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Revision **1.6** -
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*Sat Feb 7 23:29:18 2015 UTC*
(4 years ago)
by *jsr166*

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Changes since**1.5: +1 -1 lines**

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remedy copy-paste error of L'Ecuyer MLCG constant

/* * Copyright (c) 1995, 2010, Oracle and/or its affiliates. All rights reserved. * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER. * * This code is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License version 2 only, as * published by the Free Software Foundation. Oracle designates this * particular file as subject to the "Classpath" exception as provided * by Oracle in the LICENSE file that accompanied this code. * * This code is distributed in the hope that it will be useful, but WITHOUT * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License * version 2 for more details (a copy is included in the LICENSE file that * accompanied this code). * * You should have received a copy of the GNU General Public License version * 2 along with this work; if not, write to the Free Software Foundation, * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA. * * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA * or visit www.oracle.com if you need additional information or have any * questions. */ package java.util; import java.io.*; import java.util.concurrent.atomic.AtomicLong; import sun.misc.Unsafe; /** * 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, * <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.) * <p> * 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. * <p> * 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. * <p> * Many applications will find the method {@link Math#random} simpler to use. * * <p>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. * * <p>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 final AtomicLong 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 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 * 1181783497276652981L; if (seedUniquifier.compareAndSet(current, next)) return next; } } private static final AtomicLong seedUniquifier = new AtomicLong(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}. * * <p>The invocation {@code new Random(seed)} is equivalent to: * <pre> {@code * Random rnd = new Random(); * rnd.setSeed(seed);}</pre> * * @param seed the initial seed * @see #setSeed(long) */ public Random(long seed) { if (getClass() == Random.class) this.seed = new AtomicLong(initialScramble(seed)); else { // subclass might have overridden setSeed this.seed = new AtomicLong(); setSeed(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 * <pre>{@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}</pre> * and clearing the {@code haveNextNextGaussian} flag used by {@link * #nextGaussian}. * * <p>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 */ public synchronized void setSeed(long seed) { this.seed.set(initialScramble(seed)); haveNextNextGaussian = false; } /** * Generates the next pseudorandom number. Subclasses should * override this, as this is used by all other methods. * * <p>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 * <pre>{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}</pre> * and returning * <pre>{@code (int)(seed >>> (48 - bits))}.</pre> * * This is a linear congruential pseudorandom number generator, as * defined by D. H. Lehmer and described by Donald E. Knuth in * <i>The Art of Computer Programming,</i> Volume 3: * <i>Seminumerical Algorithms</i>, section 3.2.1. * * @param bits random bits * @return the next pseudorandom value from this random number * generator's sequence * @since 1.1 */ protected int next(int bits) { 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)); } /** * 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. * * <p>The method {@code nextBytes} is implemented by class {@code Random} * as if by: * <pre> {@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; * }}</pre> * * @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 2<font size="-1"><sup>32 * </sup></font> possible {@code int} values are produced with * (approximately) equal probability. * * <p>The method {@code nextInt} is implemented by class {@code Random} * as if by: * <pre> {@code * public int nextInt() { * return next(32); * }}</pre> * * @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: * <pre> {@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; * }}</pre> * * <p>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. * <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 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. * * <p>The method {@code nextLong} is implemented by class {@code Random} * as if by: * <pre> {@code * public long nextLong() { * return ((long)next(32) << 32) + next(32); * }}</pre> * * 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. * * <p>The method {@code nextBoolean} is implemented by class {@code Random} * as if by: * <pre> {@code * public boolean nextBoolean() { * return next(1) != 0; * }}</pre> * * @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. * * <p>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 2<font * size="-1"><sup>24</sup></font> possible {@code float} values * of the form <i>m x </i>2<font * size="-1"><sup>-24</sup></font>, where <i>m</i> is a positive * integer less than 2<font size="-1"><sup>24</sup> </font>, are * produced with (approximately) equal probability. * * <p>The method {@code nextFloat} is implemented by class {@code Random} * as if by: * <pre> {@code * public float nextFloat() { * return next(24) / ((float)(1 << 24)); * }}</pre> * * <p>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.<p> * [In early versions of Java, the result was incorrectly calculated as: * <pre> {@code * return next(30) / ((float)(1 << 30));}</pre> * 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. * * <p>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. * * <p>The method {@code nextDouble} is implemented by class {@code Random} * as if by: * <pre> {@code * public double nextDouble() { * return (((long)next(26) << 27) + next(27)) * / (double)(1L << 53); * }}</pre> * * <p>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. * <p>[In early versions of Java, the result was incorrectly calculated as: * <pre> {@code * return (((long)next(27) << 27) + next(27)) * / (double)(1L << 54);}</pre> * 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. * <p> * 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. * * <p>The method {@code nextGaussian} is implemented by class * {@code Random} as if by a threadsafe version of the following: * <pre> {@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; * } * }}</pre> * This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and * G. Marsaglia, as described by Donald E. Knuth in <i>The Art of * Computer Programming</i>, Volume 3: <i>Seminumerical Algorithms</i>, * 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 */ 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; } } /** * Serializable fields for Random. * * @serialField seed long * seed for random computations * @serialField nextNextGaussian double * next Gaussian to be returned * @serialField haveNextNextGaussian boolean * nextNextGaussian is valid */ private static final ObjectStreamField[] serialPersistentFields = { new ObjectStreamField("seed", Long.TYPE), new ObjectStreamField("nextNextGaussian", Double.TYPE), new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE) }; /** * Reconstitute the {@code Random} instance from a stream (that is, * deserialize it). */ private void readObject(java.io.ObjectInputStream s) throws java.io.IOException, ClassNotFoundException { 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 = 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); } /** * Save the {@code Random} instance to a stream. */ private synchronized void writeObject(ObjectOutputStream s) throws IOException { // 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(); } // Support for resetting seed while deserializing private static final Unsafe unsafe = Unsafe.getUnsafe(); private static final long seedOffset; static { try { seedOffset = unsafe.objectFieldOffset (Random.class.getDeclaredField("seed")); } catch (Exception ex) { throw new Error(ex); } } private void resetSeed(long seedVal) { unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal)); } }

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