sklearn.datasets.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_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.

Examples using sklearn.datasets.make_s_curve

Comparison of Manifold Learning methods