Randompublic class Random extends Object implements Serializable| An instance of this class is used to generate a stream of
pseudorandom numbers. The class uses a 48-bit seed, which is
modified using a linear congruential formula. (See Donald Knuth,
The Art of Computer Programming, Volume 3, Section 3.2.1.)
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. |
| Fields Summary |
|---|
| static final long | serialVersionUIDuse serialVersionUID from JDK 1.1 for interoperability | | private final AtomicLong | seedThe 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 double | nextNextGaussian | | private boolean | haveNextNextGaussian | | private static final ObjectStreamField[] | serialPersistentFieldsSerializable fields for Random. | | private static final Unsafe | unsafe | | private static final long | seedOffset |
| 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 {@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);}
this.seed = new AtomicLong(0L);
setSeed(seed);
|
| Methods Summary |
|---|
| protected int | next(int bits)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.
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
{@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 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.
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;
}}
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;
| | public double | nextDouble()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 (((long)(next(26)) << 27) + next(27))
/ (double)(1L << 53);
| | public float | nextFloat()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 next(24) / ((float)(1 << 24));
| | public synchronized double | nextGaussian()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}.
// 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;
}
| | public int | nextInt()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 next(32);
| | public int | nextInt(int n)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.
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 {@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.
// 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 {@code Random} instance from a stream (that is,
deserialize it).
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 = (long) 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);
| | private void | resetSeed(long seedVal)
try {
seedOffset = unsafe.objectFieldOffset
(Random.class.getDeclaredField("seed"));
} catch (Exception ex) { throw new Error(ex); }
unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal));
| | public synchronized void | setSeed(long seed)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.
seed = (seed ^ multiplier) & mask;
this.seed.set(seed);
haveNextNextGaussian = false;
| | private synchronized void | writeObject(java.io.ObjectOutputStream s)Save the {@code Random} instance to a stream.
// 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();
|
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