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/* |
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* @(#)Random.java 1.38 02/03/04 |
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* |
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* Copyright 2002 Sun Microsystems, Inc. All rights reserved. |
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* SUN PROPRIETARY/CONFIDENTIAL. Use is subject to license terms. |
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*/ |
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|
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package java.util; |
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import java.io.*; |
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import sun.misc.Unsafe; |
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|
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/** |
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* An instance of this class is used to generate a stream of |
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* pseudorandom numbers. The class uses a 48-bit seed, which is |
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* modified using a linear congruential formula. (See Donald Knuth, |
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* <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.) |
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* <p> |
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* If two instances of <code>Random</code> are created with the same |
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* seed, and the same sequence of method calls is made for each, they |
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* will generate and return identical sequences of numbers. In order to |
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* guarantee this property, particular algorithms are specified for the |
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* class <tt>Random</tt>. Java implementations must use all the algorithms |
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* shown here for the class <tt>Random</tt>, for the sake of absolute |
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* portability of Java code. However, subclasses of class <tt>Random</tt> |
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* are permitted to use other algorithms, so long as they adhere to the |
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* general contracts for all the methods. |
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* <p> |
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* The algorithms implemented by class <tt>Random</tt> use a |
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* <tt>protected</tt> utility method that on each invocation can supply |
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* up to 32 pseudorandomly generated bits. |
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* <p> |
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* Many applications will find the <code>random</code> method in |
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* class <code>Math</code> simpler to use. |
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* |
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* @author Frank Yellin |
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* @version 1.38, 03/04/02 |
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* @see java.lang.Math#random() |
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* @since JDK1.0 |
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*/ |
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public |
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class Random implements java.io.Serializable { |
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/** use serialVersionUID from JDK 1.1 for interoperability */ |
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static final long serialVersionUID = 3905348978240129619L; |
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|
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// Setup to use Unsafe.compareAndSwapLong to update seed. |
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private static final Unsafe unsafe = Unsafe.getUnsafe(); |
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private static final long seedOffset; |
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static { |
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try { |
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seedOffset = |
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unsafe.objectFieldOffset(Random.class.getDeclaredField("seed")); |
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} |
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catch(Exception ex) { throw new Error(ex); } |
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} |
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|
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/** |
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* The internal state associated with this pseudorandom number generator. |
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* (The specs for the methods in this class describe the ongoing |
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* computation of this value.) |
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* |
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* @serial |
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*/ |
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private volatile long seed; |
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|
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private final static long multiplier = 0x5DEECE66DL; |
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private final static long addend = 0xBL; |
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private final static long mask = (1L << 48) - 1; |
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|
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/** |
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* Creates a new random number generator. Its seed is initialized to |
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* a value based on the current time: |
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* <blockquote><pre> |
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* public Random() { this(System.currentTimeMillis()); }</pre></blockquote> |
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* Two Random objects created within the same millisecond will have |
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* the same sequence of random numbers. |
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* |
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* @see java.lang.System#currentTimeMillis() |
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*/ |
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public Random() { this(System.currentTimeMillis()); } |
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|
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/** |
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* Creates a new random number generator using a single |
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* <code>long</code> seed: |
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* <blockquote><pre> |
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* public Random(long seed) { setSeed(seed); }</pre></blockquote> |
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* Used by method <tt>next</tt> to hold |
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* the state of the pseudorandom number generator. |
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* |
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* @param seed the initial seed. |
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* @see java.util.Random#setSeed(long) |
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*/ |
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public Random(long seed) { |
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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 of this random number generator using a single |
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* <code>long</code> seed. The general contract of <tt>setSeed</tt> |
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* is that it alters the state of this random number generator |
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* object so as to be in exactly the same state as if it had just |
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* been created with the argument <tt>seed</tt> as a seed. The method |
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* <tt>setSeed</tt> is implemented by class Random as follows: |
