public class Random extends Object implements Serializable
 If two instances of Random are created with the same seed, and the same sequence of
 method calls is made for each, they will generate and return identical sequences of numbers. In
 order to guarantee this property, particular algorithms are specified for the class
 Random. Java implementations must use all the algorithms shown here for the class
 Random, for the sake of absolute portability of Java code. However, subclasses of class
 Random are permitted to use other algorithms, so long as they adhere to the general
 contracts for all the methods.
 
 The algorithms implemented by class Random use a protected utility method that on
 each invocation can supply up to 32 pseudorandomly generated bits.
 
 Many applications will find the method Math.random() simpler to use.
| Constructor and Description | 
|---|
| Random()Creates a new random number generator. | 
| Random(long seed)Creates a new random number generator using a single  longseed. | 
| Modifier and Type | Method and Description | 
|---|---|
| protected int | next(int bits)Generates the next pseudorandom number. | 
| boolean | nextBoolean()Returns the next pseudorandom, uniformly distributed  booleanvalue from this random
 number generator's sequence. | 
| void | nextBytes(byte[] bytes)Generates random bytes and places them into a user-supplied byte array. | 
| double | nextDouble()Returns the next pseudorandom, uniformly distributed  doublevalue between0.0and1.0from this random number generator's sequence. | 
| float | nextFloat()Returns the next pseudorandom, uniformly distributed  floatvalue between0.0and1.0from this random number generator's sequence. | 
| double | nextGaussian()Returns the next pseudorandom, Gaussian ("normally") distributed  doublevalue with mean0.0and standard deviation1.0from this random number generator's sequence. | 
| int | nextInt()Returns the next pseudorandom, uniformly distributed  intvalue from this random number
 generator's sequence. | 
| int | nextInt(int n)Returns a pseudorandom, uniformly distributed  intvalue between 0 (inclusive) and the
 specified value (exclusive), drawn from this random number generator's sequence. | 
| long | nextLong()Returns the next pseudorandom, uniformly distributed  longvalue from this random number
 generator's sequence. | 
| void | setSeed(long seed)Sets the seed of this random number generator using a single  longseed. | 
public Random()
public Random(long seed)
long seed. The seed is the initial
 value of the internal state of the pseudorandom number generator which is maintained by method
 next(int).
 
 The invocation new Random(seed) is equivalent to:
 
 {
        @code
        Random rnd = new Random();
        rnd.setSeed(seed);
 }
 seed - the initial seedsetSeed(long)protected int next(int bits)
 The general contract of next is that it returns an int value and if the argument
 bits is between 1 and 32 (inclusive), then that many low-order bits of
 the returned value will be (approximately) independently chosen bit values, each of which is
 (approximately) equally likely to be 0 or 1. The method next is
 implemented by class Random by atomically updating the seed to
 
  (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)
 
 and returning
 
  (int)(seed >>> (48 - bits)).
 
 This is a linear congruential pseudorandom number generator, as defined by D. H. Lehmer and
 described by Donald E. Knuth in The Art of Computer Programming, Volume 3:
 Seminumerical Algorithms, section 3.2.1.bits - random bitspublic boolean nextBoolean()
boolean value from this random
 number generator's sequence. The general contract of nextBoolean is that one
 boolean value is pseudorandomly generated and returned. The values true and
 false are produced with (approximately) equal probability.
 
 The method nextBoolean is implemented by class Random as if by:
 
 
 public boolean nextBoolean() {
   return next(1) != 0;
 }
 boolean value from this random
         number generator's sequencepublic void nextBytes(byte[] bytes)
 The method nextBytes is implemented by class Random as if by:
 
 
 public void nextBytes(byte[] bytes) {
   for (int i = 0; i < bytes.length; )
     for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4);
          n-- > 0; rnd >>= 8)
       bytes[i++] = (byte)rnd;
 }
 bytes - the byte array to fill with random bytesNullPointerException - if the byte array is nullpublic double nextDouble()
double value between 0.0 and
 1.0 from this random number generator's sequence.
 
 The general contract of nextDouble is that one double value, chosen
 (approximately) uniformly from the range 0.0d (inclusive) to 1.0d (exclusive), is
 pseudorandomly generated and returned.
 
 The method nextDouble is implemented by class Random as if by:
 
 
 public double nextDouble() {
   return (((long)next(26) << 27) + next(27))
     / (double)(1L << 53);
 }
 
 
 The hedge "approximately" is used in the foregoing description only because the next
 method is only approximately an unbiased source of independently chosen bits. If it were a
 perfect source of randomly chosen bits, then the algorithm shown would choose double
 values from the stated range with perfect uniformity.
 
[In early versions of Java, the result was incorrectly calculated as:
 
   return (((long)next(27) << 27) + next(27))
     / (double)(1L << 54);
 
 This might seem to be equivalent, if not better, but in fact it introduced a large nonuniformity
 because of the bias in the rounding of floating-point numbers: it was three times as likely that
 the low-order bit of the significand would be 0 than that it would be 1! This nonuniformity
 probably doesn't matter much in practice, but we strive for perfection.]double value between 0.0 and
         1.0 from this random number generator's sequencepublic float nextFloat()
float value between 0.0 and
 1.0 from this random number generator's sequence.
 
 The general contract of nextFloat is that one float value, chosen (approximately)
 uniformly from the range 0.0f (inclusive) to 1.0f (exclusive), is pseudorandomly
 generated and returned. All 224 possible float values
 of the form m x 2-24, where m is a
 positive integer less than 224, are produced with
 (approximately) equal probability.
 
