objective-c arc4random 和 arc4random_uniform 有什么区别？
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What's the difference between arc4random and arc4random_uniform?
提问by Brennan Adler
I've seen old posts about the differences between randomand arc4randomin Objective-C, and I've seen answers to this online but I didn't really understand, so I was hoping someone here could explain it in an easier-to-understand manner.
我看过关于Objective-Crandom和arc4randomObjective-C之间差异的旧帖子，我在网上看到了这个答案，但我不太明白，所以我希望这里有人能以更容易理解的方式解释它.
What is the difference between using arc4randomand arc4random_uniformto generate random numbers?
使用arc4random和arc4random_uniform生成随机数有什么区别？
回答by Connor
arc4randomreturns an integer between 0 and (2^32)-1 while arc4random_uniformreturns an integer between 0 and the upper bound you pass it.
arc4random返回 0 到 (2^32)-1arc4random_uniform之间的整数，同时返回 0 和传递给它的上限之间的整数。
From man 3 arc4random:
来自man 3 arc4random：
arc4random_uniform() will return a uniformly distributed random number less than upper_bound. arc4random_uniform() is recommended over constructions like ``arc4random() % upper_bound'' as it avoids "modulo bias" when the upper bound is not a power of two.
arc4random_uniform() 将返回一个小于 upper_bound 的均匀分布的随机数。arc4random_uniform() 被推荐用于类似 ``arc4random() % upper_bound'' 的结构，因为当上限不是 2 的幂时，它可以避免“模偏差”。
For example if you want an integer between 0 and 4 you could use
例如，如果你想要一个 0 到 4 之间的整数，你可以使用
arc4random() % 5
or
或者
arc4random_uniform(5)
Using the modulus operator in this case introduces modulo bias, so it's better to use arc4random_uniform.
在这种情况下使用模运算符会引入模偏差，因此最好使用 arc4random_uniform。
To understand modulo bias assume that arc4randomhad a much smaller range. Instead of 0 to (2^32) -1, it was 0 to (2^4) -1. If you perform % 5 on each number in that range you will get 0 four times, and 1, 2, 3 and 4 three times each making 0 more likely to occur. This difference becomes less significant when the range is much larger, but it's still better to avoid using modulus.
要理解模偏差，假设 的arc4random范围要小得多。不是 0 到 (2^32) -1，而是 0 到 (2^4) -1。如果您对该范围内的每个数字执行 % 5，您将获得四次 0，而 1、2、3 和 4 则各获得 3 次，从而使 0 更有可能出现。当范围大得多时，这种差异变得不那么显着，但最好避免使用模数。
