Swiss Roll reduction with LLE¶

An illustration of Swiss Roll reduction with locally linear embedding

Script output:

Computing LLE embedding
Done. Reconstruction error: 1.27216e-07

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()

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Swiss Roll reduction with LLE¶