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My first initial commit with all of mine experimental changes.

Dmitry Kabak (userdima committed 13 years ago

Commit d37b88d1 parent 3f5b46618352 files · +2598093 −-8690Browse files

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1+/*
2+ * Copyright (c) 1995, 2010, Oracle and/or its affiliates. All rights reserved.
3+ * DO NOT ALTER OR REMOVE COPYRIGHT NOTICES OR THIS FILE HEADER.
4+ *
5+ * This code is free software; you can redistribute it and/or modify it
6+ * under the terms of the GNU General Public License version 2 only, as
7+ * published by the Free Software Foundation. Oracle designates this
8+ * particular file as subject to the "Classpath" exception as provided
9+ * by Oracle in the LICENSE file that accompanied this code.
10+ *
11+ * This code is distributed in the hope that it will be useful, but WITHOUT
12+ * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
13+ * FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
14+ * version 2 for more details (a copy is included in the LICENSE file that
15+ * accompanied this code).
16+ *
17+ * You should have received a copy of the GNU General Public License version
18+ * 2 along with this work; if not, write to the Free Software Foundation,
19+ * Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA.
20+ *
21+ * Please contact Oracle, 500 Oracle Parkway, Redwood Shores, CA 94065 USA
22+ * or visit www.oracle.com if you need additional information or have any
23+ * questions.
24+ */
25+
26+package java.util;
27+import java.io.*;
28+import java.util.concurrent.atomic.AtomicLong;
29+import sun.misc.Unsafe;
30+
31+/**
32+ * An instance of this class is used to generate a stream of
33+ * pseudorandom numbers. The class uses a 48-bit seed, which is
34+ * modified using a linear congruential formula. (See Donald Knuth,
35+ * <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.)
36+ * <p>
37+ * If two instances of {@code Random} are created with the same
38+ * seed, and the same sequence of method calls is made for each, they
39+ * will generate and return identical sequences of numbers. In order to
40+ * guarantee this property, particular algorithms are specified for the
41+ * class {@code Random}. Java implementations must use all the algorithms
42+ * shown here for the class {@code Random}, for the sake of absolute
43+ * portability of Java code. However, subclasses of class {@code Random}
44+ * are permitted to use other algorithms, so long as they adhere to the
45+ * general contracts for all the methods.
46+ * <p>
47+ * The algorithms implemented by class {@code Random} use a
48+ * {@code protected} utility method that on each invocation can supply
49+ * up to 32 pseudorandomly generated bits.
50+ * <p>
51+ * Many applications will find the method {@link Math#random} simpler to use.
52+ *
53+ * <p>Instances of {@code java.util.Random} are threadsafe.
54+ * However, the concurrent use of the same {@code java.util.Random}
55+ * instance across threads may encounter contention and consequent
56+ * poor performance. Consider instead using
57+ * {@link java.util.concurrent.ThreadLocalRandom} in multithreaded
58+ * designs.
59+ *
60+ * <p>Instances of {@code java.util.Random} are not cryptographically
61+ * secure. Consider instead using {@link java.security.SecureRandom} to
62+ * get a cryptographically secure pseudo-random number generator for use
63+ * by security-sensitive applications.
64+ *
65+ * @author Frank Yellin
66+ * @since 1.0
67+ */
68+public
69+class Random implements java.io.Serializable {
70+ /** use serialVersionUID from JDK 1.1 for interoperability */
71+ static final long serialVersionUID = 3905348978240129619L;
72+
73+ /**
74+ * The internal state associated with this pseudorandom number generator.
75+ * (The specs for the methods in this class describe the ongoing
76+ * computation of this value.)
77+ */
78+ private final AtomicLong seed;
79+
80+ private static final long multiplier = 0x5DEECE66DL;
81+ private static final long addend = 0xBL;
82+ private static final long mask = (1L << 48) - 1;
83+
84+ /**
85+ * Creates a new random number generator. This constructor sets
86+ * the seed of the random number generator to a value very likely
87+ * to be distinct from any other invocation of this constructor.
