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.

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])