Swiss Roll reduction with LLE

An illustration of Swiss Roll reduction with locally linear embedding

Original data, Projected data

Out:

Computing LLE embedding
Done. Reconstruction error: 9.03879e-08

# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
# License: BSD 3 clause (C) INRIA 2011

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.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()

ax = fig.add_subplot(211, projection="3d")
ax.scatter(X[:, 0], X[:, 1], 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()

Total running time of the script: ( 0 minutes 0.426 seconds)

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