- 001
/* - 002
* @(#)Random.java 1.47 06/02/07 - 003 *
- 004 * Copyright 2006 Sun Microsystems, Inc. All rights reserved.
- 005 * SUN PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
- 006
*/ - 007
- 008package java.util;
- 009import java.io.*;
- 010import java.util.concurrent.atomic.AtomicLong;
- 011import sun.misc.Unsafe;
- 012
- 013/**
- 014 * An instance of this class is used to generate a stream of
- 015 * pseudorandom numbers. The class uses a 48-bit seed, which is
- 016 * modified using a linear congruential formula. (See Donald Knuth,
- 017 * <i>The Art of Computer Programming, Volume 3</i>, Section 3.2.1.)
- 018 * <p>
- 019 * If two instances of {@code Random} are created with the same
- 020 * seed, and the same sequence of method calls is made for each, they
- 021 * will generate and return identical sequences of numbers. In order to
- 022 * guarantee this property, particular algorithms are specified for the
- 023 * class {@code Random}. Java implementations must use all the algorithms
- 024 * shown here for the class {@code Random}, for the sake of absolute
- 025 * portability of Java code. However, subclasses of class {@code Random}
- 026 * are permitted to use other algorithms, so long as they adhere to the
- 027 * general contracts for all the methods.
- 028 * <p>
- 029 * The algorithms implemented by class {@code Random} use a
- 030 * {@code protected} utility method that on each invocation can supply
- 031 * up to 32 pseudorandomly generated bits.
- 032 * <p>
- 033 * Many applications will find the method {@link Math#random} simpler to use.
- 034 *
- 035 * @author Frank Yellin
- 036 * @version 1.47, 02/07/06
- 037 * @since 1.0
- 038 */
- 039public
- 040class Random implements java.io.Serializable {
- 041 /** use serialVersionUID from JDK 1.1 for interoperability */
- 042 static final long serialVersionUID = 3905348978240129619L;
- 043
- 044 /**
- 045 * The internal state associated with this pseudorandom number generator.
- 046 * (The specs for the methods in this class describe the ongoing
- 047 * computation of this value.)
- 048 *
- 049 * @serial
- 050 */
- 051 private final AtomicLong seed;
- 052
- 053 private final static long multiplier = 0x5DEECE66DL;
- 054 private final static long addend = 0xBL;
- 055 private final static long mask = (1L << 48) - 1;
- 056
- 057 /**
- 058 * Creates a new random number generator. This constructor sets
- 059 * the seed of the random number generator to a value very likely
- 060 * to be distinct from any other invocation of this constructor.
- 061 */
- 062 public Random() { this(++seedUniquifier + System.nanoTime()); }
- 063 private static volatile long seedUniquifier = 8682522807148012L;
- 064
- 065 /**
- 066 * Creates a new random number generator using a single {@code long} seed.
- 067 * The seed is the initial value of the internal state of the pseudorandom
- 068 * number generator which is maintained by method {@link #next}.
- 069 *
- 070 * <p>The invocation {@code new Random(seed)} is equivalent to:
- 071 * <pre> {@code
- 072 * Random rnd = new Random();
- 073 * rnd.setSeed(seed);}</pre>
- 074 *
- 075 * @param seed the initial seed
- 076 * @see #setSeed(long)
- 077 */
- 078 public Random(long seed) {
- 079 this.seed = new AtomicLong(0L);
- 080 setSeed(seed);
- 081 }
- 082
- 083 /**
- 084 * Sets the seed of this random number generator using a single
- 085 * {@code long} seed. The general contract of {@code setSeed} is
- 086 * that it alters the state of this random number generator object
- 087 * so as to be in exactly the same state as if it had just been
- 088 * created with the argument {@code seed} as a seed. The method
- 089 * {@code setSeed} is implemented by class {@code Random} by
- 090 * atomically updating the seed to
- 091 * <pre>{@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}</pre>
- 092 * and clearing the {@code haveNextNextGaussian} flag used by {@link
- 093 * #nextGaussian}.
