setSeed(System.currentTimeMillis());.
*/
public XSTR() {
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;
}
}
}
/**
* Creates a new pseudo random number generator, starting with the specified
* seed, using setSeed(seed);.
*
* @param seed the initial seed
*/
public XSTR(long seed) {
this.seed = seed;
}
public boolean nextBoolean() {
return next(1) != 0;
}
public double nextDouble() {
return (((long)(next(26)) << 27) + next(27)) * DOUBLE_UNIT;
}
/**
* Returns the current state of the seed, can be used to clone the object
*
* @return the current seed
*/
public synchronized long getSeed() {
return seed;
}
/**
* Sets the seed for this pseudo random number generator. As described
* above, two instances of the same random class, starting with the same
* seed, produce the same results, if the same methods are called.
*
* @param seed the new seed
*/
public synchronized void setSeed(long seed) {
this.seed = seed;
}
/**
* @return Returns an XSRandom object with the same state as the original
*/
@Override
public XSTR clone() {
return new XSTR(getSeed());
}
/**
* Implementation of George Marsaglia's elegant Xorshift random generator
* 30% faster and better quality than the built-in java.util.random see also
* see http://www.javamex.com/tutorials/random_numbers/xorshift.shtml
*
* @param nbits
* @return
*/
public int next(int nbits) {
long x = seed;
x ^= (x << 21);
x ^= (x >>> 35);
x ^= (x << 4);
seed = x;
x &= ((1L << nbits) - 1);
return (int) x;
}
boolean haveNextNextGaussian = false;
double nextNextGaussian = 0;
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;
}
}
/**
* 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 bound} possible {@code int} values are
* produced with (approximately) equal probability. The method
* {@code nextInt(int bound)} is implemented by class {@code Random} as if
* by:
* {@code
* public int nextInt(int bound) {
* if (bound <= 0)
* throw new IllegalArgumentException("bound must be positive");
*
* if ((bound & -bound) == bound) // i.e., bound is a power of 2
* return (int)((bound * (long)next(31)) >> 31);
*
* int bits, val;
* do {
* bits = next(31);
* val = bits % bound;
* } while (bits - val + (bound-1) < 0);
* return val;
* }}
*
* The hedge "approx * imately" 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 bound the upper bound (exclusive). Must be positive. * @return the next pseudorandom, uniformly distributed {@code int} value * between zero (inclusive) and {@code bound} (exclusive) from this random * number generator's sequence * @throws IllegalArgumentException if bound is not positive * @since 1.2 */ public int nextInt(int bound) { //if (bound <= 0) { //throw new RuntimeException("BadBound"); //} /*int r = next(31); int m = bound - 1; if ((bound & m) == 0) // i.e., bound is a power of 2 { r = (int) ((bound * (long) r) >> 31); } else { for (int u = r; u - (r = u % bound) + m < 0; u = next(31)) ; } return r;*/ //speedup, new nextInt ~+40% last = seed ^ (seed << 21); last ^= (last >>> 35); last ^= (last << 4); seed = last; int out = (int) last % bound; return (out < 0) ? -out : out; } public int nextInt() { return next(32); } public float nextFloat() { return next(24) * FLOAT_UNIT; } public long nextLong() { // it's okay that the bottom word remains signed. return ((long)(next(32)) << 32) + next(32); } public void nextBytes(byte[] bytes_arr) { for (int iba = 0, lenba = bytes_arr.length; iba < lenba; ) for (int rndba = nextInt(), nba = Math.min(lenba - iba, Integer.SIZE/Byte.SIZE); nba-- > 0; rndba >>= Byte.SIZE) bytes_arr[iba++] = (byte)rndba; } }