sklearn.datasets.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) [source]

Generate isotropic Gaussian blobs for clustering.

Read more in the User Guide.

Parameters:

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.

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])
>>> 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])

Examples using sklearn.datasets.make_blobs