88+ */
89+ public Random() {
90+ this(seedUniquifier() ^ System.nanoTime());
91+ }
92+
93+ private static long seedUniquifier() {
94+ // L'Ecuyer, "Tables of Linear Congruential Generators of
95+ // Different Sizes and Good Lattice Structure", 1999
96+ for (;;) {
97+ long current = seedUniquifier.get();
98+ long next = current * 181783497276652981L;
99+ if (seedUniquifier.compareAndSet(current, next))
100+ return next;
101+ }
102+ }
103+
104+ private static final AtomicLong seedUniquifier
105+ = new AtomicLong(8682522807148012L);
106+
107+ /**
108+ * Creates a new random number generator using a single {@code long} seed.
109+ * The seed is the initial value of the internal state of the pseudorandom
110+ * number generator which is maintained by method {@link #next}.
111+ *
112+ * <p>The invocation {@code new Random(seed)} is equivalent to:
113+ * <pre> {@code
114+ * Random rnd = new Random();
115+ * rnd.setSeed(seed);}</pre>
116+ *
117+ * @param seed the initial seed
118+ * @see #setSeed(long)
119+ */
120+ public Random(long seed) {
121+ this.seed = new AtomicLong(initialScramble(seed));
122+ }
123+
124+ private static long initialScramble(long seed) {
125+ return (seed ^ multiplier) & mask;
126+ }
127+
128+ /**
129+ * Sets the seed of this random number generator using a single
130+ * {@code long} seed. The general contract of {@code setSeed} is
131+ * that it alters the state of this random number generator object
132+ * so as to be in exactly the same state as if it had just been
133+ * created with the argument {@code seed} as a seed. The method
134+ * {@code setSeed} is implemented by class {@code Random} by
135+ * atomically updating the seed to
136+ * <pre>{@code (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)}</pre>
137+ * and clearing the {@code haveNextNextGaussian} flag used by {@link
138+ * #nextGaussian}.
139+ *
140+ * <p>The implementation of {@code setSeed} by class {@code Random}
141+ * happens to use only 48 bits of the given seed. In general, however,
142+ * an overriding method may use all 64 bits of the {@code long}
143+ * argument as a seed value.
144+ *
145+ * @param seed the initial seed
146+ */
147+ synchronized public void setSeed(long seed) {
148+ this.seed.set(initialScramble(seed));
149+ haveNextNextGaussian = false;
150+ }
151+
152+ /**
153+ * Generates the next pseudorandom number. Subclasses should
154+ * override this, as this is used by all other methods.
155+ *
156+ * <p>The general contract of {@code next} is that it returns an
157+ * {@code int} value and if the argument {@code bits} is between
158+ * {@code 1} and {@code 32} (inclusive), then that many low-order
159+ * bits of the returned value will be (approximately) independently
160+ * chosen bit values, each of which is (approximately) equally
161+ * likely to be {@code 0} or {@code 1}. The method {@code next} is
162+ * implemented by class {@code Random} by atomically updating the seed to
163+ * <pre>{@code (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)}</pre>
164+ * and returning
165+ * <pre>{@code (int)(seed >>> (48 - bits))}.</pre>
166+ *
167+ * This is a linear congruential pseudorandom number generator, as
168+ * defined by D. H. Lehmer and described by Donald E. Knuth in
169+ * <i>The Art of Computer Programming,</i> Volume 3:
170+ * <i>Seminumerical Algorithms</i>, section 3.2.1.
171+ *
172+ * @param bits random bits
173+ * @return the next pseudorandom value from this random number
174+ * generator's sequence
175+ * @since 1.1
176+ */
177+ protected int next(int bits) {
178+ long oldseed, nextseed;
179+ AtomicLong seed = this.seed;
180+ do {
181+ oldseed = seed.get();
182+ nextseed = (oldseed * multiplier + addend) & mask;
183+ } while (!seed.compareAndSet(oldseed, nextseed));
184+ return (int)(nextseed >>> (48 - bits));
185+ }
186+
187+ /**
188+ * Generates random bytes and places them into a user-supplied
189+ * byte array. The number of random bytes produced is equal to
190+ * the length of the byte array.
