sklearn.datasets.make_blobs

sklearn.datasets.make_blobs(n_samples=100, n_features=2, centers=3, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)[source]

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

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])