make_moons
是函数用来生成数据集,在sklearn.datasets
里,具体用法如下:
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 (default) Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary. 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.
主要参数作用如下:
n_numbers:生成样本数量
shuffle:是否打乱,类似于将数据集random一下
noise:默认是false,数据集是否加入高斯噪声
random_state:生成随机种子,给定一个int型数据,能够保证每次生成数据相同。
sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)
for example:
X, y = datasets.make_moons(500, noise=0.5)
参考文献:
【1】https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
————————————————
版权声明:本文为CSDN博主「禅心001」的原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接及本声明。
原文链接:https://blog.csdn.net/woai8339/article/details/88628509