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* <blockquote><pre> |
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* synchronized public 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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* }</pre></blockquote> |
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* The implementation of <tt>setSeed</tt> by class <tt>Random</tt> |
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* happens to use only 48 bits of the given seed. In general, however, |
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* an overriding method may use all 64 bits of the long argument |
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* as a seed value. |
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* |
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* Note: Even though seed is updated atomically, this method |
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* must still be synchronized to ensure correct semantics |
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* of haveNextNextGaussian. |
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* |
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* @param seed the initial seed. |
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*/ |
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synchronized public void setSeed(long seed) { |
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this.seed = (seed ^ multiplier) & mask; |
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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. Subclass should |
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* override this, as this is used by all other methods.<p> |
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* The general contract of <tt>next</tt> is that it returns an |
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* <tt>int</tt> value and if the argument bits is between <tt>1</tt> |
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* and <tt>32</tt> (inclusive), then that many low-order bits of the |
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* returned value will be (approximately) independently chosen bit |
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* values, each of which is (approximately) equally likely to be |
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* <tt>0</tt> or <tt>1</tt>. The method <tt>next</tt> is implemented |
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* by class <tt>Random</tt> as follows: |
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* <blockquote><pre> |
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* synchronized protected 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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* }</pre></blockquote> |
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* This is a linear congruential pseudorandom number generator, as |
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* defined by D. H. Lehmer and described by Donald E. Knuth in <i>The |
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* Art of Computer Programming,</i> Volume 2: <i>Seminumerical |
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* Algorithms</i>, section 3.2.1. |
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* |
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* @param bits random bits |
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* @return the next pseudorandom value from this random number generator's sequence. |
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* @since JDK1.1 |
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*/ |
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protected int next(int bits) { |
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long oldseed, nextseed; |
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do { |
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oldseed = seed; |
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nextseed = (oldseed * multiplier + addend) & mask; |
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} while (!unsafe.compareAndSwapLong(this, seedOffset, |
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oldseed, nextseed)); |
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return (int)(nextseed >>> (48 - bits)); |
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} |
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|
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private static final int BITS_PER_BYTE = 8; |
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private static final int BYTES_PER_INT = 4; |
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|
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/** |
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* Generates random bytes and places them into a user-supplied |
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* byte array. The number of random bytes produced is equal to |
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* the length of the byte array. |
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* |
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* @param bytes the non-null byte array in which to put the |
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* random bytes. |
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* @since JDK1.1 |
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*/ |
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public void nextBytes(byte[] bytes) { |
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int numRequested = bytes.length; |
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|
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int numGot = 0, rnd = 0; |
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|
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while (true) { |
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for (int i = 0; i < BYTES_PER_INT; i++) { |
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if (numGot == numRequested) |
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return; |
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|
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rnd = (i==0 ? next(BITS_PER_BYTE * BYTES_PER_INT) |
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: rnd >> BITS_PER_BYTE); |
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bytes[numGot++] = (byte)rnd; |
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} |
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} |
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} |
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|
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/** |
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* Returns the next pseudorandom, uniformly distributed <code>int</code> |
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* value from this random number generator's sequence. The general |
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* contract of <tt>nextInt</tt> is that one <tt>int</tt> value is |
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* pseudorandomly generated and returned. All 2<font size="-1"><sup>32 |
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* </sup></font> possible <tt>int</tt> values are produced with |
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* (approximately) equal probability. The method <tt>nextInt</tt> is |
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* implemented by class <tt>Random</tt> as follows: |
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* <blockquote><pre> |
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* public int nextInt() { return next(32); }</pre></blockquote> |
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* |
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* @return the next pseudorandom, uniformly distributed <code>int</code> |
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* value from this random number generator's sequence. |
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*/ |
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public int nextInt() { return next(32); } |
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|
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/** |
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* Returns a pseudorandom, uniformly distributed <tt>int</tt> value |
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* between 0 (inclusive) and the specified value (exclusive), drawn from |
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* this random number generator's sequence. The general contract of |
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* <tt>nextInt</tt> is that one <tt>int</tt> value in the specified range |
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* is pseudorandomly generated and returned. All <tt>n</tt> possible |
