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, return_centers=False)[source]¶

Generate isotropic Gaussian blobs for clustering.

Read more in the User Guide.

Parameters

n_samplesint 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.

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

n_featuresint, optional (default=2)

The number of features for each sample.

centersint 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_stdfloat or sequence of floats, optional (default=1.0)

The standard deviation of the clusters.

center_boxpair of floats (min, max), optional (default=(-10.0, 10.0))

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

shuffleboolean, optional (default=True)

Shuffle the samples.

random_stateint, RandomState instance, default=None

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

return_centersbool, optional (default=False)

If True, then return the centers of each cluster

New in version 0.23.

Returns

Xarray of shape [n_samples, n_features]

The generated samples.

yarray of shape [n_samples]

The integer labels for cluster membership of each sample.

centersarray, 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])

Examples using sklearn.datasets.make_blobs¶

Release Highlights for scikit-learn 0.23¶

Probability calibration of classifiers¶

Probability Calibration for 3-class classification¶

Normal and Shrinkage Linear Discriminant Analysis for classification¶

A demo of the mean-shift clustering algorithm¶

Demonstration of k-means assumptions¶

Demo of affinity propagation clustering algorithm¶

Demo of DBSCAN clustering algorithm¶

Inductive Clustering¶

Compare BIRCH and MiniBatchKMeans¶

Comparison of the K-Means and MiniBatchKMeans clustering algorithms¶

Comparing different hierarchical linkage methods on toy datasets¶

Selecting the number of clusters with silhouette analysis on KMeans clustering¶

Comparing different clustering algorithms on toy datasets¶

Plot randomly generated classification dataset¶

SGD: Maximum margin separating hyperplane¶

Plot multinomial and One-vs-Rest Logistic Regression¶

Comparing anomaly detection algorithms for outlier detection on toy datasets¶

Demonstrating the different strategies of KBinsDiscretizer¶

SVM: Maximum margin separating hyperplane¶

Plot the support vectors in LinearSVC¶

SVM Tie Breaking Example¶

SVM: Separating hyperplane for unbalanced classes¶