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leveldb源码学习--Random简单随机数生成

leveldb源码学习--Random简单随机数生成

作者: icecity96 | 来源:发表于2017-02-03 20:59 被阅读0次

    leveldbrandom.h中实现一个非常简单的随机数生成器,说成简陋也可以。

    源码分析

    构造函数

    explicit Random(uint32_t s) : seed_(s & 0x7fffffffu) {
        // Avoid bad seeds.
        if (seed_ == 0 || seed_ == 2147483647L) {
          seed_ = 1;
        }
      }
    

    构造函数用来初始化随机种子数,注意到作者认为当seed_0或者 M(2^31-1)时是一种不好的种子数。这是因为在后面的随机数生成方法中采用的是seed_ = (seed_ * A) % M,如果seed_0或者M,那么以后产生的所有seed_都是0了(具体表现在Next()函数中)。

    Next()

    uint32_t Next() {
        static const uint32_t M = 2147483647L;   // 2^31-1
        static const uint64_t A = 16807;  // bits 14, 8, 7, 5, 2, 1, 0
        // We are computing
        //       seed_ = (seed_ * A) % M,    where M = 2^31-1
        //
        // seed_ must not be zero or M, or else all subsequent computed values
        // will be zero or M respectively.  For all other values, seed_ will end
        // up cycling through every number in [1,M-1]
        uint64_t product = seed_ * A;
    
        // Compute (product % M) using the fact that ((x << 31) % M) == x.
        seed_ = static_cast<uint32_t>((product >> 31) + (product & M));
        // The first reduction may overflow by 1 bit, so we may need to
        // repeat.  mod == M is not possible; using > allows the faster
        // sign-bit-based test.
        if (seed_ > M) {
          seed_ -= M;
        }
        return seed_;
      }
    

    Next()函数中,开始我没看明白的一点是seed_ 如何由product得来的,其实注意到product是一个uint64_t,而seed_Muint32_t。如同注释中提示到的Compute (product % M) using the fact that ((x << 31) % M) == x.,将(product % M)分成高32位的结果和低32位的结果,然后相加即可。(不知道为何能分开求的可以去面壁了)

    Uniform() && OneIn()

      // Returns a uniformly distributed value in the range [0..n-1]
      // REQUIRES: n > 0
      uint32_t Uniform(int n) { return Next() % n; }
    
      // Randomly returns true ~"1/n" of the time, and false otherwise.
      // REQUIRES: n > 0
      bool OneIn(int n) { return (Next() % n) == 0; }
    

    非常简单的两个函数,注释也写的很明白,要是不明白。那也没办法了

    Skewed()

    // Skewed: pick "base" uniformly from range [0,max_log] and then
    // return "base" random bits.  The effect is to pick a number in the
    // range [0,2^max_log-1] with exponential bias towards smaller numbers.
    uint32_t Skewed(int max_log) {
        return Uniform(1 << Uniform(max_log + 1));
      }
    

    这个函数随机返回[0, 2^(max_log-1)]区间中的一个2的指数值。

    ice_city liuhiter@gmail.com

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