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/* Random.java -- a pseudo-random number generator |
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Copyright (C) 1998, 1999, 2000, 2001, 2002 Free Software Foundation, Inc. |
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|
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This file is part of GNU Classpath. |
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|
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GNU Classpath is free software; you can redistribute it and/or modify |
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it under the terms of the GNU General Public License as published by |
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the Free Software Foundation; either version 2, or (at your option) |
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any later version. |
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|
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GNU Classpath is distributed in the hope that it will be useful, but |
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WITHOUT ANY WARRANTY; without even the implied warranty of |
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU |
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General Public License for more details. |
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|
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You should have received a copy of the GNU General Public License |
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along with GNU Classpath; see the file COPYING. If not, write to the |
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Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA |
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02110-1301 USA. |
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|
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Linking this library statically or dynamically with other modules is |
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making a combined work based on this library. Thus, the terms and |
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conditions of the GNU General Public License cover the whole |
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combination. |
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|
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As a special exception, the copyright holders of this library give you |
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permission to link this library with independent modules to produce an |
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executable, regardless of the license terms of these independent |
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modules, and to copy and distribute the resulting executable under |
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terms of your choice, provided that you also meet, for each linked |
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independent module, the terms and conditions of the license of that |
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module. An independent module is a module which is not derived from |
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or based on this library. If you modify this library, you may extend |
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this exception to your version of the library, but you are not |
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obligated to do so. If you do not wish to do so, delete this |
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exception statement from your version. */ |
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|
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|
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package java.util; |
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|
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import java.io.Serializable; |
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|
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/** |
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* This class generates pseudorandom numbers. It uses the same |
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* algorithm as the original JDK-class, so that your programs behave |
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* exactly the same way, if started with the same seed. |
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* |
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* The algorithm is described in <em>The Art of Computer Programming, |
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* Volume 2</em> by Donald Knuth in Section 3.2.1. It is a 48-bit seed, |
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* linear congruential formula. |
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* |
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* If two instances of this class are created with the same seed and |
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* the same calls to these classes are made, they behave exactly the |
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* same way. This should be even true for foreign implementations |
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* (like this), so every port must use the same algorithm as described |
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* here. |
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* |
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* If you want to implement your own pseudorandom algorithm, you |
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* should extend this class and overload the <code>next()</code> and |
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* <code>setSeed(long)</code> method. In that case the above |
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* paragraph doesn't apply to you. |
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* |
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* This class shouldn't be used for security sensitive purposes (like |
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* generating passwords or encryption keys. See <code>SecureRandom</code> |
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* in package <code>java.security</code> for this purpose. |
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* |
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* For simple random doubles between 0.0 and 1.0, you may consider using |
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* Math.random instead. |
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* |
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* @see java.security.SecureRandom |
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* @see Math#random() |
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* @author Jochen Hoenicke |
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* @author Eric Blake (ebb9@email.byu.edu) |
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* @status updated to 1.4 |
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*/ |
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public class Random implements Serializable |
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{ |
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/** |
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* True if the next nextGaussian is available. This is used by |
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* nextGaussian, which generates two gaussian numbers by one call, |
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* and returns the second on the second call. |
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* |
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* @serial whether nextNextGaussian is available |
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* @see #nextGaussian() |
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* @see #nextNextGaussian |
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*/ |
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private boolean haveNextNextGaussian; |
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|
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/** |
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* The next nextGaussian, when available. This is used by nextGaussian, |
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* which generates two gaussian numbers by one call, and returns the |
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* second on the second call. |
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* |
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* @serial the second gaussian of a pair |
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* @see #nextGaussian() |
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* @see #haveNextNextGaussian |
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*/ |
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private double nextNextGaussian; |
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|
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/** |
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* The seed. This is the number set by setSeed and which is used |
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* in next. |
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* |
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* @serial the internal state of this generator |
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* @see #next(int) |
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*/ |
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private long seed; |
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|
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/** |
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* Compatible with JDK 1.0+. |
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*/ |
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private static final long serialVersionUID = 3905348978240129619L; |
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|
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/** |
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* Creates a new pseudorandom number generator. The seed is initialized |
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* to the current time, as if by |
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* <code>setSeed(System.currentTimeMillis());</code>. |
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* |
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* @see System#currentTimeMillis() |
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*/ |
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public Random() |
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{ |
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this(System.currentTimeMillis()); |
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} |
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|
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/** |
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* Creates a new pseudorandom number generator, starting with the |
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* specified seed, using <code>setSeed(seed);</code>. |
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* |
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* @param seed the initial seed |
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*/ |
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public Random(long seed) |
