mars.learn.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)[source]#

Generate isotropic Gaussian blobs for clustering.

Read more in the User Guide.

  • n_samples (int or array-like, optional (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.

  • 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=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_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 (default)) – Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.


  • X (tensor of shape [n_samples, n_features]) – The generated samples.

  • y (tensor of shape [n_samples]) – The integer labels for cluster membership of each sample.


>>> 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])

See also


a more intricate variant