使用和实用的std :: shuffle？ [英] Use and utility of std::shuffle?

问题描述

如果你看看随机shuffle在C ++ 11中的规格，有3个函数。我的问题是什么是典型的用途和优点：

  template<类RandomIt，类URNG> 
 void shuffle（RandomIt first，RandomIt last，URNG&& g）; 
  

：

  template< class RandomIt> 
 void random_shuffle（RandomIt first，RandomIt last）; 
  

我的意思是，无论URNG是什么（均匀分布），结果都是一样的（从统计角度看）。我看到的唯一一点是， std :: shuffle 是安全的，而这个重载 std :: random_shuffle 不是。你能确认吗？

编辑：我认为URNG应该是一个统一的分布，但似乎不编译。因此，有人可以提供一个使用 std :: shuffle ？

解决方案

如评论中所述， std :: shuffle 在标准中采用了随机数字生成器（或引擎说话），而不是随机数分布。

• 随机或伪随机 - 真随机数生成器使用某种外部熵源。伪随机生成器（PRNG）是严格确定的。


• 性能 - 某些发电机比其他发电机更快。


• strong>内存使用 - 某些PRNG需要更多内存来存储其状态。


• 期间长度 - 所有PRNG都有一个有限期它们从开始重复相同的序列。


• 随机性质量 - 有众多测试用于测量是否有微妙的（或不那么微妙的！）模式在pseurorandom流。例如，请参阅 Diehard测试。


• 是否该流是 加密安全 。 AFAIK，标准PRNG都不是。

有关该标准提供的不同发电机的概述，请参阅 http://en.cppreference.com/w/cpp/numeric/random 。 p>

If you look to the specifications of random shuffle in C++11, there are 3 functions. My question is what is the typical use and advantage of :

template< class RandomIt, class URNG >
void shuffle( RandomIt first, RandomIt last, URNG&& g );

compared to:

template< class RandomIt >
void random_shuffle( RandomIt first, RandomIt last );

I mean, it seems that whatever URNG is (a uniform distribution), the result will be the same (from a statistical point of view). The only point I see, is that std::shuffle is thead-safe, whereas this overload ofstd::random_shuffle is not. Could you confirm that ?

EDIT: I thought that URNG should be a uniform distribution but that does not seem to compile. So can someone provide a little example of use of std::shuffle?

解决方案

As mentioned in the comments, std::shuffle takes a random number generator (or engine in standard speak), not a random number distribution. Different random number generators have different characteristics even if they have a theoretically uniform distribution.

• Random or pseudo-random - True random number generators use some sort of an external entropy source. Pseudo-random generators (PRNGs) are strictly deterministic.
• Performance - some generators are faster than others.
• Memory usage - some PRNGs need more memory to store their state than others.
• Period length - all PRNGs have a finite period after which they start repeating the same sequence from the beginning. Some have much longer periods than others.
• Randomness quality - there are numerous tests for measuring whether there are subtle (or not-so-subtle!) patterns in a pseurorandom stream. See, for example, the Diehard tests.
• Whether the stream is cryptographically secure or not. AFAIK, none of the standard PRNGs are.

For an overview of the different generators offered by the standard, please refer to http://en.cppreference.com/w/cpp/numeric/random.

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