C++ 中的确定性随机数流 STL
我想提供一个数字，然后收到一组随机数。 但是，我希望这些数字是相同的，无论我在哪台计算机上运行它（假设我提供相同的种子）。
基本上我的问题是：在 C++ 中，如果我使用 rand()，但为 srand() 提供用户定义的种子而不是当前时间，我会吗能够在任何计算机上生成相同的随机数流？
I want to supply a number, and then receive a set of random numbers. However, I want those numbers to be the same regardless of which computer I run it on (assuming I supply the same seed).
Basically my question is: in C++, if I make use of rand(), but supply srand() with a user-defined seed rather than the current time, will I be able to generate the same random number stream on any computer?
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有数十个 PRNG 可用作库。 选一个。 我倾向于使用 Mersenne Twister。
通过使用外部提供的库，您可以避免语言库
rand()实现奇怪或有错误的风险。 只要您的平台都符合相同的数学语义，您就会得到一致的结果。MT 是我的最爱，因为我是一名物理学家，我将这些东西用于蒙特卡洛，其中保证高维度的均匀分布非常重要。 但是不要将 MT 用作加密 PRNG！
There are dozens of PRNGs available as libraries. Pick one. I tend to use Mersenne Twister.
By using an externally supplied library, you bypass the risk of a weird or buggy implementation of your language's library
rand(). As long as your platforms all conform to the same mathematical semantics, you'll get consistent results.MT is a favorite of mine because I'm a physicist, and I use these things for Monte Carlo, where the guarantee of equal-distribution to high dimensions is important. But don't use MT as a cryptographic PRNG!
srand()&rand()不是 STL 的一部分。 它们实际上是 C 运行时的一部分。是的，只要是
srand()/rand()的相同实现，它们就会产生相同的结果。根据您的需求，您可能需要考虑使用 Boost.Random 。 它提供了几种高质量的随机数生成器。
srand()&rand()are not part of the STL. They're actually part of the C runtime.Yes, they will produce the same results as long as it's the same implementation of
srand()/rand().Depending on your needs, you might want to consider using Boost.Random. It provides several high-quality random number generators.
假设 rand() 的实现是相同的，是的。
确保这一点的最简单方法是在您的程序中包含已知的 rand() 实现 - 要么包含在项目的源代码中，要么以您可以管理的库的形式包含。
Assuming the implementations of
rand()are the same, yes.The easiest way to ensure this is to include a known rand() implementation with your program - either included in your project's source code or in the form of a library you can manage.
编写您自己的伪随机数例程。 互联网上有很多算法记录，并且它们有许多应用程序，但 rand 不够好（例如 柏林噪音）。
对于初学者，请尝试以下链接：
http://en.wikipedia.org/wiki/Linear_congruential_generator
< a href="http://en.wikipedia.org/wiki/Pseudorandom_number_generator" rel="nofollow noreferrer">http://en.wikipedia.org/wiki/Pseudorandom_number_generator
Write your own pseudorandom number routine. There are a lot of algorithms documented on the internet, and they have a number of applications where rand isn't good enough (e.g. Perlin Noise).
Try these links for starters:
http://en.wikipedia.org/wiki/Linear_congruential_generator
http://en.wikipedia.org/wiki/Pseudorandom_number_generator
不，ANSI C 标准仅指定
rand()必须生成 0 到 RAND_MAX 之间的随机整数流，该整数必须至少为 32767 (来源）。 该流必须是确定性的，只是对于给定机器上的给定实现，它必须在给定相同种子的情况下生成相同的整数流。您想要一个便携式 PRNG。 Mersenne Twister（许多实现链接在底部）非常可移植，Ben Pfaff 自行开发的符合 C99 的 PRNG。 Boost.Random 也应该没问题； 当您用 C++ 编写代码时，使用 Boost 不会对您的平台选择产生太大限制（尽管一些“较小”（即不兼容）编译器可能会因大量使用模板元编程而遇到问题）。 对于小批量嵌入式平台和可能新颖的研究架构来说，这只是一个真正的问题，因此，如果“任何计算机”是指“GCC 目标的任何 x86/PPC/ARM/SPARC/Alpha/等平台”，则任何上面应该就可以了。
No, the ANSI C standard only specifies that
rand()must produce a stream of random integers between 0 and RAND_MAX, which must be at least 32767 (source). This stream must be deterministic only in that, for a given implementation on a given machine, it must produce the same integer stream given the same seed.You want a portable PRNG. Mersenne Twister (many implementations linked at the bottom) is pretty portable, as is Ben Pfaff's homegrown C99-compliant PRNG. Boost.Random should be fine too; as you're writing your code in C++, using Boost doesn't limit your choice of platforms much (although some "lesser" (i.e. non-compliant) compilers may have trouble with its heavy use of template metaprogramming). This is only really a problem for low-volume embedded platforms and perhaps novel research architectures, so if by "any computer" you mean "any x86/PPC/ARM/SPARC/Alpha/etc. platform that GCC targets", any of the above should do just fine.
我相信，如果您提供具有相同种子的 srand，您会得到相同的结果。 这几乎就是伪随机数生成器中种子的定义。
I believe if you supply with srand with the same seed, you will get the same results. That's pretty much the definition of a seed in terms of pseudo random number generators.
是的。 对于给定的种子（起始值），rand() 返回的数字序列将始终相同。
Yes. For a given seed (starting value), the sequence of numbers that rand() returns will always be the same.