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Note

Click :ref:`here <sphx_glr_download_auto_examples_manifold_plot_swissroll.py>` to download the full example code or to run this example in your browser via Binder

.. rst-class:: sphx-glr-example-title

Swiss Roll reduction with LLE

An illustration of Swiss Roll reduction with locally linear embedding

/auto_examples/manifold/images/sphx_glr_plot_swissroll_001.png

.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    Computing LLE embedding
    Done. Reconstruction error: 1.27445e-07






# Author: Fabian Pedregosa -- <[email protected]>
# 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.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()
.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.723 seconds)

Estimated memory usage: 9 MB

.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/manifold/plot_swissroll.ipynb
      :width: 150 px


  .. container:: sphx-glr-download

     :download:`Download Python source code: plot_swissroll.py <plot_swissroll.py>`



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: plot_swissroll.ipynb <plot_swissroll.ipynb>`

.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_