* If two instances of 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 Random. Java implementations must use all the algorithms
* shown here for the class Random, for the sake of absolute
* portability of Java code. However, subclasses of class 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 Random use a * protected utility method that on each invocation can supply * up to 32 pseudorandomly generated bits. *
* Many applications will find the random method in
* class Math simpler to use.
*
* @author Frank Yellin
* @version %I%, %G%
* @see java.lang.Math#random()
* @since JDK1.0
*/
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.)
*
* @serial
*/
private AtomicLong seed;
private final static long multiplier = 0x5DEECE66DL;
private final static long addend = 0xBL;
private final static 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 volatile long seedUniquifier = 8682522807148012L;
/**
* Creates a new random number generator using a single
* long seed:
*
* public Random(long seed) { setSeed(seed); }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 using a thread-safe update of the seed to (seed *
* 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1) and clearing the
* haveNextNextGaussian flag used by {@link
* #nextGaussian}. 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.
*
* @param seed the initial seed.
*/
synchronized public void setSeed(long seed) {
seed = (seed ^ multiplier) & mask;
this.seed.set(seed);
haveNextNextGaussian = false;
}
/**
* 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 using a thread-safe update of the seed to
* (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1) and
* returning (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
*/
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));
}
private static final int BITS_PER_BYTE = 8;
private static final int BYTES_PER_INT = 4;
/**
* 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
*/
public void nextBytes(byte[] bytes) {
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;
}
}
}
/**
* 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); }int
* value from this random number generator's sequence.
*/
public int nextInt() { return next(32); }
/**
* 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
*/
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 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);
* }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
* 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;}
* 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 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));
* }* [In early versions of Java, the result was incorrectly calculated as: *
* return next(30) / ((float)(1 << 30));float
* value between 0.0 and 1.0 from this
* random number generator's sequence.
*/
public float nextFloat() {
int i = next(24);
return i / ((float)(1 << 24));
}
/**
* 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);double value between 0.0 and
* 1.0 from this random number generator's sequence.
*/
public double nextDouble() {
long l = ((long)(next(26)) << 27) + next(27);
return l / (double)(1L << 53);
}
private double nextNextGaussian;
private boolean haveNextNextGaussian = false;
/**
* 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 if * by a threadsafe version of the following: *
* 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;
* }
* }double value with mean 0.0 and
* standard deviation 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 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.
*/
private void readObject(java.io.ObjectInputStream s)
throws java.io.IOException, ClassNotFoundException {
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);
}
/**
* Save the Random instance to a stream.
* The seed of a Random is serialized as a long for
* historical reasons.
*
*/
synchronized private void writeObject(ObjectOutputStream s) throws IOException {
// 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();
}
}