 The method nextFloat is implemented by class Random as if by:
 
 
 public float nextFloat() {
   return next(24) / ((float)(1 << 24));
 }
 
 
 The hedge "approximately" is used in the foregoing description only because the next method is
 only approximately an unbiased source of independently chosen bits. If it were a perfect source
 of randomly chosen bits, then the algorithm shown would choose float values from the
 stated range with perfect uniformity.
 
[In early versions of Java, the result was incorrectly calculated as:
 
   return next(30) / ((float)(1 << 30));
 
 This might seem to be equivalent, if not better, but in fact it introduced a slight nonuniformity
 because of the bias in the rounding of floating-point numbers: it was slightly more likely that
 the low-order bit of the significand would be 0 than that it would be 1.]float value between 0.0 and
         1.0 from this random number generator's sequencepublic double nextGaussian()
double value with mean
 0.0 and standard deviation 1.0 from this random number generator's sequence.
 
 The general contract of nextGaussian is that one double value, chosen from
 (approximately) the usual normal distribution with mean 0.0 and standard deviation
 1.0, is pseudorandomly generated and returned.
 
 The method nextGaussian is implemented by class Random as if by a threadsafe
 version of the following:
 
 
 private double nextNextGaussian;
 private boolean haveNextNextGaussian = false;
 public double nextGaussian() {
   if (haveNextNextGaussian) {
     haveNextNextGaussian = false;
     return nextNextGaussian;
   } else {
     double v1, v2, s;
     do {
       v1 = 2 * nextDouble() - 1;   // between -1.0 and 1.0
       v2 = 2 * nextDouble() - 1;   // between -1.0 and 1.0
       s = v1 * v1 + v2 * v2;
     } while (s >= 1 || s == 0);
     double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
     nextNextGaussian = v2 * multiplier;
     haveNextNextGaussian = true;
     return v1 * multiplier;
   }
 }
 
 This uses the polar method of G. E. P. Box, M. E. Muller, and G. Marsaglia, as described
 by Donald E. Knuth in The Art of Computer Programming, Volume 3: Seminumerical
 Algorithms, section 3.4.1, subsection C, algorithm P. Note that it generates two independent
 values at the cost of only one call to StrictMath.log and one call to
 StrictMath.sqrt.double value with mean
         0.0 and standard deviation 1.0 from this random number generator's
         sequencepublic int nextInt()
int value from this random number
 generator's sequence. The general contract of nextInt is that one int value is
 pseudorandomly generated and returned. All 232  possible
 int values are produced with (approximately) equal probability.
 
 The method nextInt is implemented by class Random as if by:
 
 
 public int nextInt() {
   return next(32);
 }
 int value from this random number
         generator's sequencepublic int nextInt(int n)
int value between 0 (inclusive) and the
 specified value (exclusive), drawn from this random number generator's sequence. The general
 contract of nextInt is that one int value in the specified range is
 pseudorandomly generated and returned. All n possible int values are produced
 with (approximately) equal probability. The method nextInt(int n) is implemented by class
 Random as if by:
 
 
 public int nextInt(int n) {
   if (n <= 0)
     throw new IllegalArgumentException("n must be positive");
   if ((n & -n) == n)  // i.e., n is a power of 2
     return (int)((n * (long)next(31)) >> 31);
   int bits, val;
   do {
       bits = next(31);
       val = bits % n;
   } while (bits - val + (n-1) < 0);
   return val;
 }
 
 
 The hedge "approximately" is used in the foregoing description only because the next method is
 only approximately an unbiased source of independently chosen bits. If it were a perfect source
 of randomly chosen bits, then the algorithm shown would choose int values from the stated
 range with perfect uniformity.
 
The algorithm is slightly tricky. It rejects values that would result in an uneven distribution (due to the fact that 2^31 is not divisible by n). The probability of a value being rejected depends on n. The worst case is n=2^30+1, for which the probability of a reject is 1/2, and the expected number of iterations before the loop terminates is 2.
The algorithm treats the case where n is a power of two specially: it returns the correct number of high-order bits from the underlying pseudo-random number generator. In the absence of special treatment, the correct number of low-order bits would be returned. Linear congruential pseudo-random number generators such as the one implemented by this class are known to have short periods in the sequence of values of their low-order bits. Thus, this special case greatly increases the length of the sequence of values returned by successive calls to this method if n is a small power of two.
n - the bound on the random number to be returned. Must be positive.int value between 0
         (inclusive) and n (exclusive) from this random number generator's sequenceIllegalArgumentException - if n is not positivepublic long nextLong()
long value from this random number
 generator's sequence. The general contract of nextLong is that one long value is
 pseudorandomly generated and returned.
 
 The method nextLong is implemented by class Random as if by:
 
 
 public long nextLong() {
   return ((long)next(32) << 32) + next(32);
 }
 
 Because class Random uses a seed with only 48 bits, this algorithm will not return all
 possible long values.long value from this random number
         generator's sequencepublic void setSeed(long seed)
long seed. The general
 contract of setSeed is that it alters the state of this random number generator object so
 as to be in exactly the same state as if it had just been created with the argument seed
 as a seed. The method setSeed is implemented by class Random by atomically
 updating the seed to
 
  (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)
 
 and clearing the haveNextNextGaussian flag used by nextGaussian().
 
 The implementation of setSeed by class Random happens to use only 48 bits of the
 given seed. In general, however, an overriding method may use all 64 bits of the long
 argument as a seed value.
seed - the initial seed