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* <tt>int</tt> values are produced with (approximately) equal |
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* probability. The method <tt>nextInt(int n)</tt> is implemented by |
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* class <tt>Random</tt> as follows: |
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* <blockquote><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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* |
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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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* bits = next(31); |
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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></blockquote> |
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* <p> |
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* The hedge "approximately" is used in the foregoing description only |
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* because the next method is only approximately an unbiased source of |
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* independently chosen bits. If it were a perfect source of randomly |
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* chosen bits, then the algorithm shown would choose <tt>int</tt> |
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* values from the stated range with perfect uniformity. |
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* <p> |
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* The algorithm is slightly tricky. It rejects values that would result |
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* in an uneven distribution (due to the fact that 2^31 is not divisible |
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* by n). The probability of a value being rejected depends on n. The |
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* worst case is n=2^30+1, for which the probability of a reject is 1/2, |
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* and the expected number of iterations before the loop terminates is 2. |
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* <p> |
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* The algorithm treats the case where n is a power of two specially: it |
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* returns the correct number of high-order bits from the underlying |
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* pseudo-random number generator. In the absence of special treatment, |
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* the correct number of <i>low-order</i> bits would be returned. Linear |
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* congruential pseudo-random number generators such as the one |
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* implemented by this class are known to have short periods in the |
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* sequence of values of their low-order bits. Thus, this special case |
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* greatly increases the length of the sequence of values returned by |
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* successive calls to this method if n is a small power of two. |
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* |
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* @param n the bound on the random number to be returned. Must be |
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* positive. |
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* @return a pseudorandom, uniformly distributed <tt>int</tt> |
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* value between 0 (inclusive) and n (exclusive). |
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* @exception IllegalArgumentException n is not positive. |
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* @since 1.2 |
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*/ |
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|
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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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|
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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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bits = next(31); |
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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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|
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/** |
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* Returns the next pseudorandom, uniformly distributed <code>long</code> |
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* value from this random number generator's sequence. The general |
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* contract of <tt>nextLong</tt> is that one long value is pseudorandomly |
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* generated and returned. All 2<font size="-1"><sup>64</sup></font> |
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* possible <tt>long</tt> values are produced with (approximately) equal |
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* probability. The method <tt>nextLong</tt> is implemented by class |
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* <tt>Random</tt> as follows: |
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* <blockquote><pre> |
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* public long nextLong() { |
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* return ((long)next(32) << 32) + next(32); |
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* }</pre></blockquote> |
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* |
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* @return the next pseudorandom, uniformly distributed <code>long</code> |
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* value from this random number generator's sequence. |
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*/ |
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public long nextLong() { |
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// it's okay that the bottom word remains signed. |
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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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* Returns the next pseudorandom, uniformly distributed |
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* <code>boolean</code> value from this random number generator's |
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* sequence. The general contract of <tt>nextBoolean</tt> is that one |
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* <tt>boolean</tt> value is pseudorandomly generated and returned. The |
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* values <code>true</code> and <code>false</code> are produced with |
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* (approximately) equal probability. The method <tt>nextBoolean</tt> is |
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* implemented by class <tt>Random</tt> as follows: |
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* <blockquote><pre> |
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* public boolean nextBoolean() {return next(1) != 0;} |
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* </pre></blockquote> |
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* @return the next pseudorandom, uniformly distributed |
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* <code>boolean</code> value from this random number generator's |
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* sequence. |
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* @since 1.2 |
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*/ |
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public boolean nextBoolean() {return next(1) != 0;} |
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|
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/** |
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* Returns the next pseudorandom, uniformly distributed <code>float</code> |
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* value between <code>0.0</code> and <code>1.0</code> from this random |
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* number generator's sequence. <p> |
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* The general contract of <tt>nextFloat</tt> is that one <tt>float</tt> |
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* value, chosen (approximately) uniformly from the range <tt>0.0f</tt> |
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* (inclusive) to <tt>1.0f</tt> (exclusive), is pseudorandomly |
