Python中numpy.random和random.random之间的区别

本文介绍了Python中numpy.random和random.random之间的区别的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述

我在Python中有一个很大的脚本.我从别人的代码中获得启发，因此最终我在某些方面使用了numpy.random模块(例如，用于创建从二项式分布中获取的随机数的数组)，而在其他地方，我使用了模块random.random. >

有人可以告诉我两者之间的主要区别吗? 在这两个文档的网页上，对我来说，numpy.random似乎有更多的方法，但是我不清楚随机数的生成方式有何不同.

我问的原因是因为我需要为调试目的播种我的主程序.除非我在要导入的所有模块中使用相同的随机数生成器，否则它是行不通的吗?

此外，我在另一篇文章中阅读了有关不使用numpy.random.seed()的讨论，但是我并不真正理解为什么这是一个糟糕的主意.如果有人向我解释为什么会这样，我将不胜感激.

解决方案

您已经做出了许多正确的观察！

除非您想为两个随机生成器都作为种子，从长远来看，选择一个或另一个生成器可能更简单.但是，如果您确实需要同时使用两者，那么是的，您还需要同时对两者进行播种，因为它们彼此独立地生成随机数.

对于numpy.random.seed()，主要困难在于它不是线程安全的-也就是说，如果您具有，因为如果两个不同的线程同时执行该函数，则不能保证它们能正常工作.如果您不使用线程，并且可以合理地期望将来不需要以这种方式重写程序，则numpy.random.seed()应该没问题.如果有任何理由怀疑您将来可能需要线程，那么从长远来看，按照建议进行操作和创建numpy.random.Random类的本地实例.据我所知，random.random.seed()是线程安全的(或者至少我没有发现任何相反的证据).

numpy.random库包含一些科学研究中常用的额外概率分布，以及用于生成随机数据数组的几个便捷函数. random.random库要轻巧一些，如果您不从事科学研究或其他统计工作，那应该很好.

否则，它们都使用 Merenne扭曲序列生成其随机数，并且它们两者都是完全确定性的-也就是说，如果您知道一些关键信息，则可以绝对确定地进行预测下一个数字是.因此，numpy.random和random.random都不适合任何严重的加密用途.但是因为序列非常长，所以在您不担心有人试图对数据进行反向工程的情况下，两者都适合生成随机数.这也是必须播下随机值的原因-如果每次都从同一位置开始，那么您将始终获得相同的随机数序列！

请注意，如果您需要需要加密级别的随机性，则应使用秘密模块，或类似 Crypto.Random (如果您使用的Python版本早于Python 3.6).

I have a big script in Python. I inspired myself in other people's code so I ended up using the numpy.random module for some things (for example for creating an array of random numbers taken from a binomial distribution) and in other places I use the module random.random.

Can someone please tell me the major differences between the two? Looking at the doc webpage for each of the two it seems to me that numpy.random just has more methods, but I am unclear about how the generation of the random numbers is different.

The reason why I am asking is because I need to seed my main program for debugging purposes. But it doesn't work unless I use the same random number generator in all the modules that I am importing, is this correct?

Also, I read here, in another post, a discussion about NOT using numpy.random.seed(), but I didn't really understand why this was such a bad idea. I would really appreciate if someone explain me why this is the case.

解决方案

You have made many correct observations already!

Unless you'd like to seed both of the random generators, it's probably simpler in the long run to choose one generator or the other. But if you do need to use both, then yes, you'll also need to seed them both, because they generate random numbers independently of each other.

For numpy.random.seed(), the main difficulty is that it is not thread-safe - that is, it's not safe to use if you have many different threads of execution, because it's not guaranteed to work if two different threads are executing the function at the same time. If you're not using threads, and if you can reasonably expect that you won't need to rewrite your program this way in the future, numpy.random.seed() should be fine. If there's any reason to suspect that you may need threads in the future, it's much safer in the long run to do as suggested, and to make a local instance of the numpy.random.Random class. As far as I can tell, random.random.seed() is thread-safe (or at least, I haven't found any evidence to the contrary).

The numpy.random library contains a few extra probability distributions commonly used in scientific research, as well as a couple of convenience functions for generating arrays of random data. The random.random library is a little more lightweight, and should be fine if you're not doing scientific research or other kinds of work in statistics.

Otherwise, they both use the Mersenne twister sequence to generate their random numbers, and they're both completely deterministic - that is, if you know a few key bits of information, it's possible to predict with absolute certainty what number will come next. For this reason, neither numpy.random nor random.random is suitable for any serious cryptographic uses. But because the sequence is so very very long, both are fine for generating random numbers in cases where you aren't worried about people trying to reverse-engineer your data. This is also the reason for the necessity to seed the random value - if you start in the same place each time, you'll always get the same sequence of random numbers!

As a side note, if you do need cryptographic level randomness, you should use the secrets module, or something like Crypto.Random if you're using a Python version earlier than Python 3.6.