mvpa2.datasets.sources.skl_data.skl_blobs

mvpa2.datasets.sources.skl_data.skl_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)

Generate isotropic Gaussian blobs for clustering.

Parameters:

n_samples : int, optional (default=100)

The total number of points equally divided among clusters.

n_features : int, optional (default=2)

The number of features for each sample.

centers : int or array of shape [n_centers, n_features], optional

(default=3) The number of centers to generate, or the fixed center locations.

cluster_std: float or sequence of floats, optional (default=1.0) :

The standard deviation of the clusters.

center_box: pair of floats (min, max), optional (default=(-10.0, 10.0)) :

The bounding box for each cluster center when centers are generated at random.

shuffle : boolean, optional (default=True)

Shuffle the samples.

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, n_features]

The generated samples.

y : array of shape [n_samples]

The integer labels for cluster membership of each sample.

See also

make_classification

a more intricate variant

Notes

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

Examples

>>> from sklearn.datasets.samples_generator import make_blobs
>>> X, y = make_blobs(n_samples=10, centers=3, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 0, 1, 0, 2, 2, 2, 1, 1, 0])