- * 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 long 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 synchronized long seedUniquifier() { - // L'Ecuyer, "Tables of Linear Congruential Generators of - // Different Sizes and Good Lattice Structure", 1999 - long current = seedUniquifier; - long next = current * 181783497276652981L; - seedUniquifier = next; - return next; - } - - private static long seedUniquifier = 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) {
- this.seed = initialScramble(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 = 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 synchronized int next(int bits) {
- long oldseed, nextseed;
- long seed = this.seed;
- oldseed = seed;
- nextseed = (oldseed * multiplier + addend) & mask;
- this.seed = 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 = Math.sqrt(-2 * Math.log(s)/s);
- nextNextGaussian = v2 * multiplier;
- haveNextNextGaussian = true;
- return v1 * multiplier;
- }
- }
-}