.. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code or to run this example in your browser via Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_manifold_plot_swissroll.py: =================================== Swiss Roll reduction with LLE =================================== An illustration of Swiss Roll reduction with locally linear embedding .. image:: /auto_examples/manifold/images/sphx_glr_plot_swissroll_001.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none Computing LLE embedding Done. Reconstruction error: 1.27445e-07 | .. code-block:: default # Author: Fabian Pedregosa -- # 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 .. _sphx_glr_download_auto_examples_manifold_plot_swissroll.py: .. 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 ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_swissroll.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_