package com.gtnewhorizon.structurelib.util; /* * A subclass of java.util.random that implements the Xorshift random number generator

- 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

Usage: XSRandom rand = new XSRandom(); //Instantiation x = rand.nextInt(); * //pull a random number

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();

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

This code is released * under the GNU Lesser General Public License Version 3 http://www.gnu.org/licenses/lgpl-3.0.txt */ import java.util.Random; import java.util.concurrent.atomic.AtomicLong; /** * XSTR - Xorshift ThermiteRandom Modified by Bogdan-G 03.06.2016 version 0.0.4 */ public class XSTR extends Random { private static final long serialVersionUID = 6208727693524452904L; private long seed; 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 * 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 while (true) { 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; } @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 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 will shift nbits bits * @return next seed */ @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; } private boolean haveNextNextGaussian = false; private 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, vs; do { v1 = 2 * nextDouble() - 1; // between -1 and 1 v2 = 2 * nextDouble() - 1; // between -1 and 1 vs = v1 * v1 + v2 * v2; } while (vs >= 1 || vs == 0); double multiplier = StrictMath.sqrt(-2 * StrictMath.log(vs) / vs); 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 */ @Override 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% long 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; } } } }