- 094 *
- 095 * <p>The implementation of {@code setSeed} by class {@code Random}
- 096 * happens to use only 48 bits of the given seed. In general, however,
- 097 * an overriding method may use all 64 bits of the {@code long}
- 098 * argument as a seed value.
- 099 *
- 100 * @param seed the initial seed
- 101 */
- 102 synchronized public void setSeed(long seed) {
- 103 seed = (seed ^ multiplier) & mask;
- 104 this.seed.set(seed);
- 105 haveNextNextGaussian = false;
- 106 }
- 107
- 108 /**
- 109 * Generates the next pseudorandom number. Subclasses should
- 110 * override this, as this is used by all other methods.
- 111 *
- 112 * <p>The general contract of {@code next} is that it returns an
- 113 * {@code int} value and if the argument {@code bits} is between
- 114 * {@code 1} and {@code 32} (inclusive), then that many low-order
- 115 * bits of the returned value will be (approximately) independently
- 116 * chosen bit values, each of which is (approximately) equally
- 117 * likely to be {@code 0} or {@code 1}. The method {@code next} is
- 118 * implemented by class {@code Random} by atomically updating the seed to
- 119 * <pre>{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}</pre>
- 120 * and returning
- 121 * <pre>{@code (int)(seed >>> (48 - bits))}.</pre>
- 122 *
- 123 * This is a linear congruential pseudorandom number generator, as
- 124 * defined by D. H. Lehmer and described by Donald E. Knuth in
- 125 * <i>The Art of Computer Programming,</i> Volume 3:
- 126 * <i>Seminumerical Algorithms</i>, section 3.2.1.
- 127 *
- 128 * @param bits random bits
- 129 * @return the next pseudorandom value from this random number
- 130 * generator's sequence
- 131 * @since 1.1
- 132 */
- 133 protected int next(int bits) {
- 134 long oldseed, nextseed;
- 135 AtomicLong seed = this.seed;
- 136 do {
- 137 oldseed = seed.get();
- 138 nextseed = (oldseed * multiplier + addend) & mask;
- 139 } while (!seed.compareAndSet(oldseed, nextseed));
- 140 return (int)(nextseed >>> (48 - bits));
- 141 }
- 142
- 143 /**
- 144 * Generates random bytes and places them into a user-supplied
- 145 * byte array. The number of random bytes produced is equal to
- 146 * the length of the byte array.
- 147 *
- 148 * <p>The method {@code nextBytes} is implemented by class {@code Random}
- 149 * as if by:
- 150 * <pre> {@code
- 151 * public void nextBytes(byte[] bytes) {
- 152 * for (int i = 0; i < bytes.length; )
- 153 * for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4);
- 154 * n-- > 0; rnd >>= 8)
- 155 * bytes[i++] = (byte)rnd;
- 156 * }}</pre>
- 157 *
- 158 * @param bytes the byte array to fill with random bytes
- 159 * @throws NullPointerException if the byte array is null
- 160 * @since 1.1
- 161 */
- 162 public void nextBytes(byte[] bytes) {
- 163 for (int i = 0, len = bytes.length; i < len; )
- 164 for (int rnd = nextInt(),
- 165 n = Math.min(len - i, Integer.SIZE/Byte.SIZE);
- 166 n-- > 0; rnd >>= Byte.SIZE)
- 167 bytes[i++] = (byte)rnd;
- 168 }
- 169
- 170 /**
- 171 * Returns the next pseudorandom, uniformly distributed {@code int}
- 172 * value from this random number generator's sequence. The general
- 173 * contract of {@code nextInt} is that one {@code int} value is
- 174 * pseudorandomly generated and returned. All 2<font size="-1"><sup>32
- 175 * </sup></font> possible {@code int} values are produced with
- 176 * (approximately) equal probability.
