001 /* Random.java -- a pseudo-random number generator
002 Copyright (C) 1998, 1999, 2000, 2001, 2002 Free Software Foundation, Inc.
003
004 This file is part of GNU Classpath.
005
006 GNU Classpath is free software; you can redistribute it and/or modify
007 it under the terms of the GNU General Public License as published by
008 the Free Software Foundation; either version 2, or (at your option)
009 any later version.
010
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012 WITHOUT ANY WARRANTY; without even the implied warranty of
013 MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
014 General Public License for more details.
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019 02110-1301 USA.
020
021 Linking this library statically or dynamically with other modules is
022 making a combined work based on this library. Thus, the terms and
023 conditions of the GNU General Public License cover the whole
024 combination.
025
026 As a special exception, the copyright holders of this library give you
027 permission to link this library with independent modules to produce an
028 executable, regardless of the license terms of these independent
029 modules, and to copy and distribute the resulting executable under
030 terms of your choice, provided that you also meet, for each linked
031 independent module, the terms and conditions of the license of that
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033 or based on this library. If you modify this library, you may extend
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035 obligated to do so. If you do not wish to do so, delete this
036 exception statement from your version. */
037
038
039 package java.util;
040
041 import java.io.Serializable;
042
043 /**
044 * This class generates pseudorandom numbers. It uses the same
045 * algorithm as the original JDK-class, so that your programs behave
046 * exactly the same way, if started with the same seed.
047 *
048 * The algorithm is described in <em>The Art of Computer Programming,
049 * Volume 2</em> by Donald Knuth in Section 3.2.1. It is a 48-bit seed,
050 * linear congruential formula.
051 *
052 * If two instances of this class are created with the same seed and
053 * the same calls to these classes are made, they behave exactly the
054 * same way. This should be even true for foreign implementations
055 * (like this), so every port must use the same algorithm as described
056 * here.
057 *
058 * If you want to implement your own pseudorandom algorithm, you
059 * should extend this class and overload the <code>next()</code> and
060 * <code>setSeed(long)</code> method. In that case the above
061 * paragraph doesn't apply to you.
062 *
063 * This class shouldn't be used for security sensitive purposes (like
064 * generating passwords or encryption keys. See <code>SecureRandom</code>
065 * in package <code>java.security</code> for this purpose.
066 *
067 * For simple random doubles between 0.0 and 1.0, you may consider using
068 * Math.random instead.
069 *
070 * @see java.security.SecureRandom
071 * @see Math#random()
072 * @author Jochen Hoenicke
073 * @author Eric Blake (ebb9@email.byu.edu)
074 * @status updated to 1.4
075 */
076 public class Random implements Serializable
077 {
078 /**
079 * True if the next nextGaussian is available. This is used by
080 * nextGaussian, which generates two gaussian numbers by one call,
081 * and returns the second on the second call.
082 *
083 * @serial whether nextNextGaussian is available
084 * @see #nextGaussian()
085 * @see #nextNextGaussian
086 */
087 private boolean haveNextNextGaussian;
088
089 /**
090 * The next nextGaussian, when available. This is used by nextGaussian,
091 * which generates two gaussian numbers by one call, and returns the
092 * second on the second call.
093 *
094 * @serial the second gaussian of a pair
095 * @see #nextGaussian()
096 * @see #haveNextNextGaussian
097 */
098 private double nextNextGaussian;
099
100 /**
101 * The seed. This is the number set by setSeed and which is used
102 * in next.
103 *
104 * @serial the internal state of this generator
105 * @see #next(int)
106 */
107 private long seed;
108
109 /**
110 * Compatible with JDK 1.0+.
111 */
112 private static final long serialVersionUID = 3905348978240129619L;
113
114 /**
115 * Creates a new pseudorandom number generator. The seed is initialized
116 * to the current time, as if by
117 * <code>setSeed(System.currentTimeMillis());</code>.
118 *
119 * @see System#currentTimeMillis()
120 */
121 public Random()
122 {
123 this(System.currentTimeMillis());
124 }
125
126 /**
127 * Creates a new pseudorandom number generator, starting with the
128 * specified seed, using <code>setSeed(seed);</code>.
