Save This Page
Home » openjdk-7 » java » util » [javadoc | source]
java.util
public class: Random [javadoc | source]
java.lang.Object
   java.util.Random

All Implemented Interfaces:
    java$io$Serializable

Direct Known Subclasses:
    SecureRandom, ThreadLocalRandom

An instance of this class is used to generate a stream of pseudorandom numbers. The class uses a 48-bit seed, which is modified using a linear congruential formula. (See Donald Knuth, The Art of Computer Programming, Volume 2, Section 3.2.1.)

If two instances of {@code 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 {@code Random}. Java implementations must use all the algorithms shown here for the class {@code Random}, for the sake of absolute portability of Java code. However, subclasses of class {@code 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 {@code Random} use a {@code 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.

Instances of {@code java.util.Random} are threadsafe. However, the concurrent use of the same {@code java.util.Random} instance across threads may encounter contention and consequent poor performance. Consider instead using java.util.concurrent.ThreadLocalRandom in multithreaded designs.

Instances of {@code java.util.Random} are not cryptographically secure. Consider instead using java.security.SecureRandom to get a cryptographically secure pseudo-random number generator for use by security-sensitive applications.

Field Summary
static final  long serialVersionUID    use serialVersionUID from JDK 1.1 for interoperability 
Constructor:
 public Random() 
 public Random(long seed) 
Method from java.util.Random Summary:
next,   nextBoolean,   nextBytes,   nextDouble,   nextFloat,   nextGaussian,   nextInt,   nextInt,   nextLong,   setSeed
Methods from java.lang.Object:
clone,   equals,   finalize,   getClass,   hashCode,   notify,   notifyAll,   toString,   wait,   wait,   wait
Method from java.util.Random Detail:
 protected int next(int 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() 
    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) 
    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() 
    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() 
    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() 
    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() 
    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) 
    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() 
    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) 
    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.