問題描述
盡管通過 srand() 使用種子，但通常不贊成使用 rand().為什么會是這樣?有哪些更好的替代方案?
Usage of rand() is usually frowned upon despite using a seed via srand(). Why would that be the case? What better alternatives are available?
推薦答案
這個故事有兩個部分.
首先，rand 是一個偽隨機數(shù)生成器.這意味著它取決于種子.對于給定的種子，它總是給出相同的序列(假設(shè)實現(xiàn)相同).這使得它不適合某些安全性非常重要的應(yīng)用程序.但是這并不特定于rand.這是任何偽隨機生成器的問題.并且肯定有很多類別的問題可以接受偽隨機生成器.真隨機生成器有其自身的問題(效率、實現(xiàn)、熵)，因此對于與安全無關(guān)的問題，通常使用偽隨機生成器.
First, rand is a pseudorandom number generator. This means it depends on a seed. For a given seed it will always give the same sequence (assuming the same implementation). This makes it not suitable for certain applications where security is of a great concern. But this is not specific to rand. It's an issue with any pseudo-random generator. And there are most certainly a lot of classes of problems where a pseudo-random generator is acceptable. A true random generator has its own issues (efficiency, implementation, entropy) so for problems that are not security related most often a pseudo-random generator is used.
所以您分析了您的問題并得出結(jié)論，偽隨機生成器是解決方案.在這里，我們遇到了 C 隨機庫(包括 rand 和 srand)的真正麻煩，這些庫是特定于它的，并使其過時(又名:您應(yīng)該從不使用 rand 和 C 隨機庫的原因).
So you analyzed your problem and you conclude a pseudo-random generator is the solution. And here we arrive to the real troubles with the C random library (which includes rand and srand) that are specific to it and make it obsolete (a.k.a.: the reasons you should never use rand and the C random library).
一個問題是它有全局狀態(tài)(由
srand設(shè)置).這使得無法同時使用多個隨機引擎.它還使多線程任務(wù)變得非常復(fù)雜.
One issue is that it has a global state (set by
srand). This makes it impossible to use multiple random engines at the same time. It also greatly complicates multithreaded tasks.
其中最明顯的問題是缺少分發(fā)引擎:rand 給你一個區(qū)間[0 RAND_MAX].在這個區(qū)間是一致的，也就是說這個區(qū)間的每個數(shù)字出現(xiàn)的概率都是一樣的.但大多數(shù)情況下，您需要一個特定時間間隔內(nèi)的隨機數(shù).假設(shè) [0, 1017].一個常用的(也很簡單的)公式是 rand() % 1018.但問題在于，除非 RAND_MAX 是 1018 的精確倍數(shù)，否則您不會得到均勻分布.
The most visible problem of it is that it lacks a distribution engine: rand gives you a number in interval [0 RAND_MAX]. It is uniform in this interval, which means that each number in this interval has the same probability to appear. But most often you need a random number in a specific interval. Let's say [0, 1017]. A commonly (and naive) used formula is rand() % 1018. But the issue with this is that unless RAND_MAX is an exact multiple of 1018 you won't get an uniform distribution.
另一個問題是rand 的實現(xiàn)質(zhì)量.這里還有其他答案比我更詳細地說明了這一點，所以請閱讀它們.
Another issue is the Quality of Implementation of rand. There are other answers here detailing this better than I could, so please read them.
在現(xiàn)代 C++ 中，您絕對應(yīng)該使用 <random> 中的 C++ 庫，它帶有多個定義良好的隨機引擎以及整數(shù)和浮點類型的各種分布.
In modern C++ you should definitely use the C++ library from <random> which comes with multiple random well-defined engines and various distributions for integer and floating point types.
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