File Content: plot_sgd_iris.py """ ======================================== Plot multi-class SGD on the iris dataset ======================================== Plot decision surface of multi-class SGD on iris dataset. The hyperplanes corresponding to the three one-versus-all (OVA) classifiers are represented by the dashed lines. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.linear_model import SGDClassifier # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. We could # avoid this ugly slicing by using a two-dim dataset y = iris.target colors = "bry" # shuffle idx = np.arange(X.shape[0]) np.random.seed(13) np.random.shuffle(idx) X = X[idx] y = y[idx] # standardize mean = X.mean(axis=0) std = X.std(axis=0) X = (X - mean) / std h = .02 # step size in the mesh clf = SGDClassifier(alpha=0.001, n_iter=100).fit(X, y) # create a mesh to plot in x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired) plt.axis('tight') # Plot also the training points for i, color in zip(clf.classes_, colors): idx = np.where(y == i) plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i], cmap=plt.cm.Paired) plt.title("Decision surface of multi-class SGD") plt.axis('tight') # Plot the three one-against-all classifiers xmin, xmax = plt.xlim() ymin, ymax = plt.ylim() coef = clf.coef_ intercept = clf.intercept_ def plot_hyperplane(c, color): def line(x0): return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1] plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color) for i, color in zip(clf.classes_, colors): plot_hyperplane(i, color) plt.legend() plt.show() Submit