129 *
130 * @param seed the initial seed
131 */
132 public Random(long seed)
133 {
134 setSeed(seed);
135 }
136
137 /**
138 * Sets the seed for this pseudorandom number generator. As described
139 * above, two instances of the same random class, starting with the
140 * same seed, should produce the same results, if the same methods
141 * are called. The implementation for java.util.Random is:
142 *
143 <pre>public synchronized void setSeed(long seed)
144 {
145 this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
146 haveNextNextGaussian = false;
147 }</pre>
148 *
149 * @param seed the new seed
150 */
151 public synchronized void setSeed(long seed)
152 {
153 this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
154 haveNextNextGaussian = false;
155 }
156
157 /**
158 * Generates the next pseudorandom number. This returns
159 * an int value whose <code>bits</code> low order bits are
160 * independent chosen random bits (0 and 1 are equally likely).
161 * The implementation for java.util.Random is:
162 *
163 <pre>protected synchronized int next(int bits)
164 {
165 seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
166 return (int) (seed >>> (48 - bits));
167 }</pre>
168 *
169 * @param bits the number of random bits to generate, in the range 1..32
170 * @return the next pseudorandom value
171 * @since 1.1
172 */
173 protected synchronized int next(int bits)
174 {
175 seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
176 return (int) (seed >>> (48 - bits));
177 }
178
179 /**
180 * Fills an array of bytes with random numbers. All possible values
181 * are (approximately) equally likely.
182 * The JDK documentation gives no implementation, but it seems to be:
183 *
184 <pre>public void nextBytes(byte[] bytes)
185 {
186 for (int i = 0; i < bytes.length; i += 4)
187 {
188 int random = next(32);
189 for (int j = 0; i + j < bytes.length && j < 4; j++)
190 {
191 bytes[i+j] = (byte) (random & 0xff)
192 random >>= 8;
193 }
194 }
195 }</pre>
196 *
197 * @param bytes the byte array that should be filled
198 * @throws NullPointerException if bytes is null
199 * @since 1.1
200 */
201 public void nextBytes(byte[] bytes)
202 {
203 int random;
204 // Do a little bit unrolling of the above algorithm.
205 int max = bytes.length & ~0x3;
206 for (int i = 0; i < max; i += 4)
207 {
208 random = next(32);
209 bytes[i] = (byte) random;
210 bytes[i + 1] = (byte) (random >> 8);
211 bytes[i + 2] = (byte) (random >> 16);
212 bytes[i + 3] = (byte) (random >> 24);
213 }
214 if (max < bytes.length)
215 {
216 random = next(32);
217 for (int j = max; j < bytes.length; j++)
218 {
219 bytes[j] = (byte) random;
220 random >>= 8;
221 }
222 }
223 }
224
225 /**
226 * Generates the next pseudorandom number. This returns
227 * an int value whose 32 bits are independent chosen random bits
228 * (0 and 1 are equally likely). The implementation for
229 * java.util.Random is:
230 *
231 <pre>public int nextInt()
232 {
233 return next(32);
234 }</pre>
235 *
236 * @return the next pseudorandom value
237 */
238 public int nextInt()
239 {
240 return next(32);
241 }
242
243 /**
244 * Generates the next pseudorandom number. This returns
245 * a value between 0(inclusive) and <code>n</code>(exclusive), and
246 * each value has the same likelihodd (1/<code>n</code>).
247 * (0 and 1 are equally likely). The implementation for
248 * java.util.Random is:
249 *
250 <pre>
251 public int nextInt(int n)
252 {
253 if (n <= 0)
254 throw new IllegalArgumentException("n must be positive");
255
256 if ((n & -n) == n) // i.e., n is a power of 2
257 return (int)((n * (long) next(31)) >> 31);
258
259 int bits, val;
260 do
261 {
262 bits = next(31);
263 val = bits % n;
264 }
265 while(bits - val + (n-1) < 0);
266
267 return val;
268 }</pre>
269 *
270 * <p>This algorithm would return every value with exactly the same
271 * probability, if the next()-method would be a perfect random number
272 * generator.
273 *
274 * The loop at the bottom only accepts a value, if the random
275 * number was between 0 and the highest number less then 1<<31,
276 * which is divisible by n. The probability for this is high for small
277 * n, and the worst case is 1/2 (for n=(1<<30)+1).
278 *
279 * The special treatment for n = power of 2, selects the high bits of
280 * the random number (the loop at the bottom would select the low order
281 * bits). This is done, because the low order bits of linear congruential
282 * number generators (like the one used in this class) are known to be
283 * ``less random'' than the high order bits.
