File	Doc	Category	Size	Date	Package
Random.java	API Doc	Java SE 5 API	21840	Fri Aug 26 14:57:24 BST 2005	java.util

Random

• java.lang.Object

Fields Summary
static final long	serialVersionUID use serialVersionUID from JDK 1.1 for interoperability
private AtomicLong	seed The internal state associated with this pseudorandom number generator. (The specs for the methods in this class describe the ongoing computation of this value.)
private static final long	multiplier
private static final long	addend
private static final long	mask
private static volatile long	seedUniquifier
private static final int	BITS_PER_BYTE
private static final int	BYTES_PER_INT
private double	nextNextGaussian
private boolean	haveNextNextGaussian
private static final ObjectStreamField[]	serialPersistentFields Serializable fields for Random.

Constructors Summary
public Random() 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. this(++seedUniquifier + System.nanoTime());
public Random(long seed) Creates a new random number generator using a single long seed: public Random(long seed) { setSeed(seed); } Used by method next to hold the state of the pseudorandom number generator. param seed the initial seed. see java.util.Random#setSeed(long) this.seed = new AtomicLong(0L); setSeed(seed);

Methods Summary
protected int	next(int bits) Generates the next pseudorandom number. Subclass should override this, as this is used by all other methods. 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 as follows: synchronized protected int next(int bits) { seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1); return (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 2: Seminumerical Algorithms, section 3.2.1. param bits random bits return the next pseudorandom value from this random number generator's sequence. since JDK1.1 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));
public boolean	nextBoolean() Returns the next pseudorandom, uniformly distributed 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 follows: public boolean nextBoolean() {return next(1) != 0;} return the next pseudorandom, uniformly distributed boolean value from this random number generator's sequence. since 1.2 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. param bytes the non-null byte array in which to put the random bytes. since JDK1.1 int numRequested = bytes.length; int numGot = 0, rnd = 0; while (true) { for (int i = 0; i < BYTES_PER_INT; i++) { if (numGot == numRequested) return; rnd = (i==0 ? next(BITS_PER_BYTE * BYTES_PER_INT) : rnd >> BITS_PER_BYTE); bytes[numGot++] = (byte)rnd; } }
public double	nextDouble() Returns the next pseudorandom, uniformly distributed 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. All 253 possible float values of the form m x 2-53 , where m is a positive integer less than 253, are produced with (approximately) equal probability. The method nextDouble is implemented by class Random as follows: 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 or 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.] return the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence. long l = ((long)(next(26)) << 27) + next(27); return l / (double)(1L << 53);
public float	nextFloat() Returns the next pseudorandom, uniformly distributed 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 follows: 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 or 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.] return the next pseudorandom, uniformly distributed float value between 0.0 and 1.0 from this random number generator's sequence. int i = next(24); return i / ((float)(1 << 24));
public synchronized double	nextGaussian() Returns the next pseudorandom, Gaussian ("normally") distributed 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 follows: synchronized 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 = Math.sqrt(-2 * Math.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 2: 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 Math.log and one call to Math.sqrt. return the next pseudorandom, Gaussian ("normally") distributed double value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence. // 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 = Math.sqrt(-2 * Math.log(s)/s); nextNextGaussian = v2 * multiplier; haveNextNextGaussian = true; return v1 * multiplier; }
public int	nextInt() Returns the next pseudorandom, uniformly distributed 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 follows: public int nextInt() { return next(32); } return the next pseudorandom, uniformly distributed int value from this random number generator's sequence. return next(32);
public int	nextInt(int n) Returns a pseudorandom, uniformly distributed 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 follows: 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. param n the bound on the random number to be returned. Must be positive. return a pseudorandom, uniformly distributed int value between 0 (inclusive) and n (exclusive). exception IllegalArgumentException n is not positive. since 1.2 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;
public long	nextLong() Returns the next pseudorandom, uniformly distributed long value from this random number generator's sequence. The general contract of nextLong is that one long value is pseudorandomly generated and returned. All 264 possible long values are produced with (approximately) equal probability. The method nextLong is implemented by class Random as follows: public long nextLong() { return ((long)next(32) << 32) + next(32); } return the next pseudorandom, uniformly distributed long value from this random number generator's sequence. // it's okay that the bottom word remains signed. return ((long)(next(32)) << 32) + next(32);
private void	readObject(java.io.ObjectInputStream s) Reconstitute the Random instance from a stream (that is, deserialize it). The seed is read in as long for historical reasons, but it is converted to an AtomicLong. ObjectInputStream.GetField fields = s.readFields(); long seedVal; seedVal = (long) fields.get("seed", -1L); if (seedVal < 0) throw new java.io.StreamCorruptedException( "Random: invalid seed"); seed = new AtomicLong(seedVal); nextNextGaussian = fields.get("nextNextGaussian", 0.0); haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
public synchronized void	setSeed(long seed) Sets the seed of this random number generator using a single 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 as follows: synchronized public void setSeed(long seed) { this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1); haveNextNextGaussian = false; } 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. Note: Although the seed value is an AtomicLong, this method must still be synchronized to ensure correct semantics of haveNextNextGaussian. param seed the initial seed. seed = (seed ^ multiplier) & mask; this.seed.set(seed); haveNextNextGaussian = false;
private synchronized void	writeObject(java.io.ObjectOutputStream s) Save the Random instance to a stream. The seed of a Random is serialized as a long for historical reasons. // set the values of the Serializable fields ObjectOutputStream.PutField fields = s.putFields(); fields.put("seed", seed.get()); fields.put("nextNextGaussian", nextNextGaussian); fields.put("haveNextNextGaussian", haveNextNextGaussian); // save them s.writeFields();