import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import SGDClassifier from sklearn.datasets.samples_generator import make_blobs def plot_sgd_separator(): # we create 50 separable points X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60) # fit the model clf = 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 plane xx = 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' ax = plt.axes() ax.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles) ax.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired) ax.axis('tight') if __name__ == '__main__': plot_sgd_separator() plt.show()