47 lines
1.2 KiB
Python
47 lines
1.2 KiB
Python
"""
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===================================
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Swiss Roll reduction with LLE
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===================================
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An illustration of Swiss Roll reduction
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with locally linear embedding
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"""
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# Author: Fabian Pedregosa -- <fabian.pedregosa@inria.fr>
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# License: BSD 3 clause (C) INRIA 2011
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print(__doc__)
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import matplotlib.pyplot as plt
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# This import is needed to modify the way figure behaves
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from mpl_toolkits.mplot3d import Axes3D
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Axes3D
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#----------------------------------------------------------------------
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# Locally linear embedding of the swiss roll
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from sklearn import manifold, datasets
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X, color = datasets.make_swiss_roll(n_samples=1500)
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print("Computing LLE embedding")
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X_r, err = manifold.locally_linear_embedding(X, n_neighbors=12,
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n_components=2)
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print("Done. Reconstruction error: %g" % err)
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#----------------------------------------------------------------------
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# Plot result
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fig = plt.figure()
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ax = fig.add_subplot(211, projection='3d')
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ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=color, cmap=plt.cm.Spectral)
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ax.set_title("Original data")
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ax = fig.add_subplot(212)
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ax.scatter(X_r[:, 0], X_r[:, 1], c=color, cmap=plt.cm.Spectral)
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plt.axis('tight')
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plt.xticks([]), plt.yticks([])
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plt.title('Projected data')
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plt.show()
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