java.lang.Object java.util.Random
All Implemented Interfaces:
java$io$Serializable
Direct Known Subclasses:
SecureRandom, ThreadLocalRandom
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 pseudorandom number generator for use by securitysensitive applications.
Frank
 Yellin1.0
 Field Summary  

static final long  serialVersionUID  use serialVersionUID from JDK 1.1 for interoperability 
Constructor: 

public Random(){ this(seedUniquifier() ^ System.nanoTime()); } 
public Random(long seed){ this.seed = new AtomicLong(initialScramble(seed)); }
The invocation {@code new Random(seed)} is equivalent to: {@code Random rnd = new Random(); rnd.setSeed(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){ 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)); }
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 loworder 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; }
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; }
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); }
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 floatingpoint numbers: it was three times as likely that the loworder 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)); }
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 2^{24} possible {@code float} values of the form m x 2^{24}, where m is a positive integer less than 2^{24} , 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 floatingpoint numbers: it was slightly more likely that the loworder 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; } }
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); }
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 + (n1) < 0); return val; }
{@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 + (n1) < 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 highorder bits from the underlying pseudorandom number generator. In the absence of special treatment, the correct number of loworder bits would be returned. Linear congruential pseudorandom number generators such as the one implemented by this class are known to have short periods in the sequence of values of their loworder 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); }
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; }
{@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. 