make_s_curve

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

Generate an S curve dataset.

Read more in the User Guide.

Parameters:

n_samplesint, default=100

The number of sample points on the S curve.

noisefloat, default=0.0

The standard deviation of the gaussian noise.

random_stateint, RandomState instance or None, default=None

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

Returns:

Xndarray of shape (n_samples, 3)

The points.

tndarray of shape (n_samples,)

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

Examples

>>> from sklearn.datasets import make_s_curve
>>> X, t = make_s_curve(noise=0.05, random_state=0)
>>> X.shape
(100, 3)
>>> t.shape
(100,)

Gallery examples

Comparison of Manifold Learning methods

t-SNE: The effect of various perplexity values on the shape