make_circles

sklearn.datasets.make_circles(n_samples=100, *, shuffle=True, noise=None, random_state=None, factor=0.8)[source]

Make a large circle containing a smaller circle in 2d.

A simple toy dataset to visualize clustering and classification algorithms.

Read more in the User Guide.

Parameters:

n_samplesint or tuple of shape (2,), dtype=int, default=100

If int, it is the total number of points generated. For odd numbers, the inner circle will have one point more than the outer circle. If two-element tuple, number of points in outer circle and inner circle.

Changed in version 0.23: Added two-element tuple.

shufflebool, default=True

Whether to shuffle the samples.

noisefloat, default=None

Standard deviation of Gaussian noise added to the data.

random_stateint, RandomState instance or None, default=None

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

factorfloat, default=.8

Scale factor between inner and outer circle in the range [0, 1).

Returns:

Xndarray of shape (n_samples, 2)

The generated samples.

yndarray of shape (n_samples,)

The integer labels (0 or 1) for class membership of each sample.

Examples

>>> from sklearn.datasets import make_circles
>>> X, y = make_circles(random_state=42)
>>> X.shape
(100, 2)
>>> y.shape
(100,)
>>> list(y[:5])
[np.int64(1), np.int64(1), np.int64(1), np.int64(0), np.int64(0)]

Gallery examples

Classifier comparison

Comparing different clustering algorithms on toy datasets

Comparing different hierarchical linkage methods on toy datasets

Kernel PCA

Hashing feature transformation using Totally Random Trees

t-SNE: The effect of various perplexity values on the shape

Compare Stochastic learning strategies for MLPClassifier

Varying regularization in Multi-layer Perceptron

Feature discretization

Label Propagation learning a complex structure