我有一个向量列表：

>>> import numpy as np

>>> num_dim, num_data = 10, 5

>>> data = np.random.rand(num_data, num_dim)

>>> data

array([[ 0.0498063 , 0.18659463, 0.30563225, 0.99681495, 0.35692358,

0.47759707, 0.85755606, 0.39373145, 0.54677259, 0.5168117 ],

[ 0.18034536, 0.25935541, 0.79718771, 0.28604057, 0.17165293,

0.90277904, 0.94016733, 0.15689765, 0.79758063, 0.41250143],

[ 0.80716045, 0.84998745, 0.17893211, 0.36206016, 0.69604008,

0.27249491, 0.92570247, 0.446499 , 0.34424945, 0.08576628],

[ 0.35311449, 0.67901964, 0.71023927, 0.03120829, 0.72864953,

0.60717032, 0.8020118 , 0.36047207, 0.46362718, 0.12441942],

[ 0.1955419 , 0.02702753, 0.76828842, 0.5438226 , 0.69407709,

0.20865243, 0.12783666, 0.81486189, 0.95583274, 0.30157658]])

从数据中,我需要随机选择3个向量,我可以用：

>>> import random

>>> random.sample(data, 3)

[array([ 0.80716045, 0.84998745, 0.17893211, 0.36206016, 0.69604008,

0.27249491, 0.92570247, 0.446499 , 0.34424945, 0.08576628]), array([ 0.18034536, 0.25935541, 0.79718771, 0.28604057, 0.17165293,

0.90277904, 0.94016733, 0.15689765, 0.79758063, 0.41250143]), array([ 0.35311449, 0.67901964, 0.71023927, 0.03120829, 0.72864953,

0.60717032, 0.8020118 , 0.36047207, 0.46362718, 0.12441942])]

我在http://docs.scipy.org/doc/numpy/reference/routines.random.html检查了文档,我无法弄清楚numpy中是否存在random.sample()这样的功能.

numpy.random.sample()与random.sample()不一样吗？

在numpy中是否存在random.sample()的等价性？

[email protected],可以这样做：

>>> data[np.random.choice(len(data), size=3, replace=False)]

array([[ 0.80716045, 0.84998745, 0.17893211, 0.36206016, 0.69604008,

0.27249491, 0.92570247, 0.446499 , 0.34424945, 0.08576628],

[ 0.35311449, 0.67901964, 0.71023927, 0.03120829, 0.72864953,

0.60717032, 0.8020118 , 0.36047207, 0.46362718, 0.12441942],

[ 0.1955419 , 0.02702753, 0.76828842, 0.5438226 , 0.69407709,

0.20865243, 0.12783666, 0.81486189, 0.95583274, 0.30157658]])

从docs：

numpy.random.choice(a, size=None, replace=True, p=None)

Generates a random sample from a given 1-D array

np.random.choice(data,size = 3,replace = False)从数据的索引列表中选择3个元素而不进行替换.

然后data […]对索引进行切片并检索用np.random.choice选择的索引.