Java random随机数/ seed 种子 / System.nanoTime() 的理解与使用

我会带着你远行 2021-09-25 02:42 274阅读 0赞

**伪随机**（preundorandom）：**通过算法产生的随机数都是伪随机！！**

**只有通过真实的随机事件产生的随机数才是真随机！！****比如，通过机器的[硬件噪声产生随机数][Link 1]、通过[大气噪声产生随机数][Link 2]**

**Random生成的随机数都是伪随机数！！！**

是由可确定的函数（常用线性同余），通过一个种子（常用时钟），产生的伪随机数。这意味着：如果知道了种子，或者已经产生的随机数，都可能获得接下来随机数序列的信息（可预测性）

Random类拥有两个构造方法，用于实现随机数生成器：

<table> 
 <tbody> 
  <tr> 
   <td>Random( )</td> 
   <td>构造一个随机数生成器，种子是&nbsp; 与nanoTime异或后的值。每遍输出的多个序列均不同。随机性更强。</td> 
  </tr> 
  <tr> 
   <td>Random(long seed)</td> 
   <td>用种子seed构造一个随机数生成器，种子是给定的。每遍输出的多个序列均相同。</td> 
  </tr> 
 </tbody> 
</table>

/**
         * Creates a new random number generator. This constructor sets
         * the seed of the random number generator to a value very likely
         * to be distinct from any other invocation of this constructor.
         */
        public Random() {
            this(seedUniquifier() ^ System.nanoTime());//与System.nanoTime()异或
            //这里System.nanoTime();并不是以纳秒为单位的系统时间，只用来计算花费多少时间用的。与时间的概念无关。
        }
    
        private static long seedUniquifier() {
            // L'Ecuyer, "Tables of Linear Congruential Generators of
            // Different Sizes and Good Lattice Structure", 1999
            for (;;) {
                long current = seedUniquifier.get();
                long next = current * 181783497276652981L;
                if (seedUniquifier.compareAndSet(current, next))
                    return next;
            }
        }
    
        private static final AtomicLong seedUniquifier
            = new AtomicLong(8682522807148012L); //种子分配器
    
        /**
         * Creates a new random number generator using a single {@code long} seed.
         * The seed is the initial value of the internal state of the pseudorandom
         * number generator which is maintained by method {@link #next}.
         *
         * <p>The invocation {@code new Random(seed)} is equivalent to:
         *  <pre> {@code
         * Random rnd = new Random();
         * rnd.setSeed(seed);}</pre>
         *
         * @param seed the initial seed
         * @see   #setSeed(long)
         */
        public Random(long seed) {//无参构造，有参构造，均是调用这个方法
            if (getClass() == Random.class)// Random random1 = new Random();或者  Random random2 = new Random(100); 均是调用这个if
                this.seed = new AtomicLong(initialScramble(seed));//处理后，seed值没变
            else {
                // subclass might have overriden setSeed
                this.seed = new AtomicLong();
                setSeed(seed);
            }
        }
    
        private static long initialScramble(long seed) {
            return (seed ^ multiplier) & mask;
        }

### **一、无参构造方法（不设置种子）** ###

虽然表面上看我们未设置种子，但Random构造方法里有一套自己的种子生成机制，源码如上。

**生成种子过程：**（参考[解密随机数生成器（二）——从java源码看线性同余算法][java]）

1、获得一个长整形数作为“初始种子”（系统默认的是8682522807148012L）

2、不断与一个变态的数——181783497276652981L相乘（天知道这些数是不是工程师随便滚键盘滚出来的-.-）得到一个不能预测的值，直到 能把这个不能事先预期的值 赋给Random对象的静态常量seedUniquifier 。因为多线程环境下赋值操作可能失败，就for(;;)来保证一定要赋值成功

3、与系统随机出来的nanotime值作异或运算，得到最终的种子

nanotime算是一个随机性比较强的参数，用于描述代码的执行时间。源码中关于nanotime的描述（部分）：

**并不是以纳秒为单位的系统时间，只用来计算花费多少时间用的。与时间的概念无关。**

/**
         * Returns the current value of the running Java Virtual Machine's
         * high-resolution time source, in nanoseconds.
         *
         * <p>This method can only be used to measure elapsed time and is
         * not related to any other notion of system or wall-clock time.
         * The value returned represents nanoseconds since some fixed but
         * arbitrary <i>origin</i> time (perhaps in the future, so values
         * may be negative).  The same origin is used by all invocations of
         * this method in an instance of a Java virtual machine; other
         * virtual machine instances are likely to use a different origin.
         *
         * <p>This method provides nanosecond precision, but not necessarily
         * nanosecond resolution (that is, how frequently the value changes)
         * - no guarantees are made except that the resolution is at least as
         * good as that of {@link #currentTimeMillis()}.
         *
         * <p>Differences in successive calls that span greater than
         * approximately 292 years (2<sup>63</sup> nanoseconds) will not
         * correctly compute elapsed time due to numerical overflow.
         *
         * <p>The values returned by this method become meaningful only when
         * the difference between two such values, obtained within the same
         * instance of a Java virtual machine, is computed.
         *
         * <p> For example, to measure how long some code takes to execute:
         *  <pre> {@code
         * long startTime = System.nanoTime();
         * // ... the code being measured ...
         * long estimatedTime = System.nanoTime() - startTime;}</pre>
         *
         * <p>To compare two nanoTime values
         *  <pre> {@code
         * long t0 = System.nanoTime();
         * ...
         * long t1 = System.nanoTime();}</pre>
         *
         * one should use {@code t1 - t0 < 0}, not {@code t1 < t0},
         * because of the possibility of numerical overflow.
         *
         * @return the current value of the running Java Virtual Machine's
         *         high-resolution time source, in nanoseconds
         * @since 1.5
         */
        public static native long nanoTime();