191+ *
192+ * <p>The method {@code nextBytes} is implemented by class {@code Random}
193+ * as if by:
194+ * <pre> {@code
195+ * public void nextBytes(byte[] bytes) {
196+ * for (int i = 0; i < bytes.length; )
197+ * for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4);
198+ * n-- > 0; rnd >>= 8)
199+ * bytes[i++] = (byte)rnd;
200+ * }}</pre>
201+ *
202+ * @param bytes the byte array to fill with random bytes
203+ * @throws NullPointerException if the byte array is null
204+ * @since 1.1
205+ */
206+ public void nextBytes(byte[] bytes) {
207+ for (int i = 0, len = bytes.length; i < len; )
208+ for (int rnd = nextInt(),
209+ n = Math.min(len - i, Integer.SIZE/Byte.SIZE);
210+ n-- > 0; rnd >>= Byte.SIZE)
211+ bytes[i++] = (byte)rnd;
212+ }
213+
214+ /**
215+ * Returns the next pseudorandom, uniformly distributed {@code int}
216+ * value from this random number generator's sequence. The general
217+ * contract of {@code nextInt} is that one {@code int} value is
218+ * pseudorandomly generated and returned. All 2<font size="-1"><sup>32
219+ * </sup></font> possible {@code int} values are produced with
220+ * (approximately) equal probability.
221+ *
222+ * <p>The method {@code nextInt} is implemented by class {@code Random}
223+ * as if by:
224+ * <pre> {@code
225+ * public int nextInt() {
226+ * return next(32);
227+ * }}</pre>
228+ *
229+ * @return the next pseudorandom, uniformly distributed {@code int}
230+ * value from this random number generator's sequence
231+ */
232+ public int nextInt() {
233+ return next(32);
234+ }
235+
236+ /**
237+ * Returns a pseudorandom, uniformly distributed {@code int} value
238+ * between 0 (inclusive) and the specified value (exclusive), drawn from
239+ * this random number generator's sequence. The general contract of
240+ * {@code nextInt} is that one {@code int} value in the specified range
241+ * is pseudorandomly generated and returned. All {@code n} possible
242+ * {@code int} values are produced with (approximately) equal
243+ * probability. The method {@code nextInt(int n)} is implemented by
244+ * class {@code Random} as if by:
245+ * <pre> {@code
246+ * public int nextInt(int n) {
247+ * if (n <= 0)
248+ * throw new IllegalArgumentException("n must be positive");
249+ *
250+ * if ((n & -n) == n) // i.e., n is a power of 2
251+ * return (int)((n * (long)next(31)) >> 31);
252+ *
253+ * int bits, val;
254+ * do {
255+ * bits = next(31);
256+ * val = bits % n;
257+ * } while (bits - val + (n-1) < 0);
258+ * return val;
259+ * }}</pre>
260+ *
261+ * <p>The hedge "approximately" is used in the foregoing description only
262+ * because the next method is only approximately an unbiased source of
263+ * independently chosen bits. If it were a perfect source of randomly
264+ * chosen bits, then the algorithm shown would choose {@code int}
265+ * values from the stated range with perfect uniformity.
266+ * <p>
267+ * The algorithm is slightly tricky. It rejects values that would result
268+ * in an uneven distribution (due to the fact that 2^31 is not divisible
269+ * by n). The probability of a value being rejected depends on n. The
270+ * worst case is n=2^30+1, for which the probability of a reject is 1/2,
271+ * and the expected number of iterations before the loop terminates is 2.
272+ * <p>
273+ * The algorithm treats the case where n is a power of two specially: it
274+ * returns the correct number of high-order bits from the underlying
275+ * pseudo-random number generator. In the absence of special treatment,
276+ * the correct number of <i>low-order</i> bits would be returned. Linear
277+ * congruential pseudo-random number generators such as the one
278+ * implemented by this class are known to have short periods in the
279+ * sequence of values of their low-order bits. Thus, this special case
280+ * greatly increases the length of the sequence of values returned by
281+ * successive calls to this method if n is a small power of two.
