mvpa2.datasets.sources.skl_data.skl_circles

mvpa2.datasets.sources.skl_data.skl_circles(n_samples=100, shuffle=True, noise=None, random_state=None, factor=0.8)

Make a large circle containing a smaller circle in 2d.

A simple toy dataset to visualize clustering and classification algorithms.

Read more in the User Guide.

Parameters:

n_samples : int, optional (default=100)

The total number of points generated.

shuffle : bool, optional (default=True)

Whether to shuffle the samples.

noise : double or None (default=None)

Standard deviation of Gaussian noise added to the data.

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.

factor : double < 1 (default=.8)

Scale factor between inner and outer circle.

Returns:

X : array of shape [n_samples, 2]

The generated samples.

y : array of shape [n_samples]

The integer labels (0 or 1) for class membership of each sample.

Notes

This function has been auto-generated by wrapping make_circles() 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.