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、通过大气噪声产生随机数**
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：
Random( )构造一个随机数生成器 、Random(long seed)用种子seed构造一个随机数生成器
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，源码如下：
java
/**
* 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());
}
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);/**
* 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());
}
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);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）
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）：
/**
* 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./**
* 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.二、有参构造方法（设置种子)*
java
/**
* 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) {
if (getClass() == Random.class)
this.seed = new AtomicLong(initialScramble(seed));
else {
// subclass might have overriden setSeed
this.seed = new AtomicLong();
setSeed(seed);
}
}
private static long initialScramble(long seed) {
return (seed ^ multiplier) & mask;
}/**
* 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) {
if (getClass() == Random.class)
this.seed = new AtomicLong(initialScramble(seed));
else {
// subclass might have overriden setSeed
this.seed = new AtomicLong();
setSeed(seed);
}
}
private static long initialScramble(long seed) {
return (seed ^ multiplier) & mask;
}其中的multiplier和mask都是定值：
java
private static final long multiplier = 0x5DEECE66DL;
private static final long mask = (1L << 48) - 1;private static final long multiplier = 0x5DEECE66DL;
private static final long mask = (1L << 48) - 1;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！！