282+ *
283+ * @param n the bound on the random number to be returned. Must be
284+ * positive.
285+ * @return the next pseudorandom, uniformly distributed {@code int}
286+ * value between {@code 0} (inclusive) and {@code n} (exclusive)
287+ * from this random number generator's sequence
288+ * @throws IllegalArgumentException if n is not positive
289+ * @since 1.2
290+ */
291+
292+ public int nextInt(int n) {
293+ if (n <= 0)
294+ throw new IllegalArgumentException("n must be positive");
295+
296+ if ((n & -n) == n) // i.e., n is a power of 2
297+ return (int)((n * (long)next(31)) >> 31);
298+
299+ int bits, val;
300+ do {
301+ bits = next(31);
302+ val = bits % n;
303+ } while (bits - val + (n-1) < 0);
304+ return val;
305+ }
306+
307+ /**
308+ * Returns the next pseudorandom, uniformly distributed {@code long}
309+ * value from this random number generator's sequence. The general
310+ * contract of {@code nextLong} is that one {@code long} value is
311+ * pseudorandomly generated and returned.
312+ *
313+ * <p>The method {@code nextLong} is implemented by class {@code Random}
314+ * as if by:
315+ * <pre> {@code
316+ * public long nextLong() {
317+ * return ((long)next(32) << 32) + next(32);
318+ * }}</pre>
319+ *
320+ * Because class {@code Random} uses a seed with only 48 bits,
321+ * this algorithm will not return all possible {@code long} values.
322+ *
323+ * @return the next pseudorandom, uniformly distributed {@code long}
324+ * value from this random number generator's sequence
325+ */
326+ public long nextLong() {
327+ // it's okay that the bottom word remains signed.
328+ return ((long)(next(32)) << 32) + next(32);
329+ }
330+
331+ /**
332+ * Returns the next pseudorandom, uniformly distributed
333+ * {@code boolean} value from this random number generator's
334+ * sequence. The general contract of {@code nextBoolean} is that one
335+ * {@code boolean} value is pseudorandomly generated and returned. The
336+ * values {@code true} and {@code false} are produced with
337+ * (approximately) equal probability.
338+ *
339+ * <p>The method {@code nextBoolean} is implemented by class {@code Random}
340+ * as if by:
341+ * <pre> {@code
342+ * public boolean nextBoolean() {
343+ * return next(1) != 0;
344+ * }}</pre>
345+ *
346+ * @return the next pseudorandom, uniformly distributed
347+ * {@code boolean} value from this random number generator's
348+ * sequence
349+ * @since 1.2
350+ */
351+ public boolean nextBoolean() {
352+ return next(1) != 0;
353+ }
354+
355+ /**
356+ * Returns the next pseudorandom, uniformly distributed {@code float}
357+ * value between {@code 0.0} and {@code 1.0} from this random
358+ * number generator's sequence.
359+ *
360+ * <p>The general contract of {@code nextFloat} is that one
361+ * {@code float} value, chosen (approximately) uniformly from the
362+ * range {@code 0.0f} (inclusive) to {@code 1.0f} (exclusive), is
363+ * pseudorandomly generated and returned. All 2<font
364+ * size="-1"><sup>24</sup></font> possible {@code float} values
365+ * of the form <i>m&nbsp;x&nbsp</i>2<font
366+ * size="-1"><sup>-24</sup></font>, where <i>m</i> is a positive
367+ * integer less than 2<font size="-1"><sup>24</sup> </font>, are
368+ * produced with (approximately) equal probability.
369+ *
370+ * <p>The method {@code nextFloat} is implemented by class {@code Random}
371+ * as if by:
372+ * <pre> {@code
373+ * public float nextFloat() {
374+ * return next(24) / ((float)(1 << 24));
375+ * }}</pre>
376+ *
377+ * <p>The hedge "approximately" is used in the foregoing description only
378+ * because the next method is only approximately an unbiased source of
379+ * independently chosen bits. If it were a perfect source of randomly
380+ * chosen bits, then the algorithm shown would choose {@code float}
381+ * values from the stated range with perfect uniformity.<p>
382+ * [In early versions of Java, the result was incorrectly calculated as:
383+ * <pre> {@code
384+ * return next(30) / ((float)(1 << 30));}</pre>
385+ * This might seem to be equivalent, if not better, but in fact it
386+ * introduced a slight nonuniformity because of the bias in the rounding
387+ * of floating-point numbers: it was slightly more likely that the
388+ * low-order bit of the significand would be 0 than that it would be 1.]
