1 /* Random.java -- a pseudo-random number generator
2 Copyright (C) 1998, 1999, 2000, 2001, 2002 Free Software Foundation, Inc.
4 This file is part of GNU Classpath.
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7 it under the terms of the GNU General Public License as published by
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12 WITHOUT ANY WARRANTY; without even the implied warranty of
13 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
14 General Public License for more details.
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18 Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
21 Linking this library statically or dynamically with other modules is
22 making a combined work based on this library. Thus, the terms and
23 conditions of the GNU General Public License cover the whole
26 As a special exception, the copyright holders of this library give you
27 permission to link this library with independent modules to produce an
28 executable, regardless of the license terms of these independent
29 modules, and to copy and distribute the resulting executable under
30 terms of your choice, provided that you also meet, for each linked
31 independent module, the terms and conditions of the license of that
32 module. An independent module is a module which is not derived from
33 or based on this library. If you modify this library, you may extend
34 this exception to your version of the library, but you are not
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36 exception statement from your version. */
40 * This class generates pseudorandom numbers. It uses the same
41 * algorithm as the original JDK-class, so that your programs behave
42 * exactly the same way, if started with the same seed.
44 * The algorithm is described in <em>The Art of Computer Programming,
45 * Volume 2</em> by Donald Knuth in Section 3.2.1. It is a 48-bit seed,
46 * linear congruential formula.
48 * If two instances of this class are created with the same seed and
49 * the same calls to these classes are made, they behave exactly the
50 * same way. This should be even true for foreign implementations
51 * (like this), so every port must use the same algorithm as described
54 * If you want to implement your own pseudorandom algorithm, you
55 * should extend this class and overload the <code>next()</code> and
56 * <code>setSeed(long)</code> method. In that case the above
57 * paragraph doesn't apply to you.
59 * This class shouldn't be used for security sensitive purposes (like
60 * generating passwords or encryption keys. See <code>SecureRandom</code>
61 * in package <code>java.security</code> for this purpose.
63 * For simple random doubles between 0.0 and 1.0, you may consider using
64 * Math.random instead.
66 * @see java.security.SecureRandom
68 * @author Jochen Hoenicke
69 * @author Eric Blake (ebb9@email.byu.edu)
70 * @status updated to 1.4
75 * True if the next nextGaussian is available. This is used by
76 * nextGaussian, which generates two gaussian numbers by one call,
77 * and returns the second on the second call.
79 * @serial whether nextNextGaussian is available
80 * @see #nextGaussian()
81 * @see #nextNextGaussian
83 private boolean haveNextNextGaussian;
86 * The next nextGaussian, when available. This is used by nextGaussian,
87 * which generates two gaussian numbers by one call, and returns the
88 * second on the second call.
90 * @serial the second gaussian of a pair
91 * @see #nextGaussian()
92 * @see #haveNextNextGaussian
94 private double nextNextGaussian;
97 * The seed. This is the number set by setSeed and which is used
100 * @serial the internal state of this generator
107 * Creates a new pseudorandom number generator. The seed is initialized
108 * to the current time, as if by
109 * <code>setSeed(System.currentTimeMillis());</code>.
111 * @see System#currentTimeMillis()
114 setSeed(System.currentTimeMillis());
118 * Creates a new pseudorandom number generator, starting with the
119 * specified seed, using <code>setSeed(seed);</code>.
121 * @param seed the initial seed
123 public Random(long seed) {
128 * Sets the seed for this pseudorandom number generator. As described
129 * above, two instances of the same random class, starting with the
130 * same seed, should produce the same results, if the same methods
131 * are called. The implementation for java.util.Random is:
133 <pre>public synchronized void setSeed(long seed)
135 this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
136 haveNextNextGaussian = false;
139 * @param seed the new seed
141 public synchronized void setSeed(long seed) {
142 this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
143 haveNextNextGaussian = false;
147 * Generates the next pseudorandom number. This returns
148 * an int value whose <code>bits</code> low order bits are
149 * independent chosen random bits (0 and 1 are equally likely).
150 * The implementation for java.util.Random is:
152 <pre>protected synchronized int next(int bits)
154 seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
155 return (int) (seed >>> (48 - bits));
158 * @param bits the number of random bits to generate, in the range 1..32
159 * @return the next pseudorandom value
162 protected synchronized int next(int bits) {
163 seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
164 return (int) (seed >>> (48 - bits));
168 * Fills an array of bytes with random numbers. All possible values
169 * are (approximately) equally likely.
170 * The JDK documentation gives no implementation, but it seems to be:
172 <pre>public void nextBytes(byte[] bytes)
174 for (int i = 0; i < bytes.length; i += 4)
176 int random = next(32);
177 for (int j = 0; i + j < bytes.length && j < 4; j++)
179 bytes[i+j] = (byte) (random & 0xff)
185 * @param bytes the byte array that should be filled
186 * @throws NullPointerException if bytes is null
189 public void nextBytes(byte[] bytes) {
191 // Do a little bit unrolling of the above algorithm.
