Swiss Roll reduction with LLE¶
An illustration of Swiss Roll reduction with locally linear embedding
Script output:
Computing LLE embedding
Done. Reconstruction error: 9.74347e-08
Python source code: plot_swissroll.py
# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD, (C) INRIA 2011
print __doc__
import pylab as pl
# This import is needed to modify the way figure behaves
from mpl_toolkits.mplot3d import 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, out_dim=2)
print "Done. Reconstruction error: %g" % err
#----------------------------------------------------------------------
# Plot result
fig = pl.figure()
try:
# compatibility matplotlib < 1.0
ax = fig.add_subplot(211, projection='3d')
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=pl.cm.Spectral)
except:
ax = fig.add_subplot(211)
ax.scatter(X[:, 0], X[:, 2], c=color, cmap=pl.cm.Spectral)
ax.set_title("Original data")
ax = fig.add_subplot(212)
ax.scatter(X_r[:, 0], X_r[:, 1], c=color, cmap=pl.cm.Spectral)
pl.axis('tight')
pl.xticks([]), pl.yticks([])
pl.title('Projected data')
pl.show()