package gregtech.api.objects;
import java.util.Random;
import java.util.concurrent.atomic.AtomicLong;
/*
* TODO: Check the validity of the algorithm.
* There is a claim that this particular implementation is not faithful to the articles it links, skewing the
* distribution.
*/
/**
* XSTR - Xorshift ThermiteRandom Modified by Bogdan-G 03.06.2016 version 0.0.4
* <p>
* A subclass of java.util.random that implements the Xorshift random number generator
* <p>
* - it is 30% faster than the generator from Java's library - it produces random sequences of higher quality than
* java.util.Random - this class also provides a clone() function
* <p>
* Usage: XSRandom rand = new XSRandom(); //Instantiation x = rand.nextInt(); //pull a random number
* <p>
* To use the class in legacy code, you may also instantiate an XSRandom object and assign it to a java.util.Random
* object: java.util.Random rand = new XSRandom();
* <p>
* for an explanation of the algorithm, see http://demesos.blogspot.com/2011/09/pseudo-random-number-generators.html
*
* @author Wilfried Elmenreich University of Klagenfurt/Lakeside Labs http://www.elmenreich.tk
* <p>
* This code is released under the GNU Lesser General Public License Version 3
* http://www.gnu.org/licenses/lgpl-3.0.txt
*/
public class XSTR extends Random {
private static final long serialVersionUID = 6208727693524452904L;
private long seed;
private long last;
private static final long GAMMA = 0x9e3779b97f4a7c15L;
private static final int PROBE_INCREMENT = 0x9e3779b9;
private static final long SEEDER_INCREMENT = 0xbb67ae8584caa73bL;
private static final double DOUBLE_UNIT = 0x1.0p-53; // 1.0 / (1L << 53)
private static final float FLOAT_UNIT = 0x1.0p-24f; // 1.0f / (1 << 24)
private static final AtomicLong seedUniquifier = new AtomicLong(8682522807148012L);
public static final XSTR XSTR_INSTANCE = new XSTR() {
@Override
public synchronized void setSeed(long seed) {
if (!Thread.currentThread()
.getStackTrace()[2].getClassName()
.equals(Random.class.getName()))
throw new NoSuchMethodError("This is meant to be shared!, leave seed state alone!");
}
};
/*
* MODIFIED BY: Robotia Modification: Implemented Random class seed generator
*/
/**
* Creates a new pseudo random number generator. The seed is initialized to the current time, as if by
* <code>setSeed(System.currentTimeMillis());</code>.
*/
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
* <code>setSeed(seed);</code>.
*
* @param seed the initial seed
*/
public XSTR(long seed) {
this.seed = seed;
}
@Override
public boolean nextBoolean() {
return next(1) != 0;
}
@Override
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
*/
@Override
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 Xorshift random generator that is 30% faster and better quality than the
* built-in java.util.random.
*
* @param nbits number of bits to shift the result for
* @return a random integer
* @see <a href="https://www.javamex.com/tutorials/random_numbers/xorshift.shtml">the Xorshift article</a>
*/
@Override
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;
@Override
public synchronized 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:
*
* <pre>
* {@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;
* }}
* </pre>
*
* <p>
* 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.
* <p>
* 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.
* <p>
* 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 <i>low-order</i> 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
*/
@Override
public int nextInt(int bound) {
last = seed ^ (seed << 21);
last ^= (last >>> 35);
last ^= (last << 4);
seed = last;
int out = (int) last % bound;
return (out < 0) ? -out : out;
}
@Override
public int nextInt() {
return next(32);
}
@Override
public float nextFloat() {
return next(24) * FLOAT_UNIT;
}
@Override
public long nextLong() {
// it's okay that the bottom word remains signed.
return ((long) (next(32)) << 32) + next(32);
}
@Override
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;
}
}