389+ *
390+ * @return the next pseudorandom, uniformly distributed {@code float}
391+ * value between {@code 0.0} and {@code 1.0} from this
392+ * random number generator's sequence
393+ */
394+ public float nextFloat() {
395+ return next(24) / ((float)(1 << 24));
396+ }
397+
398+ /**
399+ * Returns the next pseudorandom, uniformly distributed
400+ * {@code double} value between {@code 0.0} and
401+ * {@code 1.0} from this random number generator's sequence.
402+ *
403+ * <p>The general contract of {@code nextDouble} is that one
404+ * {@code double} value, chosen (approximately) uniformly from the
405+ * range {@code 0.0d} (inclusive) to {@code 1.0d} (exclusive), is
406+ * pseudorandomly generated and returned.
407+ *
408+ * <p>The method {@code nextDouble} is implemented by class {@code Random}
409+ * as if by:
410+ * <pre> {@code
411+ * public double nextDouble() {
412+ * return (((long)next(26) << 27) + next(27))
413+ * / (double)(1L << 53);
414+ * }}</pre>
415+ *
416+ * <p>The hedge "approximately" is used in the foregoing description only
417+ * because the {@code next} method is only approximately an unbiased
418+ * source of independently chosen bits. If it were a perfect source of
419+ * randomly chosen bits, then the algorithm shown would choose
420+ * {@code double} values from the stated range with perfect uniformity.
421+ * <p>[In early versions of Java, the result was incorrectly calculated as:
422+ * <pre> {@code
423+ * return (((long)next(27) << 27) + next(27))
424+ * / (double)(1L << 54);}</pre>
425+ * This might seem to be equivalent, if not better, but in fact it
426+ * introduced a large nonuniformity because of the bias in the rounding
427+ * of floating-point numbers: it was three times as likely that the
428+ * low-order bit of the significand would be 0 than that it would be 1!
429+ * This nonuniformity probably doesn't matter much in practice, but we
430+ * strive for perfection.]
431+ *
432+ * @return the next pseudorandom, uniformly distributed {@code double}
433+ * value between {@code 0.0} and {@code 1.0} from this
434+ * random number generator's sequence
435+ * @see Math#random
436+ */
437+ public double nextDouble() {
438+ return (((long)(next(26)) << 27) + next(27))
439+ / (double)(1L << 53);
440+ }
441+
442+ private double nextNextGaussian;
443+ private boolean haveNextNextGaussian = false;
444+
445+ /**
446+ * Returns the next pseudorandom, Gaussian ("normally") distributed
447+ * {@code double} value with mean {@code 0.0} and standard
448+ * deviation {@code 1.0} from this random number generator's sequence.
449+ * <p>
450+ * The general contract of {@code nextGaussian} is that one
451+ * {@code double} value, chosen from (approximately) the usual
452+ * normal distribution with mean {@code 0.0} and standard deviation
453+ * {@code 1.0}, is pseudorandomly generated and returned.
454+ *
455+ * <p>The method {@code nextGaussian} is implemented by class
456+ * {@code Random} as if by a threadsafe version of the following:
457+ * <pre> {@code
458+ * private double nextNextGaussian;
459+ * private boolean haveNextNextGaussian = false;
460+ *
461+ * public double nextGaussian() {
462+ * if (haveNextNextGaussian) {
463+ * haveNextNextGaussian = false;
464+ * return nextNextGaussian;
465+ * } else {
466+ * double v1, v2, s;
467+ * do {
468+ * v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0
469+ * v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0
470+ * s = v1 * v1 + v2 * v2;
471+ * } while (s >= 1 || s == 0);
472+ * double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
473+ * nextNextGaussian = v2 * multiplier;
474+ * haveNextNextGaussian = true;
475+ * return v1 * multiplier;
476+ * }
477+ * }}</pre>
478+ * This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and
479+ * G. Marsaglia, as described by Donald E. Knuth in <i>The Art of
480+ * Computer Programming</i>, Volume 3: <i>Seminumerical Algorithms</i>,
481+ * section 3.4.1, subsection C, algorithm P. Note that it generates two
482+ * independent values at the cost of only one call to {@code StrictMath.log}
483+ * and one call to {@code StrictMath.sqrt}.