192 int max = bytes.length & ~0x3;
193 for (int i = 0; i < max; i += 4)
196 bytes[i] = (byte) random;
197 bytes[i + 1] = (byte) (random >> 8);
198 bytes[i + 2] = (byte) (random >> 16);
199 bytes[i + 3] = (byte) (random >> 24);
201 if (max < bytes.length){
203 for (int j = max; j < bytes.length; j++)
205 bytes[j] = (byte) random;
212 * Generates the next pseudorandom number. This returns
213 * an int value whose 32 bits are independent chosen random bits
214 * (0 and 1 are equally likely). The implementation for
215 * java.util.Random is:
217 <pre>public int nextInt()
222 * @return the next pseudorandom value
224 public int nextInt() {
229 * Generates the next pseudorandom number. This returns
230 * a value between 0(inclusive) and <code>n</code>(exclusive), and
231 * each value has the same likelihodd (1/<code>n</code>).
232 * (0 and 1 are equally likely). The implementation for
233 * java.util.Random is:
236 public int nextInt(int n)
239 throw new IllegalArgumentException("n must be positive");
241 if ((n & -n) == n) // i.e., n is a power of 2
242 return (int)((n * (long) next(31)) >> 31);
250 while(bits - val + (n-1) < 0);
255 * <p>This algorithm would return every value with exactly the same
256 * probability, if the next()-method would be a perfect random number
259 * The loop at the bottom only accepts a value, if the random
260 * number was between 0 and the highest number less then 1<<31,
261 * which is divisible by n. The probability for this is high for small
262 * n, and the worst case is 1/2 (for n=(1<<30)+1).
264 * The special treatment for n = power of 2, selects the high bits of
265 * the random number (the loop at the bottom would select the low order
266 * bits). This is done, because the low order bits of linear congruential
267 * number generators (like the one used in this class) are known to be
268 * ``less random'' than the high order bits.
270 * @param n the upper bound
271 * @throws IllegalArgumentException if the given upper bound is negative
272 * @return the next pseudorandom value
275 public int nextInt(int n) {
277 System.printString("ERROR: n must be positive\n");
278 if ((n & -n) == n) // i.e., n is a power of 2
279 return (int) ((n * (long) next(31)) >> 31);
284 } while (bits - val + (n - 1) < 0);
289 * Generates the next pseudorandom long number. All bits of this
290 * long are independently chosen and 0 and 1 have equal likelihood.
291 * The implementation for java.util.Random is:
293 <pre>public long nextLong()
295 return ((long) next(32) << 32) + next(32);
298 * @return the next pseudorandom value
300 public long nextLong() {
301 return ((long) next(32) << 32) + next(32);
305 * Generates the next pseudorandom boolean. True and false have
306 * the same probability. The implementation is:
308 <pre>public boolean nextBoolean()
313 * @return the next pseudorandom boolean
316 public boolean nextBoolean() {
321 * Generates the next pseudorandom float uniformly distributed
322 * between 0.0f (inclusive) and 1.0f (exclusive). The
323 * implementation is as follows.
325 <pre>public float nextFloat()
327 return next(24) / ((float)(1 << 24));
330 * @return the next pseudorandom float
332 public float nextFloat() {
333 return next(24) / (float) (1 << 24);
337 * Generates the next pseudorandom double uniformly distributed
338 * between 0.0 (inclusive) and 1.0 (exclusive). The
339 * implementation is as follows.
341 <pre>public double nextDouble()
343 return (((long) next(26) << 27) + next(27)) / (double)(1L << 53);
346 * @return the next pseudorandom double
348 public double nextDouble() {
349 return (((long) next(26) << 27) + next(27)) / (double) (1L << 53);
353 * Generates the next pseudorandom, Gaussian (normally) distributed
354 * double value, with mean 0.0 and standard deviation 1.0.
355 * The algorithm is as follows.
357 <pre>public synchronized double nextGaussian()
359 if (haveNextNextGaussian)
361 haveNextNextGaussian = false;
362 return nextNextGaussian;
369 v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0
370 v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0
371 s = v1 * v1 + v2 * v2;
375 double norm = Math.sqrt(-2 * Math.log(s) / s);
376 nextNextGaussian = v2 * norm;
377 haveNextNextGaussian = true;
382 * <p>This is described in section 3.4.1 of <em>The Art of Computer
383 * Programming, Volume 2</em> by Donald Knuth.
385 * @return the next pseudorandom Gaussian distributed double
387 public synchronized double nextGaussian() {
388 if (haveNextNextGaussian){
389 haveNextNextGaussian = false;
390 return nextNextGaussian;
394 v1 = 2 * nextDouble() - 1; // Between -1.0 and 1.0.
395 v2 = 2 * nextDouble() - 1; // Between -1.0 and 1.0.
396 s = v1 * v1 + v2 * v2;
398 double norm = Math.sqrt(-2 * Math.log(s) / s);
399 nextNextGaussian = v2 * norm;
400 haveNextNextGaussian = true;