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sklearn.datasets.make_swiss_roll

sklearn.datasets.make_swiss_roll(n_samples=100, noise=0.0, random_state=None) [source]

Generate a swiss roll dataset.

Read more in the User Guide.

Parameters:
n_samples : int, optional (default=100)

The number of sample points on the S curve.

noise : float, optional (default=0.0)

The standard deviation of the gaussian noise.

random_state : int, RandomState instance or None (default)

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:
X : array of shape [n_samples, 3]

The points.

t : array of shape [n_samples]

The univariate position of the sample according to the main dimension of the points in the manifold.

Notes

The algorithm is from Marsland [1].

References

[1] S. Marsland, “Machine Learning: An Algorithmic Perspective”, Chapter 10, 2009. http://seat.massey.ac.nz/personal/s.r.marsland/Code/10/lle.py

Examples using sklearn.datasets.make_swiss_roll

© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html