mvpa2.datasets.sources.skl_data.skl_swiss_roll(n_samples=100, noise=0.0, random_state=None)

Generate a swiss roll dataset.


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.


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.


This function has been auto-generated by wrapping make_swiss_roll() from the sklearn package. The documentation of this function has been kept verbatim. Consequently, the actual return value is not as described in the documentation, but the data is returned as a PyMVPA dataset.


[R34]S. Marsland, “Machine Learning: An Algorithmic Perspective”, Chapter 10, 2009.