484+ *
485+ * @return the next pseudorandom, Gaussian ("normally") distributed
486+ * {@code double} value with mean {@code 0.0} and
487+ * standard deviation {@code 1.0} from this random number
488+ * generator's sequence
489+ */
490+ synchronized public double nextGaussian() {
491+ // See Knuth, ACP, Section 3.4.1 Algorithm C.
492+ if (haveNextNextGaussian) {
493+ haveNextNextGaussian = false;
494+ return nextNextGaussian;
495+ } else {
496+ double v1, v2, s;
497+ do {
498+ v1 = 2 * nextDouble() - 1; // between -1 and 1
499+ v2 = 2 * nextDouble() - 1; // between -1 and 1
500+ s = v1 * v1 + v2 * v2;
501+ } while (s >= 1 || s == 0);
502+ double multiplier = StrictMath.sqrt(-2 * StrictMath.log(s)/s);
503+ nextNextGaussian = v2 * multiplier;
504+ haveNextNextGaussian = true;
505+ return v1 * multiplier;
506+ }
507+ }
508+
509+ /**
510+ * Serializable fields for Random.
511+ *
512+ * @serialField seed long
513+ * seed for random computations
514+ * @serialField nextNextGaussian double
515+ * next Gaussian to be returned
516+ * @serialField haveNextNextGaussian boolean
517+ * nextNextGaussian is valid
518+ */
519+ private static final ObjectStreamField[] serialPersistentFields = {
520+ new ObjectStreamField("seed", Long.TYPE),
521+ new ObjectStreamField("nextNextGaussian", Double.TYPE),
522+ new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
523+ };
524+
525+ /**
526+ * Reconstitute the {@code Random} instance from a stream (that is,
527+ * deserialize it).
528+ */
529+ private void readObject(java.io.ObjectInputStream s)
530+ throws java.io.IOException, ClassNotFoundException {
531+
532+ ObjectInputStream.GetField fields = s.readFields();
533+
534+ // The seed is read in as {@code long} for
535+ // historical reasons, but it is converted to an AtomicLong.
536+ long seedVal = fields.get("seed", -1L);
537+ if (seedVal < 0)
538+ throw new java.io.StreamCorruptedException(
539+ "Random: invalid seed");
540+ resetSeed(seedVal);
541+ nextNextGaussian = fields.get("nextNextGaussian", 0.0);
542+ haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
543+ }
544+
545+ /**
546+ * Save the {@code Random} instance to a stream.
547+ */
548+ synchronized private void writeObject(ObjectOutputStream s)
549+ throws IOException {
550+
551+ // set the values of the Serializable fields
552+ ObjectOutputStream.PutField fields = s.putFields();
553+
554+ // The seed is serialized as a long for historical reasons.
555+ fields.put("seed", seed.get());
556+ fields.put("nextNextGaussian", nextNextGaussian);
557+ fields.put("haveNextNextGaussian", haveNextNextGaussian);
558+
559+ // save them
560+ s.writeFields();
561+ }
562+
563+ // Support for resetting seed while deserializing
564+ private static final Unsafe unsafe = Unsafe.getUnsafe();
565+ private static final long seedOffset;
566+ static {
567+ try {
568+ seedOffset = unsafe.objectFieldOffset
569+ (Random.class.getDeclaredField("seed"));
570+ } catch (Exception ex) { throw new Error(ex); }
571+ }
572+ private void resetSeed(long seedVal) {
573+ unsafe.putObjectVolatile(this, seedOffset, new AtomicLong(seedVal));
574+ }
575+}