- 177 *
- 178 * <p>The method {@code nextInt} is implemented by class {@code Random}
- 179 * as if by:
- 180 * <pre> {@code
- 181 * public int nextInt() {
- 182 * return next(32);
- 183 * }}</pre>
- 184 *
- 185 * @return the next pseudorandom, uniformly distributed {@code int}
- 186 * value from this random number generator's sequence
- 187 */
- 188 public int nextInt() {
- 189 return next(32);
- 190 }
- 191
- 192 /**
- 193 * Returns a pseudorandom, uniformly distributed {@code int} value
- 194 * between 0 (inclusive) and the specified value (exclusive), drawn from
- 195 * this random number generator's sequence. The general contract of
- 196 * {@code nextInt} is that one {@code int} value in the specified range
- 197 * is pseudorandomly generated and returned. All {@code n} possible
- 198 * {@code int} values are produced with (approximately) equal
- 199 * probability. The method {@code nextInt(int n)} is implemented by
- 200 * class {@code Random} as if by:
- 201 * <pre> {@code
- 202 * public int nextInt(int n) {
- 203 * if (n <= 0)
- 204 * throw new IllegalArgumentException("n must be positive");
- 205 *
- 206 * if ((n & -n) == n) // i.e., n is a power of 2
- 207 * return (int)((n * (long)next(31)) >> 31);
- 208 *
- 209 * int bits, val;
- 210 * do {
- 211 * bits = next(31);
- 212 * val = bits % n;
- 213 * } while (bits - val + (n-1) < 0);
- 214 * return val;
- 215 * }}</pre>
- 216 *
- 217 * <p>The hedge "approximately" is used in the foregoing description only
- 218 * because the next method is only approximately an unbiased source of
- 219 * independently chosen bits. If it were a perfect source of randomly
- 220 * chosen bits, then the algorithm shown would choose {@code int}
- 221 * values from the stated range with perfect uniformity.
- 222 * <p>
- 223 * The algorithm is slightly tricky. It rejects values that would result
- 224 * in an uneven distribution (due to the fact that 2^31 is not divisible
- 225 * by n). The probability of a value being rejected depends on n. The
- 226 * worst case is n=2^30+1, for which the probability of a reject is 1/2,
- 227 * and the expected number of iterations before the loop terminates is 2.
- 228 * <p>
- 229 * The algorithm treats the case where n is a power of two specially: it
- 230 * returns the correct number of high-order bits from the underlying
- 231 * pseudo-random number generator. In the absence of special treatment,
- 232 * the correct number of <i>low-order</i> bits would be returned. Linear
- 233 * congruential pseudo-random number generators such as the one
- 234 * implemented by this class are known to have short periods in the
- 235 * sequence of values of their low-order bits. Thus, this special case
- 236 * greatly increases the length of the sequence of values returned by
- 237 * successive calls to this method if n is a small power of two.
- 238 *
- 239 * @param n the bound on the random number to be returned. Must be
- 240 * positive.
- 241 * @return the next pseudorandom, uniformly distributed {@code int}
- 242 * value between {@code 0} (inclusive) and {@code n} (exclusive)
- 243 * from this random number generator's sequence
- 244 * @exception IllegalArgumentException if n is not positive
- 245 * @since 1.2
- 246 */
- 247
- 248 public int nextInt(int n) {
- 249 if (n <= 0)
- 250 throw new IllegalArgumentException("n must be positive");
- 251
- 252 if ((n & -n) == n) // i.e., n is a power of 2
- 253 return (int)((n * (long)next(31)) >> 31);
- 254
- 255 int bits, val;
- 256 do {
- 257 bits = next(31);
- 258 val = bits % n;
- 259 } while (bits - val + (n-1) < 0);
- 260 return val;
- 261 }
- 262
- 263 /**
- 264 * Returns the next pseudorandom, uniformly distributed {@code long}
- 265 * value from this random number generator's sequence. The general
- 266 * contract of {@code nextLong} is that one {@code long} value is
- 267 * pseudorandomly generated and returned.
