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