sklearn.datasets.make_moons

sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)

Make two interleaving half circles

A simple toy dataset to visualize clustering and classification algorithms. Read more in the User Guide.

Parameters:

n_samples : int, optional (default=100)

The total number of points generated.

shuffle : bool, optional (default=True)

Whether to shuffle the samples.

noise : double or None (default=None)

Standard deviation of Gaussian noise added to the data.

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, 2]

The generated samples.

y : array of shape [n_samples]

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

Examples using sklearn.datasets.make_moons

Classifier comparison

Comparing different clustering algorithms on toy datasets

Varying regularization in Multi-layer Perceptron

Compare Stochastic learning strategies for MLPClassifier