.. _examples-miml-classification-svm: ************************************* Support vector machine ************************************* The basic support vector machine (SVM) is a binary linear classifier which chooses the hyperplane that represents the largest separation, or margin, between the two classes. If such a hyperplane exists, it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier. If there exists no hyperplane that can perfectly split the positive and negative instances, the soft margin method will choose a hyperplane that splits the instances as cleanly as possible, while still maximizing the distance to the nearest cleanly split instances. :: from miml import datasets from miml.classification import SVM fn = os.path.join(datasets.get_data_home(), 'classification', 'toy', 'toy-test.txt') df = DataFrame.read_table(fn, header=None, names=['x1','x2'], format='%2f', index_col=0) X = df.values y = array(df.index.data) model = SVM(kernel='gaussian', C=10, strategy='one_vs_one', epochs=1, sigma=1.0) model.fit(X, y) # 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]. x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 n = 50 # size in the mesh xx, yy = np.meshgrid(np.linspace(x_min, x_max, n), np.linspace(y_min, y_max, n)) data = np.vstack((xx.flatten(), yy.flatten())).T Z = model.predict(data) # Put the result into a color plot Z = Z.reshape(xx.shape) #Plot # Create color maps cmap_light = ['#FFAAAA', '#AAAAFF'] cmap_bold = ['#FF0000', '#0000FF'] imshow(xx[0,:], yy[:,0], Z, colors=cmap_light) # Plot also the training points ls = plt.scatter(X[:, 0], X[:, 1], c=y, edgecolor=None, s=3, levels=[0,1], colors=cmap_bold) plt.contour(xx[0,:], yy[:,0], Z, [0.5], color='k', smooth=False) plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.title("SVM example") .. image:: ../../../_static/miml/svm.png