- * 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
+ * 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 Random use a - * protected utility method that on each invocation can supply + * 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 random method in
- * class Math simpler to use.
+ * Many applications will find the method {@link Math#random} simpler to use.
*
* @author Frank Yellin
- * @version 1.38, 03/04/02
- * @see java.lang.Math#random()
- * @since JDK1.0
+ * @since 1.0
*/
public
class Random implements java.io.Serializable {
/** use serialVersionUID from JDK 1.1 for interoperability */
static final long serialVersionUID = 3905348978240129619L;
- // Setup to use Unsafe.compareAndSwapLong to update seed.
- 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); }
- }
-
/**
* 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 volatile long seed;
+ private final 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. Its seed is initialized to
- * a value based on the current time:
- *
- * public Random() { this(System.currentTimeMillis()); }long seed:
- *
- * public Random(long seed) { setSeed(seed); }The invocation {@code new Random(seed)} is equivalent to: + *
{@code
+ * Random rnd = new Random();
+ * rnd.setSeed(seed);}
*
- * @param seed the initial seed.
- * @see java.util.Random#setSeed(long)
+ * @param seed the initial seed
+ * @see #setSeed(long)
*/
public Random(long seed) {
+ this.seed = new AtomicLong(0L);
setSeed(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;
- * }{@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. + * @param seed the initial seed */ synchronized public void setSeed(long seed) { - this.seed = (seed ^ multiplier) & mask; - haveNextNextGaussian = false; + 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 as follows: - *
- * synchronized protected int next(int bits) {
- * seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
- * return (int)(seed >>> (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 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;
- nextseed = (oldseed * multiplier + addend) & mask;
- } while (!unsafe.compareAndSwapLong(this, seedOffset,
- oldseed, nextseed));
+ 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
+ * 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
+ *
+ * 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) {
- 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;
- }
- }
+ 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 int
- * value from this random number generator's sequence. The general
- * contract of nextInt is that one int value is
+ * 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 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.
+ * 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); }
+ public int nextInt() {
+ return next(32);
+ }
/**
- * Returns a pseudorandom, uniformly distributed int value
+ * 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
- * 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:
- *
+ * {@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 <= 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);
+ * 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 + * 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 + * 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
@@ -248,15 +252,16 @@ class Random implements java.io.Serializ
* 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.
+ * 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
+ * @exception IllegalArgumentException if n is not positive
* @since 1.2
*/
public int nextInt(int n) {
- if (n<=0)
+ if (n <= 0)
throw new IllegalArgumentException("n must be positive");
if ((n & -n) == n) // i.e., n is a power of 2
@@ -266,25 +271,28 @@ class Random implements java.io.Serializ
do {
bits = next(31);
val = bits % n;
- } while(bits - val + (n-1) < 0);
+ } 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:
- *
+ * 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);
- * }long
- * value from this random number generator's sequence.
+ * 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.
@@ -293,104 +301,113 @@ class Random implements java.io.Serializ
/**
* 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.
+ * {@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;}
+ 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: - *
+ * 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: - *
- * 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- * return next(30) / ((float)(1 << 30));
float
- * value between 0.0 and 1.0 from this
- * random number generator's sequence.
+ * {@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() {
- int i = next(24);
- return i / ((float)(1 << 24));
+ return next(24) / ((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: - *
+ * 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 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. + * 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: - *
- * 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 - *- * 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.
+ * {@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() {
- long l = ((long)(next(26)) << 27) + next(27);
- return l / (double)(1L << 53);
+ return (((long)(next(26)) << 27) + next(27))
+ / (double)(1L << 53);
}
private double nextNextGaussian;
@@ -398,69 +415,74 @@ class Random implements java.io.Serializ
/**
* 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.
+ * {@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 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;
- * }
- * }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 Math.log
- * and one call to Math.sqrt.
+ * 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
- * double value with mean 0.0 and
- * standard deviation 1.0 from this random number
- * generator's sequence.
+ * @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 {
+ haveNextNextGaussian = false;
+ return nextNextGaussian;
+ } else {
double v1, v2, s;
- do {
+ do {
v1 = 2 * nextDouble() - 1; // between -1 and 1
- v2 = 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;
+ } 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;
+ * @serialField seed long
* seed for random computations
- * @serialField nextNextGaussian double;
+ * @serialField nextNextGaussian double
* next Gaussian to be returned
* @serialField haveNextNextGaussian boolean
* nextNextGaussian is valid
@@ -469,45 +491,56 @@ class Random implements java.io.Serializ
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.
+ * 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();
- long seedVal;
- seedVal = (long) fields.get("seed", -1L);
+ // The seed is read in as {@code long} for
+ // historical reasons, but it is converted to an AtomicLong.
+ long seedVal = (long) fields.get("seed", -1L);
if (seedVal < 0)
throw new java.io.StreamCorruptedException(
"Random: invalid seed");
- seed = seedVal;
+ resetSeed(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.
- *
+ * Save the {@code Random} instance to a stream.
*/
- synchronized private void writeObject(ObjectOutputStream s) throws IOException {
+ synchronized private void writeObject(ObjectOutputStream s)
+ throws IOException {
+
// set the values of the Serializable fields
ObjectOutputStream.PutField fields = s.putFields();
- fields.put("seed", seed);
+
+ // 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));
+ }
+}