* 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 {@link 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 * {@link java.util.concurrent.ThreadLocalRandom} in multithreaded * designs. * *
Instances of {@code java.util.Random} are not cryptographically * secure. Consider instead using {@link java.security.SecureRandom} to * get a cryptographically secure pseudo-random number generator for use * by security-sensitive applications. * * @author Frank Yellin * @since 1.0 */ public class Random implements java.io.Serializable { /** use serialVersionUID from JDK 1.1 for interoperability */ static final long serialVersionUID = 3905348978240129619L; /** * 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 final AtomicLong seed; private static final long multiplier = 0x5DEECE66DL; private static final long addend = 0xBL; private static final long mask = (1L << 48) - 1; /** * 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() { this(seedUniquifier() ^ System.nanoTime()); } private static long seedUniquifier() { // L'Ecuyer, "Tables of Linear Congruential Generators of // Different Sizes and Good Lattice Structure", 1999 for (;;) { long current = seedUniquifier.get(); long next = current * 181783497276652981L; if (seedUniquifier.compareAndSet(current, next)) return next; } } private static final AtomicLong seedUniquifier = new AtomicLong(8682522807148012L); /** * 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 {@link #next}. * *
The invocation {@code new Random(seed)} is equivalent to: *
{@code
* Random rnd = new Random();
* rnd.setSeed(seed);}
*
* @param seed the initial seed
* @see #setSeed(long)
*/
public Random(long seed) {
if (getClass() == Random.class)
this.seed = new AtomicLong(initialScramble(seed));
else {
// subclass might have overriden setSeed
this.seed = new AtomicLong();
setSeed(seed);
}
}
private static long initialScramble(long seed) {
return (seed ^ multiplier) & mask;
}
/**
* 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 {@link
* #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. * * @param seed the initial seed */ synchronized public void setSeed(long seed) { this.seed.set(initialScramble(seed)); haveNextNextGaussian = false; } /** * 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.
*
* @param bits random bits
* @return the next pseudorandom value from this random number
* generator's sequence
* @since 1.1
*/
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 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;
* }}
*
* @param bytes the byte array to fill with random bytes
* @throws NullPointerException if the byte array is null
* @since 1.1
*/
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;
}
/**
* 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);
* }}
*
* @return the next pseudorandom, uniformly distributed {@code int}
* value from this random number generator's sequence
*/
public int nextInt() {
return next(32);
}
/**
* 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. * * @param n the bound on the random number to be returned. Must be * positive. * @return the next pseudorandom, uniformly distributed {@code int} * value between {@code 0} (inclusive) and {@code n} (exclusive) * from this random number generator's sequence * @throws IllegalArgumentException if n is not positive * @since 1.2 */ 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 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.
*
* @return the next pseudorandom, uniformly distributed {@code long}
* value from this random number generator's sequence
*/
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 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;
* }}
*
* @return the next pseudorandom, uniformly distributed
* {@code boolean} value from this random number generator's
* sequence
* @since 1.2
*/
public boolean nextBoolean() {
return next(1) != 0;
}
/**
* 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.]
*
* @return the next pseudorandom, uniformly distributed {@code float}
* value between {@code 0.0} and {@code 1.0} from this
* random number generator's sequence
*/
public float nextFloat() {
return next(24) / ((float)(1 << 24));
}
/**
* 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.]
*
* @return the next pseudorandom, uniformly distributed {@code double}
* value between {@code 0.0} and {@code 1.0} from this
* random number generator's sequence
* @see Math#random
*/
public double nextDouble() {
return (((long)(next(26)) << 27) + next(27))
/ (double)(1L << 53);
}
private double nextNextGaussian;
private boolean haveNextNextGaussian = false;
/**
* 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}.
*
* @return 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
*/
synchronized public 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;
}
}
/**
* Serializable fields for Random.
*
* @serialField seed long
* seed for random computations
* @serialField nextNextGaussian double
* next Gaussian to be returned
* @serialField haveNextNextGaussian boolean
* nextNextGaussian is valid
*/
private static final ObjectStreamField[] serialPersistentFields = {
new ObjectStreamField("seed", Long.TYPE),
new ObjectStreamField("nextNextGaussian", Double.TYPE),
new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
};
/**
* Reconstitute the {@code Random} instance from a stream (that is,
* deserialize it).
*/
private void readObject(java.io.ObjectInputStream s)
throws java.io.IOException, ClassNotFoundException {
ObjectInputStream.GetField fields = s.readFields();
// The seed is read in as {@code long} for
// historical reasons, but it is converted to an AtomicLong.
long seedVal = fields.get("seed", -1L);
if (seedVal < 0)
throw new java.io.StreamCorruptedException(
"Random: invalid seed");
resetSeed(seedVal);
nextNextGaussian = fields.get("nextNextGaussian", 0.0);
haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
}
/**
* Save the {@code Random} instance to a stream.
*/
synchronized private void writeObject(ObjectOutputStream s)
throws IOException {
// set the values of the Serializable fields
ObjectOutputStream.PutField fields = s.putFields();
// The seed is serialized as a long for historical reasons.
fields.put("seed", seed.get());
fields.put("nextNextGaussian", nextNextGaussian);
fields.put("haveNextNextGaussian", haveNextNextGaussian);
// save them
s.writeFields();
}
// Support for resetting seed while deserializing
private static final Unsafe unsafe = Unsafe.getUnsafe();
private static final long seedOffset;
static {
try {
seedOffset = unsafe.objectFieldOffset
(Random.class.getDeclaredField("seed"));
} catch (Exception ex) { throw new Error(ex); }
}
private void resetSeed(long seedVal) {
unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal));
}
}