- 268 *
- 269 * <p>The method {@code nextLong} is implemented by class {@code Random}
- 270 * as if by:
- 271 * <pre> {@code
- 272 * public long nextLong() {
- 273 * return ((long)next(32) << 32) + next(32);
- 274 * }}</pre>
- 275 *
- 276 * Because class {@code Random} uses a seed with only 48 bits,
- 277 * this algorithm will not return all possible {@code long} values.
- 278 *
- 279 * @return the next pseudorandom, uniformly distributed {@code long}
- 280 * value from this random number generator's sequence
- 281 */
- 282 public long nextLong() {
- 283 // it's okay that the bottom word remains signed.
- 284 return ((long)(next(32)) << 32) + next(32);
- 285 }
- 286
- 287 /**
- 288 * Returns the next pseudorandom, uniformly distributed
- 289 * {@code boolean} value from this random number generator's
- 290 * sequence. The general contract of {@code nextBoolean} is that one
- 291 * {@code boolean} value is pseudorandomly generated and returned. The
- 292 * values {@code true} and {@code false} are produced with
- 293 * (approximately) equal probability.
- 294 *
- 295 * <p>The method {@code nextBoolean} is implemented by class {@code Random}
- 296 * as if by:
- 297 * <pre> {@code
- 298 * public boolean nextBoolean() {
- 299 * return next(1) != 0;
- 300 * }}</pre>
- 301 *
- 302 * @return the next pseudorandom, uniformly distributed
- 303 * {@code boolean} value from this random number generator's
- 304 * sequence
- 305 * @since 1.2
- 306 */
- 307 public boolean nextBoolean() {
- 308 return next(1) != 0;
- 309 }
- 310
- 311 /**
- 312 * Returns the next pseudorandom, uniformly distributed {@code float}
- 313 * value between {@code 0.0} and {@code 1.0} from this random
- 314 * number generator's sequence.
- 315 *
- 316 * <p>The general contract of {@code nextFloat} is that one
- 317 * {@code float} value, chosen (approximately) uniformly from the
- 318 * range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is
- 319 * pseudorandomly generated and returned. All 2<font
- 320 * size="-1"><sup>24</sup></font> possible {@code float} values
- 321 * of the form <i>m x </i>2<font
- 322 * size="-1"><sup>-24</sup></font>, where <i>m</i> is a positive
- 323 * integer less than 2<font size="-1"><sup>24</sup> </font>, are
- 324 * produced with (approximately) equal probability.
- 325 *
- 326 * <p>The method {@code nextFloat} is implemented by class {@code Random}
- 327 * as if by:
- 328 * <pre> {@code
- 329 * public float nextFloat() {
- 330 * return next(24) / ((float)(1 << 24));
- 331 * }}</pre>
- 332 *
- 333 * <p>The hedge "approximately" is used in the foregoing description only
- 334 * because the next method is only approximately an unbiased source of
- 335 * independently chosen bits. If it were a perfect source of randomly
- 336 * chosen bits, then the algorithm shown would choose {@code float}
- 337 * values from the stated range with perfect uniformity.<p>
- 338 * [In early versions of Java, the result was incorrectly calculated as:
- 339 * <pre> {@code
- 340 * return next(30) / ((float)(1 << 30));}</pre>
- 341 * This might seem to be equivalent, if not better, but in fact it
- 342 * introduced a slight nonuniformity because of the bias in the rounding
- 343 * of floating-point numbers: it was slightly more likely that the
- 344 * low-order bit of the significand would be 0 than that it would be 1.]
