*#1. 参数
A simple toy dataset to visualize clustering and classification algorithms. Read more in the User Guide. Parameters n_samples： int or two-element tuple, optional (default=100) If int, the total number of points generated. If two-element tuple, number of points in each of two moons. 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, default=None Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary. Returns Xarray of shape [n_samples, 2] The generated samples. yarray of shape [n_samples] The integer labels (0 or 1) for class membership of each sample. 参数说明 n_samples ： 样本数，默认100个 shuffle：是否随机打乱， 默认true noise： double 是否添加噪音 random_state： 随机种子，给个int， 每次产生相同随机数。 返回： X : [样本数， 2] y:[样本数]， 0，1 example： import numpy as np from sklearn.datasets import make_moons import matplotlib.pyplot as plt # 手动生成一个随机的平面点分布，并画出来 np.random.seed(0) X, y = make_moons(200, noise=0.50) plt.scatter(X[:,0], X[:,1], s=10, c=y, cmap=plt.cm.Spectral) plt.show()