sklearn.datasets.make_swiss_roll

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

Generate a swiss roll dataset.

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, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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

[R119]

S. Marsland, “Machine Learning: An Algorithmic Perspective”, Chapter 10, 2009. http://www-ist.massey.ac.nz/smarsland/Code/10/lle.py

Examples using sklearn.datasets.make_swiss_roll

Hierarchical clustering: structured vs unstructured ward

Swiss Roll reduction with LLE