- 345 *
- 346 * @return the next pseudorandom, uniformly distributed {@code float}
- 347 * value between {@code 0.0} and {@code 1.0} from this
- 348 * random number generator's sequence
- 349 */
- 350 public float nextFloat() {
- 351 return next(24) / ((float)(1 << 24));
- 352 }
- 353
- 354 /**
- 355 * Returns the next pseudorandom, uniformly distributed
- 356 * {@code double} value between {@code 0.0} and
- 357 * {@code 1.0} from this random number generator's sequence.
- 358 *
- 359 * <p>The general contract of {@code nextDouble} is that one
- 360 * {@code double} value, chosen (approximately) uniformly from the
- 361 * range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is
- 362 * pseudorandomly generated and returned.
- 363 *
- 364 * <p>The method {@code nextDouble} is implemented by class {@code Random}
- 365 * as if by:
- 366 * <pre> {@code
- 367 * public double nextDouble() {
- 368 * return (((long)next(26) << 27) + next(27))
- 369 * / (double)(1L << 53);
- 370 * }}</pre>
- 371 *
- 372 * <p>The hedge "approximately" is used in the foregoing description only
- 373 * because the {@code next} method is only approximately an unbiased
- 374 * source of independently chosen bits. If it were a perfect source of
- 375 * randomly chosen bits, then the algorithm shown would choose
- 376 * {@code double} values from the stated range with perfect uniformity.
- 377 * <p>[In early versions of Java, the result was incorrectly calculated as:
- 378 * <pre> {@code
- 379 * return (((long)next(27) << 27) + next(27))
- 380 * / (double)(1L << 54);}</pre>
- 381 * This might seem to be equivalent, if not better, but in fact it
- 382 * introduced a large nonuniformity because of the bias in the rounding
- 383 * of floating-point numbers: it was three times as likely that the
- 384 * low-order bit of the significand would be 0 than that it would be 1!
- 385 * This nonuniformity probably doesn't matter much in practice, but we
- 386 * strive for perfection.]
- 387 *
- 388 * @return the next pseudorandom, uniformly distributed {@code double}
- 389 * value between {@code 0.0} and {@code 1.0} from this
- 390 * random number generator's sequence
- 391 * @see Math#random
- 392 */
- 393 public double nextDouble() {
- 394 return (((long)(next(26)) << 27) + next(27))
- 395 / (double)(1L << 53);
- 396 }
- 397
- 398 private double nextNextGaussian;
- 399 private boolean haveNextNextGaussian = false;
- 400
- 401 /**
- 402 * Returns the next pseudorandom, Gaussian ("normally") distributed
- 403 * {@code double} value with mean {@code 0.0} and standard
- 404 * deviation {@code 1.0} from this random number generator's sequence.
- 405 * <p>
- 406 * The general contract of {@code nextGaussian} is that one
- 407 * {@code double} value, chosen from (approximately) the usual
- 408 * normal distribution with mean {@code 0.0} and standard deviation
- 409 * {@code 1.0}, is pseudorandomly generated and returned.
- 410 *
- 411 * <p>The method {@code nextGaussian} is implemented by class
- 412 * {@code Random} as if by a threadsafe version of the following:
- 413 * <pre> {@code
- 414 * private double nextNextGaussian;
- 415 * private boolean haveNextNextGaussian = false;
- 416 *
- 417 * public double nextGaussian() {
- 418 * if (haveNextNextGaussian) {
- 419 * haveNextNextGaussian = false;
- 420 * return nextNextGaussian;
- 421 * } else {
- 422 * double v1, v2, s;
- 423 * do {
- 424 * v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0
- 425 * v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0
- 426 * s = v1 * v1 + v2 * v2;
- 427 * } while (s >= 1 || s == 0);
- 428 * double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
- 429 * nextNextGaussian = v2 * multiplier;
- 430 * haveNextNextGaussian = true;
- 431 * return v1 * multiplier;
- 432 * }
- 433 * }}</pre>
- 434 * This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and
- 435 * G. Marsaglia, as described by Donald E. Knuth in <i>The Art of
- 436 * Computer Programming</i>, Volume 3: <i>Seminumerical Algorithms</i>,
- 437 * section 3.4.1, subsection C, algorithm P. Note that it generates two
- 438 * independent values at the cost of only one call to {@code StrictMath.log}
- 439 * and one call to {@code StrictMath.sqrt}.
