sklearn --make

*#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]， 0，1example：

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()

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