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{ |
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setSeed(seed); |
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} |
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|
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/** |
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* Sets the seed for this pseudorandom number generator. As described |
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* above, two instances of the same random class, starting with the |
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* same seed, should produce the same results, if the same methods |
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* are called. The implementation for java.util.Random is: |
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* |
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<pre>public synchronized void setSeed(long seed) |
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{ |
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this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1); |
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haveNextNextGaussian = false; |
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}</pre> |
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* |
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* @param seed the new seed |
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*/ |
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public synchronized void setSeed(long seed) |
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{ |
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this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1); |
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haveNextNextGaussian = false; |
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} |
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|
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/** |
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* Generates the next pseudorandom number. This returns |
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* an int value whose <code>bits</code> low order bits are |
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* independent chosen random bits (0 and 1 are equally likely). |
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* The implementation for java.util.Random is: |
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* |
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<pre>protected synchronized int next(int bits) |
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{ |
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seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1); |
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return (int) (seed >>> (48 - bits)); |
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}</pre> |
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* |
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* @param bits the number of random bits to generate, in the range 1..32 |
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* @return the next pseudorandom value |
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* @since 1.1 |
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*/ |
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protected synchronized int next(int bits) |
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{ |
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seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1); |
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return (int) (seed >>> (48 - bits)); |
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} |
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|
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/** |
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* Fills an array of bytes with random numbers. All possible values |
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* are (approximately) equally likely. |
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* The JDK documentation gives no implementation, but it seems to be: |
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* |
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<pre>public void nextBytes(byte[] bytes) |
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{ |
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for (int i = 0; i < bytes.length; i += 4) |
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{ |
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int random = next(32); |
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for (int j = 0; i + j < bytes.length && j < 4; j++) |
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{ |
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bytes[i+j] = (byte) (random & 0xff) |
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random >>= 8; |
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} |
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} |
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}</pre> |
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* |
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* @param bytes the byte array that should be filled |
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* @throws NullPointerException if bytes is null |
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* @since 1.1 |
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*/ |
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public void nextBytes(byte[] bytes) |
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{ |
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int random; |
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// Do a little bit unrolling of the above algorithm. |
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int max = bytes.length & ~0x3; |
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for (int i = 0; i < max; i += 4) |
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{ |
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random = next(32); |
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bytes[i] = (byte) random; |
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bytes[i + 1] = (byte) (random >> 8); |
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bytes[i + 2] = (byte) (random >> 16); |
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bytes[i + 3] = (byte) (random >> 24); |
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} |
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if (max < bytes.length) |
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{ |
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random = next(32); |
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for (int j = max; j < bytes.length; j++) |
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{ |
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bytes[j] = (byte) random; |
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random >>= 8; |
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} |
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} |
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} |
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|
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/** |
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* Generates the next pseudorandom number. This returns |
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* an int value whose 32 bits are independent chosen random bits |
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* (0 and 1 are equally likely). The implementation for |
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* java.util.Random is: |
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* |
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<pre>public int nextInt() |
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{ |
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return next(32); |
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}</pre> |
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* |
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* @return the next pseudorandom value |
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*/ |
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public int nextInt() |
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{ |
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return next(32); |
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} |
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|
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/** |
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* Generates the next pseudorandom number. This returns |
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* a value between 0(inclusive) and <code>n</code>(exclusive), and |
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* each value has the same likelihodd (1/<code>n</code>). |
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* (0 and 1 are equally likely). The implementation for |
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* java.util.Random is: |
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* |
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<pre> |
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public int nextInt(int n) |
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{ |
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if (n <= 0) |
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throw new IllegalArgumentException("n must be positive"); |
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|
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if ((n & -n) == n) // i.e., n is a power of 2 |
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return (int)((n * (long) next(31)) >> 31); |
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|
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int bits, val; |
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do |
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{ |
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bits = next(31); |
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val = bits % n; |
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} |
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while(bits - val + (n-1) < 0); |
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|
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return val; |
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}</pre> |
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* |
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* <p>This algorithm would return every value with exactly the same |
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* probability, if the next()-method would be a perfect random number |
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* generator. |
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* |
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* The loop at the bottom only accepts a value, if the random |
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* number was between 0 and the highest number less then 1<<31, |
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* which is divisible by n. The probability for this is high for small |
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* n, and the worst case is 1/2 (for n=(1<<30)+1). |
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* |
