make_blobs#

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

Generate isotropic Gaussian blobs for clustering.

For an example of usage, see Plot randomly generated classification dataset.

Read more in the User Guide.

Parameters:
n_samplesint or array-like, default=100

If int, it is the total number of points equally divided among clusters. If array-like, each element of the sequence indicates the number of samples per cluster.

Changed in version v0.20: one can now pass an array-like to the n_samples parameter

n_featuresint, default=2

The number of features for each sample.

centersint or array-like of shape (n_centers, n_features), default=None

The number of centers to generate, or the fixed center locations. If n_samples is an int and centers is None, 3 centers are generated. If n_samples is array-like, centers must be either None or an array of length equal to the length of n_samples.

cluster_stdfloat or array-like of float, default=1.0

The standard deviation of the clusters.

center_boxtuple of float (min, max), default=(-10.0, 10.0)

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

shufflebool, default=True

Shuffle the samples.

random_stateint, RandomState instance or None, default=None

Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

return_centersbool, default=False

If True, then return the centers of each cluster.

Added in version 0.23.

Returns:
Xndarray of shape (n_samples, n_features)

The generated samples.

yndarray of shape (n_samples,)

The integer labels for cluster membership of each sample.

centersndarray of shape (n_centers, n_features)

The centers of each cluster. Only returned if return_centers=True.

See also

make_classification

A more intricate variant.

Examples

>>> from sklearn.datasets 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])
>>> X, y = make_blobs(n_samples=[3, 3, 4], centers=None, n_features=2,
...                   random_state=0)
>>> print(X.shape)
(10, 2)
>>> y
array([0, 1, 2, 0, 2, 2, 2, 1, 1, 0])
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