public Random() 
{
        this(seedUniquifier() ^ System.nanoTime());
}

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(long seed) 
{
        this.seed = new AtomicLong(initialScramble(seed));
}

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 #next .

The invocation {@code new Random(seed)} is equivalent to:

 {@code
Random rnd = new Random();
rnd.setSeed(seed);}

Parameters:

seed - the initial seed

Also see:

setSeed(long)

 protected int next(int bits) 
{
        long oldseed, nextseed;
        AtomicLong seed = this.seed;
        do {
            oldseed = seed.get();
            nextseed = (oldseed * multiplier + addend) & mask;
        } while (!seed.compareAndSet(oldseed, nextseed));
        return (int)(nextseed  > > > (48 - bits));
}

Generates the next pseudorandom number. Subclasses should override this, as this is used by all other methods.

The general contract of {@code next} is that it returns an {@code int} value and if the argument {@code bits} is between {@code 1} and {@code 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 {@code 0} or {@code 1}. The method {@code next} is implemented by class {@code Random} by atomically updating the seed to

{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}

and returning

{@code (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.

 public boolean nextBoolean() 
{
        return next(1) != 0;
}

Returns the next pseudorandom, uniformly distributed {@code boolean} value from this random number generator's sequence. The general contract of {@code nextBoolean} is that one {@code boolean} value is pseudorandomly generated and returned. The values {@code true} and {@code false} are produced with (approximately) equal probability.

The method {@code nextBoolean} is implemented by class {@code Random} as if by:

 {@code
public boolean nextBoolean() {
  return next(1) != 0;
}}

 public  void nextBytes(byte[] bytes) 
{
        for (int i = 0, len = bytes.length; i <  len; )
            for (int rnd = nextInt(),
                     n = Math.min(len - i, Integer.SIZE/Byte.SIZE);
                 n--  > 0; rnd  > >= Byte.SIZE)
                bytes[i++] = (byte)rnd;
}

Generates random bytes and places them into a user-supplied byte array. The number of random bytes produced is equal to the length of the byte array.

The method {@code nextBytes} is implemented by class {@code Random} as if by:

 {@code
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;
}}

 public double nextDouble() 
{
        return (((long)(next(26)) < <  27) + next(27))
            / (double)(1L < <  53);
}

Returns the next pseudorandom, uniformly distributed {@code double} value between {@code 0.0} and {@code 1.0} from this random number generator's sequence.

The general contract of {@code nextDouble} is that one {@code double} value, chosen (approximately) uniformly from the range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is pseudorandomly generated and returned.

The method {@code nextDouble} is implemented by class {@code Random} as if by:

 {@code
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 {@code 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 {@code double} values from the stated range with perfect uniformity.

[In early versions of Java, the result was incorrectly calculated as:

 {@code
  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.]

 public float nextFloat() 
{
        return next(24) / ((float)(1 < <  24));
}

Returns the next pseudorandom, uniformly distributed {@code float} value between {@code 0.0} and {@code 1.0} from this random number generator's sequence.

The general contract of {@code nextFloat} is that one {@code float} value, chosen (approximately) uniformly from the range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is pseudorandomly generated and returned. All 2²⁴ possible {@code 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 {@code nextFloat} is implemented by class {@code Random} as if by:

 {@code
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 {@code float} values from the stated range with perfect uniformity.

[In early versions of Java, the result was incorrectly calculated as:

 {@code
  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.]

 public synchronized double nextGaussian() 
{
        // See Knuth, ACP, Section 3.4.1 Algorithm C.
        if (haveNextNextGaussian) {
            haveNextNextGaussian = false;
            return nextNextGaussian;
        } else {
            double v1, v2, s;
            do {
                v1 = 2 * nextDouble() - 1; // between -1 and 1
                v2 = 2 * nextDouble() - 1; // between -1 and 1
                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;
        }
}

Returns the next pseudorandom, Gaussian ("normally") distributed {@code double} value with mean {@code 0.0} and standard deviation {@code 1.0} from this random number generator's sequence.

The general contract of {@code nextGaussian} is that one {@code double} value, chosen from (approximately) the usual normal distribution with mean {@code 0.0} and standard deviation {@code 1.0}, is pseudorandomly generated and returned.

The method {@code nextGaussian} is implemented by class {@code Random} as if by a threadsafe version of the following:

 {@code
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 {@code StrictMath.log} and one call to {@code StrictMath.sqrt}.

 public int nextInt() 
{
        return next(32);
}

Returns the next pseudorandom, uniformly distributed {@code int} value from this random number generator's sequence. The general contract of {@code nextInt} is that one {@code int} value is pseudorandomly generated and returned. All 2³² possible {@code int} values are produced with (approximately) equal probability.

The method {@code nextInt} is implemented by class {@code Random} as if by:

 {@code
public int nextInt() {
  return next(32);
}}

 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;
}

Returns a pseudorandom, uniformly distributed {@code int} value between 0 (inclusive) and the specified value (exclusive), drawn from this random number generator's sequence. The general contract of {@code nextInt} is that one {@code int} value in the specified range is pseudorandomly generated and returned. All {@code n} possible {@code int} values are produced with (approximately) equal probability. The method {@code nextInt(int n)} is implemented by class {@code Random} as if by:

 {@code
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 {@code 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.

 public long nextLong() 
{
        // it's okay that the bottom word remains signed.
        return ((long)(next(32)) < <  32) + next(32);
}

Returns the next pseudorandom, uniformly distributed {@code long} value from this random number generator's sequence. The general contract of {@code nextLong} is that one {@code long} value is pseudorandomly generated and returned.

The method {@code nextLong} is implemented by class {@code Random} as if by:

 {@code
public long nextLong() {
  return ((long)next(32) << 32) + next(32);
}}

Because class {@code Random} uses a seed with only 48 bits, this algorithm will not return all possible {@code long} values.

 public synchronized  void setSeed(long seed) 
{
        this.seed.set(initialScramble(seed));
        haveNextNextGaussian = false;
}

Sets the seed of this random number generator using a single {@code long} seed. The general contract of {@code 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 {@code seed} as a seed. The method {@code setSeed} is implemented by class {@code Random} by atomically updating the seed to

{@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}

and clearing the {@code haveNextNextGaussian} flag used by #nextGaussian .

The implementation of {@code setSeed} by class {@code Random} happens to use only 48 bits of the given seed. In general, however, an overriding method may use all 64 bits of the {@code long} argument as a seed value.