- 440 *
- 441 * @return the next pseudorandom, Gaussian ("normally") distributed
- 442 * {@code double} value with mean {@code 0.0} and
- 443 * standard deviation {@code 1.0} from this random number
- 444 * generator's sequence
- 445 */
- 446 synchronized public double nextGaussian() {
- 447 // See Knuth, ACP, Section 3.4.1 Algorithm C.
- 448 if (haveNextNextGaussian) {
- 449 haveNextNextGaussian = false;
- 450 return nextNextGaussian;
- 451 } else {
- 452 double v1, v2, s;
- 453 do {
- 454 v1 = 2 * nextDouble() - 1; // between -1 and 1
- 455 v2 = 2 * nextDouble() - 1; // between -1 and 1
- 456 s = v1 * v1 + v2 * v2;
- 457 } while (s >= 1 || s == 0);
- 458 double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
- 459 nextNextGaussian = v2 * multiplier;
- 460 haveNextNextGaussian = true;
- 461 return v1 * multiplier;
- 462 }
- 463 }
- 464
- 465 /**
- 466 * Serializable fields for Random.
- 467 *
- 468 * @serialField seed long;
- 469 * seed for random computations
- 470 * @serialField nextNextGaussian double;
- 471 * next Gaussian to be returned
- 472 * @serialField haveNextNextGaussian boolean
- 473 * nextNextGaussian is valid
- 474 */
- 475 private static final ObjectStreamField[] serialPersistentFields = {
- 476 new ObjectStreamField("seed", Long.TYPE),
- 477 new ObjectStreamField("nextNextGaussian", Double.TYPE),
- 478 new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
- 479 };
- 480
- 481 /**
- 482 * Reconstitute the {@code Random} instance from a stream (that is,
- 483 * deserialize it).
- 484 */
- 485 private void readObject(java.io.ObjectInputStream s)
- 486 throws java.io.IOException, ClassNotFoundException {
- 487
- 488 ObjectInputStream.GetField fields = s.readFields();
- 489
- 490 // The seed is read in as {@code long} for
- 491 // historical reasons, but it is converted to an AtomicLong.
- 492 long seedVal = (long) fields.get("seed", -1L);
- 493 if (seedVal < 0)
- 494 throw new java.io.StreamCorruptedException(
- 495 "Random: invalid seed");
- 496 resetSeed(seedVal);
- 497 nextNextGaussian = fields.get("nextNextGaussian", 0.0);
- 498 haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
- 499 }
- 500
- 501 /**
- 502 * Save the {@code Random} instance to a stream.
- 503 */
- 504 synchronized private void writeObject(ObjectOutputStream s)
- 505 throws IOException {
- 506
- 507 // set the values of the Serializable fields
- 508 ObjectOutputStream.PutField fields = s.putFields();
- 509
- 510 // The seed is serialized as a long for historical reasons.
- 511 fields.put("seed", seed.get());
- 512 fields.put("nextNextGaussian", nextNextGaussian);
- 513 fields.put("haveNextNextGaussian", haveNextNextGaussian);
- 514
- 515 // save them
- 516 s.writeFields();
- 517 }
- 518
- 519 // Support for resetting seed while deserializing
- 520 private static final Unsafe unsafe = Unsafe.getUnsafe();
- 521 private static final long seedOffset;
- 522 static {
- 523 try {
- 524 seedOffset = unsafe.objectFieldOffset
- 525 (Random.class.getDeclaredField("seed"));
- 526 } catch (Exception ex) { throw new Error(ex); }
- 527 }
- 528 private void resetSeed(long seedVal) {
- 529 unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal));
- 530 }
- 531}