284 *
285 * @param n the upper bound
286 * @throws IllegalArgumentException if the given upper bound is negative
287 * @return the next pseudorandom value
288 * @since 1.2
289 */
290 public int nextInt(int n)
291 {
292 if (n <= 0)
293 throw new IllegalArgumentException("n must be positive");
294 if ((n & -n) == n) // i.e., n is a power of 2
295 return (int) ((n * (long) next(31)) >> 31);
296 int bits, val;
297 do
298 {
299 bits = next(31);
300 val = bits % n;
301 }
302 while (bits - val + (n - 1) < 0);
303 return val;
304 }
305
306 /**
307 * Generates the next pseudorandom long number. All bits of this
308 * long are independently chosen and 0 and 1 have equal likelihood.
309 * The implementation for java.util.Random is:
310 *
311 <pre>public long nextLong()
312 {
313 return ((long) next(32) << 32) + next(32);
314 }</pre>
315 *
316 * @return the next pseudorandom value
317 */
318 public long nextLong()
319 {
320 return ((long) next(32) << 32) + next(32);
321 }
322
323 /**
324 * Generates the next pseudorandom boolean. True and false have
325 * the same probability. The implementation is:
326 *
327 <pre>public boolean nextBoolean()
328 {
329 return next(1) != 0;
330 }</pre>
331 *
332 * @return the next pseudorandom boolean
333 * @since 1.2
334 */
335 public boolean nextBoolean()
336 {
337 return next(1) != 0;
338 }
339
340 /**
341 * Generates the next pseudorandom float uniformly distributed
342 * between 0.0f (inclusive) and 1.0f (exclusive). The
343 * implementation is as follows.
344 *
345 <pre>public float nextFloat()
346 {
347 return next(24) / ((float)(1 << 24));
348 }</pre>
349 *
350 * @return the next pseudorandom float
351 */
352 public float nextFloat()
353 {
354 return next(24) / (float) (1 << 24);
355 }
356
357 /**
358 * Generates the next pseudorandom double uniformly distributed
359 * between 0.0 (inclusive) and 1.0 (exclusive). The
360 * implementation is as follows.
361 *
362 <pre>public double nextDouble()
363 {
364 return (((long) next(26) << 27) + next(27)) / (double)(1L << 53);
365 }</pre>
366 *
367 * @return the next pseudorandom double
368 */
369 public double nextDouble()
370 {
371 return (((long) next(26) << 27) + next(27)) / (double) (1L << 53);
372 }
373
374 /**
375 * Generates the next pseudorandom, Gaussian (normally) distributed
376 * double value, with mean 0.0 and standard deviation 1.0.
377 * The algorithm is as follows.
378 *
379 <pre>public synchronized double nextGaussian()
380 {
381 if (haveNextNextGaussian)
382 {
383 haveNextNextGaussian = false;
384 return nextNextGaussian;
385 }
386 else
387 {
388 double v1, v2, s;
389 do
390 {
391 v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0
392 v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0
393 s = v1 * v1 + v2 * v2;
394 }
395 while (s >= 1);
396
397 double norm = Math.sqrt(-2 * Math.log(s) / s);
398 nextNextGaussian = v2 * norm;
399 haveNextNextGaussian = true;
400 return v1 * norm;
401 }
402 }</pre>
403 *
404 * <p>This is described in section 3.4.1 of <em>The Art of Computer
405 * Programming, Volume 2</em> by Donald Knuth.
406 *
407 * @return the next pseudorandom Gaussian distributed double
408 */
409 public synchronized double nextGaussian()
410 {
411 if (haveNextNextGaussian)
412 {
413 haveNextNextGaussian = false;
414 return nextNextGaussian;
415 }
416 double v1, v2, s;
417 do
418 {
419 v1 = 2 * nextDouble() - 1; // Between -1.0 and 1.0.
420 v2 = 2 * nextDouble() - 1; // Between -1.0 and 1.0.
421 s = v1 * v1 + v2 * v2;
422 }
423 while (s >= 1);
424 double norm = Math.sqrt(-2 * Math.log(s) / s);
425 nextNextGaussian = v2 * norm;
426 haveNextNextGaussian = true;
427 return v1 * norm;
428 }
429 }