从均匀分布python生成随机数的最快方法 [英] Fastest way to generate random number from uniform distribtution python

问题描述

我需要根据均匀分布生成大量(100万)随机数.我做了一些实验，使用

从[0-999]上的均匀分布生成60K随机数

I need to generate large(1 Million) number of random numbers from a uniform distribution. I did some experiments generating 60K random number from uniform distribution over [0-999] using

scipy.stats.randint(0,1000).rvs(60000)

，大约花了0.6秒.然后我使用Boost库尝试了同样的事情

and it took ~0.6s. Then I tried same thing using boost library

boost::random::uniform_int_distribution

，大约用了0.25秒.所以我的问题是-使用python(libraries)是否有更好的方法来做我想做的事情?任何建议，不胜感激.谢谢.

and it took ~0.25s. So my question is this- Are there better ways of doing what I want to do using python(libraries)? Any suggestions are greatly appreciated. Thanks.

推荐答案

使用numpy.random.randint:

numpy.random.randint(low=1, high=1001, size=60000)

在我的试用中，它的运行速度比scipy.stats.randint快300倍，而c3是为通用性而非速度而设计的系统的一部分. (另外，scipy.stats.randint(0,1000).rvs(60000)会生成0到999的整数，而不是1到1000的整数.)

On my trials, this runs about 300 times faster than scipy.stats.randint, which is part of a system designed more for generality than speed. (Also, scipy.stats.randint(0,1000).rvs(60000) is generating integers from 0 to 999, not 1 to 1000.)

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