sklearn.datasets.make_swiss_roll¶
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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
