mirror of git://gcc.gnu.org/git/gcc.git
391 lines
12 KiB
Java
391 lines
12 KiB
Java
/* java.util.Random
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Copyright (C) 1998, 1999, 2000, 2001 Free Software Foundation, Inc.
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This file is part of GNU Classpath.
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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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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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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., 59 Temple Place, Suite 330, Boston, MA
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02111-1307 USA.
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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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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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package java.util;
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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.
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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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public class Random implements java.io.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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* @see #nextGaussian. */
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private boolean haveNextNextGaussian;
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/**
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* The next nextGaussian if 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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* @see #nextGaussian.
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*/
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private double nextNextGaussian;
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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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* @see #next
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*/
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private long seed;
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private static final long serialVersionUID = 3905348978240129619L;
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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 follows.
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* <pre>
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* setSeed(System.currentTimeMillis());
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* </pre>
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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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setSeed(System.currentTimeMillis());
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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. This does:
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* <pre>
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* setSeed(seed);
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* </pre>
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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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* 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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* <pre>
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* public synchronized void setSeed(long seed) {
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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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* </pre>
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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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* 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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* <pre>
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* protected synchronized int next(int bits) {
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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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* </pre>
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* @param bits the number of random bits to generate. Must be in range
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* 1..32.
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* @return the next pseudorandom value.
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* @since JDK1.1
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*/
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protected synchronized int next(int bits)
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/*{ require { 1 <= bits && bits <=32 ::
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"bits "+bits+" not in range [1..32]" } } */
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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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* 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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* <pre>
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* public void nextBytes(byte[] bytes) {
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* for (int i=0; i< bytes.length; i+=4) {
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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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* bytes[i+j] = (byte) (random & 0xff)
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* random >>= 8;
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* }
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* }
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* }
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* </pre>
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* @param bytes The byte array that should be filled.
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* @since JDK1.1
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*/
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public void nextBytes(byte[] bytes)
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/*{ require { bytes != null :: "bytes is null"; } } */
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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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* 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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* <pre>
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* public int nextInt() {
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* return next(32);
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* }
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* </pre>
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*
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* @return the next pseudorandom value. */
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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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* 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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* <pre>
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* public int nextInt(int n) {
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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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* bits = next(32);
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* val = bits % n;
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* } while(bits - val + (n-1) < 0);
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* return val;
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* }
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* </pre>
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* 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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* @exception IllegalArgumentException if the given upper bound is negative
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* @return the next pseudorandom value.
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*/
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public int nextInt(int n)
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/*{ require { n > 0 :: "n must be positive"; } } */
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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(32);
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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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* 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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* <pre>
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* public long nextLong() {
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* return ((long)next(32) << 32) + next(32);
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* }
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* </pre>
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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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* Generates the next pseudorandom boolean. True and false have
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* the same probability. The implementation is:
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* <pre>
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* public boolean nextBoolean() {
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* return next(1) != 0;
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* }
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* </pre>
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* @return the next pseudorandom boolean.
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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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* Generates the next pseudorandom float uniformly distributed
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* between 0.0f (inclusive) and 1.0 (exclusive). The
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* implementation is as follows.
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* <pre>
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* public float nextFloat() {
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* return next(24) / ((float)(1 << 24));
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* }
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* </pre>
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* @return the next pseudorandom float. */
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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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* Generates the next pseudorandom double uniformly distributed
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* between 0.0f (inclusive) and 1.0 (exclusive). The
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* implementation is as follows.
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* <pre>
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* public double nextDouble() {
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* return (((long)next(26) << 27) + next(27)) / (double)(1 << 53);
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* }
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* </pre>
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* @return the next pseudorandom double. */
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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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* 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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* <pre>
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* public synchronized double nextGaussian() {
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* if (haveNextNextGaussian) {
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* haveNextNextGaussian = false;
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* return nextNextGaussian;
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* } else {
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* double v1, v2, s;
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* do {
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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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* } 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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* }
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* </pre>
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* 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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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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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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}
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}
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