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* The special treatment for n = power of 2, selects the high bits of |
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* the random number (the loop at the bottom would select the low order |
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* bits). This is done, because the low order bits of linear congruential |
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* number generators (like the one used in this class) are known to be |
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* ``less random'' than the high order bits. |
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* |
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* @param n the upper bound |
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* @throws IllegalArgumentException if the given upper bound is negative |
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* @return the next pseudorandom value |
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* @since 1.2 |
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*/ |
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public int nextInt(int n) |
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{ |
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if (n <= 0) |
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throw new IllegalArgumentException("n must be positive"); |
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if ((n & -n) == n) // i.e., n is a power of 2 |
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return (int) ((n * (long) next(31)) >> 31); |
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int bits, val; |
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do |
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{ |
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bits = next(31); |
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val = bits % n; |
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} |
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while (bits - val + (n - 1) < 0); |
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return val; |
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} |
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|
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/** |
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* Generates the next pseudorandom long number. All bits of this |
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* long are independently chosen and 0 and 1 have equal likelihood. |
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* The implementation for java.util.Random is: |
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* |
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<pre>public long nextLong() |
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{ |
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return ((long) next(32) << 32) + next(32); |
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}</pre> |
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* |
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* @return the next pseudorandom value |
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*/ |
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public long nextLong() |
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{ |
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return ((long) next(32) << 32) + next(32); |
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} |
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|
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/** |
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* Generates the next pseudorandom boolean. True and false have |
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* the same probability. The implementation is: |
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* |
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<pre>public boolean nextBoolean() |
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{ |
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return next(1) != 0; |
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}</pre> |
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* |
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* @return the next pseudorandom boolean |
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* @since 1.2 |
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*/ |
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public boolean nextBoolean() |
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{ |
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return next(1) != 0; |
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} |
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|
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/** |
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* Generates the next pseudorandom float uniformly distributed |
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* between 0.0f (inclusive) and 1.0f (exclusive). The |
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* implementation is as follows. |
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* |
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<pre>public float nextFloat() |
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{ |
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return next(24) / ((float)(1 << 24)); |
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}</pre> |
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* |
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* @return the next pseudorandom float |
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*/ |
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public float nextFloat() |
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{ |
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return next(24) / (float) (1 << 24); |
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} |
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|
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/** |
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* Generates the next pseudorandom double uniformly distributed |
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* between 0.0 (inclusive) and 1.0 (exclusive). The |
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* implementation is as follows. |
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* |
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<pre>public double nextDouble() |
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{ |
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return (((long) next(26) << 27) + next(27)) / (double)(1L << 53); |
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}</pre> |
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* |
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* @return the next pseudorandom double |
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*/ |
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public double nextDouble() |
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{ |
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return (((long) next(26) << 27) + next(27)) / (double) (1L << 53); |
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} |
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|
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/** |
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* Generates the next pseudorandom, Gaussian (normally) distributed |
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* double value, with mean 0.0 and standard deviation 1.0. |
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* The algorithm is as follows. |
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* |
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<pre>public synchronized double nextGaussian() |
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{ |
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if (haveNextNextGaussian) |
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{ |
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haveNextNextGaussian = false; |
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return nextNextGaussian; |
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} |
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else |
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{ |
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double v1, v2, s; |
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do |
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{ |
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v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0 |
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v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0 |
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s = v1 * v1 + v2 * v2; |
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} |
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while (s >= 1); |
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|
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double norm = Math.sqrt(-2 * Math.log(s) / s); |
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nextNextGaussian = v2 * norm; |
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haveNextNextGaussian = true; |
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return v1 * norm; |
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} |
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}</pre> |
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* |
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* <p>This is described in section 3.4.1 of <em>The Art of Computer |
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* Programming, Volume 2</em> by Donald Knuth. |
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* |
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* @return the next pseudorandom Gaussian distributed double |
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*/ |
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public synchronized double nextGaussian() |
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{ |
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if (haveNextNextGaussian) |
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{ |
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haveNextNextGaussian = false; |
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return nextNextGaussian; |
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} |
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double v1, v2, s; |
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do |
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{ |
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v1 = 2 * nextDouble() - 1; // Between -1.0 and 1.0. |
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v2 = 2 * nextDouble() - 1; // Between -1.0 and 1.0. |
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s = v1 * v1 + v2 * v2; |
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} |
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while (s >= 1); |
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double norm = Math.sqrt(-2 * Math.log(s) / s); |
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nextNextGaussian = v2 * norm; |
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haveNextNextGaussian = true; |
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return v1 * norm; |
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} |
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} |