File Content: plot_swissroll.py """ =================================== Swiss Roll reduction with LLE =================================== An illustration of Swiss Roll reduction with locally linear embedding """ # Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr> # License: BSD 3 clause (C) INRIA 2011 print(__doc__) import matplotlib.pyplot as plt # This import is needed to modify the way figure behaves from mpl_toolkits.mplot3d import Axes3D Axes3D #---------------------------------------------------------------------- # Locally linear embedding of the swiss roll from sklearn import manifold, datasets X, color = datasets.samples_generator.make_swiss_roll(n_samples=1500) print("Computing LLE embedding") X_r, err = manifold.locally_linear_embedding(X, n_neighbors=12, n_components=2) print("Done. Reconstruction error: %g" % err) #---------------------------------------------------------------------- # Plot result fig = plt.figure() try: # compatibility matplotlib < 1.0 ax = fig.add_subplot(211, projection='3d') ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral) except: ax = fig.add_subplot(211) ax.scatter(X[:, 0], X[:, 2], c=color, cmap=plt.cm.Spectral) ax.set_title("Original data") ax = fig.add_subplot(212) ax.scatter(X_r[:, 0], X_r[:, 1], c=color, cmap=plt.cm.Spectral) plt.axis('tight') plt.xticks([]), plt.yticks([]) plt.title('Projected data') plt.show() Submit