down, 'k--')
    pl.plot(xx, yy_up, 'k--')
    
    pl.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1],
               s=80, facecolors='none')
    pl.scatter(X[:, 0], X[:, 1], c=Y, cmap=pl.cm.Paired)
    
    pl.axis('tight')
    pl.show()