### **二、有参构造方法（设置种子）** ###

语法：Random ran = Random(long seed)

有参构造方法的源码如上。

其中的multiplier和mask都是定值：

1  private static final long multiplier = 0x5DEECE66DL;
    2 
    3  private static final long mask = (1L << 48) - 1;

### **三、代码测试** ###

分别采用有参和无参两种方法，生成\[0, 100)内的随机整数，各生成五组，每组十个随机数：

1 import java.util.Random;
     2 
     3 public class RandomTest {
     4     public static void main(String[] args) {
     5         RandomTest rt = new RandomTest();
     6         rt.testRandom();
     7     }
     8 
     9     public void testRandom(){
    10         System.out.println("Random不设置种子：");
    11         for (int i = 0; i < 5; i++) {
    12             Random random = new Random();
    13             for (int j = 0; j < 10; j++) {
    14                 System.out.print(" " + random.nextInt(100) + ", ");
    15             }
    16             System.out.println("");
    17         }
    18 
    19         System.out.println("");
    20 
    21         System.out.println("Random设置种子：");
    22         for (int i = 0; i < 5; i++) {
    23             Random random = new Random();
    24             random.setSeed(100);
    25             for (int j = 0; j < 10; j++) {
    26                 System.out.print(" " + random.nextInt(100) + ", ");
    27             }
    28             System.out.println("");
    29         }
    30     }
    31 }

![复制代码][48304ba5e6f9fe08f3fa1abda7d326ab.png]

运行结果如下：

![5c215d8b426b1ab385c240607b539f5b.png][]

**结论：**

**虽然二者都是伪随机，但是，****无参数构造方法（不设置种子）具有更强的随机性，能够满足一般统计上的随机数要求。使用有参的构造方法（设置种子）无论你生成多少次，每次生成的随机序列都相同，名副其实的伪随机！！**

[https://www.cnblogs.com/huiAlex/p/8057737.html][https_www.cnblogs.com_huiAlex_p_8057737.html]

### 四、为何要使用种子？ ###

种子就是生成随机数的根，就是产生随机数的基础。计算机的随机数都是[伪随机数][Link 3]，**以一个真随机数（种子）作为初始条件**，然后用一定的算法不停迭代产生随机数。Java项目中通常是通过Math.random方法和Random类来获得随机数。Random类中不含参构造方法每次都使用当前时间作为种子，而含参构造方法是以一个固定值作为种子。

**随机数是种子经过计算生成的**。

*  不含参的构造函数每次都使用当前时间作为种子，随机性更强
 *  随机数的生成是从种子值开始。 如果反复使用同一个种子，就会生成相同的数字系列，产生不同序列的一种方法是使种子值与时间相关。

[https://blog.csdn.net/chenxiangqungo/article/details/109070602][https_blog.csdn.net_chenxiangqungo_article_details_109070602]

### 五、System.nanoTime() ###

System.out.println(System.nanoTime());//并不是以纳秒为单位的系统时间
            System.out.println(System.nanoTime());
            System.out.println(System.nanoTime());
            System.out.println(System.currentTimeMillis());
    //        第一遍运行：
    //        281092180853691
    //        281092180911666
    //        281092180936920
    //        1610767387212  =>  2021-01-16 11:23:07
    
    
    //        第二遍运行：
    //        281207286912695
    //        281207286952070
    //        281207286970745
    //        1610767502318

以上运行、源码基于jdk1.8

[Link 1]: https://wenku.baidu.com/view/a4a7e45be87101f69f31953b.html
[Link 2]: https://www.random.org/
[java]: https://www.cnblogs.com/softidea/p/5824240.html
[48304ba5e6f9fe08f3fa1abda7d326ab.png]: /images/20210923/acd0f4fb9f9a4da9b217c182c3a8c3d2.png
[5c215d8b426b1ab385c240607b539f5b.png]: /images/20210923/c4b2e98677bc4e108470058564db590f.png
[https_www.cnblogs.com_huiAlex_p_8057737.html]: https://www.cnblogs.com/huiAlex/p/8057737.html
[Link 3]: http://baike.baidu.com/view/1127.htm
[https_blog.csdn.net_chenxiangqungo_article_details_109070602]: https://blog.csdn.net/chenxiangqungo/article/details/109070602