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* generated and returned. All 2<font size="-1"><sup>24</sup></font> |
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* possible <tt>float</tt> values of the form |
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* <i>m x </i>2<font size="-1"><sup>-24</sup></font>, where |
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* <i>m</i> is a positive integer less than 2<font size="-1"><sup>24</sup> |
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* </font>, are produced with (approximately) equal probability. The |
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* method <tt>nextFloat</tt> is implemented by class <tt>Random</tt> as |
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* follows: |
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* <blockquote><pre> |
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* public float nextFloat() { |
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* return next(24) / ((float)(1 << 24)); |
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* }</pre></blockquote> |
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* The hedge "approximately" is used in the foregoing description only |
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* because the next method is only approximately an unbiased source of |
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* independently chosen bits. If it were a perfect source or randomly |
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* chosen bits, then the algorithm shown would choose <tt>float</tt> |
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* values from the stated range with perfect uniformity.<p> |
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* [In early versions of Java, the result was incorrectly calculated as: |
337 |
* <blockquote><pre> |
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* return next(30) / ((float)(1 << 30));</pre></blockquote> |
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* This might seem to be equivalent, if not better, but in fact it |
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* introduced a slight nonuniformity because of the bias in the rounding |
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* of floating-point numbers: it was slightly more likely that the |
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* low-order bit of the significand would be 0 than that it would be 1.] |
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* |
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* @return the next pseudorandom, uniformly distributed <code>float</code> |
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* value between <code>0.0</code> and <code>1.0</code> from this |
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* random number generator's sequence. |
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*/ |
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public float nextFloat() { |
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int i = next(24); |
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return i / ((float)(1 << 24)); |
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} |
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|
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/** |
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* Returns the next pseudorandom, uniformly distributed |
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* <code>double</code> value between <code>0.0</code> and |
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* <code>1.0</code> from this random number generator's sequence. <p> |
357 |
* The general contract of <tt>nextDouble</tt> is that one |
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* <tt>double</tt> value, chosen (approximately) uniformly from the |
359 |
* range <tt>0.0d</tt> (inclusive) to <tt>1.0d</tt> (exclusive), is |
360 |
* pseudorandomly generated and returned. All |
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* 2<font size="-1"><sup>53</sup></font> possible <tt>float</tt> |
362 |
* values of the form <i>m x </i>2<font size="-1"><sup>-53</sup> |
363 |
* </font>, where <i>m</i> is a positive integer less than |
364 |
* 2<font size="-1"><sup>53</sup></font>, are produced with |
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* (approximately) equal probability. The method <tt>nextDouble</tt> is |
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* implemented by class <tt>Random</tt> as follows: |
367 |
* <blockquote><pre> |
368 |
* public double nextDouble() { |
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* return (((long)next(26) << 27) + next(27)) |
370 |
* / (double)(1L << 53); |
371 |
* }</pre></blockquote><p> |
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* The hedge "approximately" is used in the foregoing description only |
373 |
* because the <tt>next</tt> method is only approximately an unbiased |
374 |
* source of independently chosen bits. If it were a perfect source or |
375 |
* randomly chosen bits, then the algorithm shown would choose |
376 |
* <tt>double</tt> values from the stated range with perfect uniformity. |
377 |
* <p>[In early versions of Java, the result was incorrectly calculated as: |
378 |
* <blockquote><pre> |
379 |
* return (((long)next(27) << 27) + next(27)) |
380 |
* / (double)(1L << 54);</pre></blockquote> |
381 |
* This might seem to be equivalent, if not better, but in fact it |
382 |
* introduced a large nonuniformity because of the bias in the rounding |
383 |
* of floating-point numbers: it was three times as likely that the |
384 |
* low-order bit of the significand would be 0 than that it would be |
385 |
* 1! This nonuniformity probably doesn't matter much in practice, but |
386 |
* we strive for perfection.] |
387 |
* |
388 |
* @return the next pseudorandom, uniformly distributed |
389 |
* <code>double</code> value between <code>0.0</code> and |
390 |
* <code>1.0</code> from this random number generator's sequence. |
391 |
*/ |
392 |
public double nextDouble() { |
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long l = ((long)(next(26)) << 27) + next(27); |
394 |
return l / (double)(1L << 53); |
395 |
} |
396 |
|
397 |
private double nextNextGaussian; |
398 |
private boolean haveNextNextGaussian = false; |
399 |
|
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/** |
401 |
* Returns the next pseudorandom, Gaussian ("normally") distributed |
402 |
* <code>double</code> value with mean <code>0.0</code> and standard |
403 |
* deviation <code>1.0</code> from this random number generator's sequence. |
404 |
* <p> |
405 |
* The general contract of <tt>nextGaussian</tt> is that one |
406 |
* <tt>double</tt> value, chosen from (approximately) the usual |
407 |
* normal distribution with mean <tt>0.0</tt> and standard deviation |
408 |
* <tt>1.0</tt>, is pseudorandomly generated and returned. The method |
409 |
* <tt>nextGaussian</tt> is implemented by class <tt>Random</tt> as follows: |
410 |
* <blockquote><pre> |
411 |
* synchronized public double nextGaussian() { |
412 |
* if (haveNextNextGaussian) { |
413 |
* haveNextNextGaussian = false; |
414 |
* return nextNextGaussian; |
415 |
* } else { |
416 |
* double v1, v2, s; |
417 |
* do { |
418 |
* v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0 |
419 |
* v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0 |
420 |
* s = v1 * v1 + v2 * v2; |
421 |
* } while (s >= 1 || s == 0); |
422 |
* double multiplier = Math.sqrt(-2 * Math.log(s)/s); |
423 |
* nextNextGaussian = v2 * multiplier; |
424 |
* haveNextNextGaussian = true; |
425 |
* return v1 * multiplier; |
426 |
* } |
427 |
* }</pre></blockquote> |
428 |
* This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and |
429 |
* G. Marsaglia, as described by Donald E. Knuth in <i>The Art of |
430 |
* Computer Programming</i>, Volume 2: <i>Seminumerical Algorithms</i>, |
431 |
* section 3.4.1, subsection C, algorithm P. Note that it generates two |
432 |
* independent values at the cost of only one call to <tt>Math.log</tt> |
433 |
* and one call to <tt>Math.sqrt</tt>. |
434 |
* |
435 |
* @return the next pseudorandom, Gaussian ("normally") distributed |
436 |
* <code>double</code> value with mean <code>0.0</code> and |
437 |
* standard deviation <code>1.0</code> from this random number |
438 |
* generator's sequence. |
439 |
*/ |
440 |
synchronized public double nextGaussian() { |
441 |
// See Knuth, ACP, Section 3.4.1 Algorithm C. |
442 |
if (haveNextNextGaussian) { |
443 |
haveNextNextGaussian = false; |
444 |
return nextNextGaussian; |
445 |
} else { |
446 |
double v1, v2, s; |
447 |
do { |
448 |
v1 = 2 * nextDouble() - 1; // between -1 and 1 |
449 |
v2 = 2 * nextDouble() - 1; // between -1 and 1 |
450 |
s = v1 * v1 + v2 * v2; |
451 |
} while (s >= 1 || s == 0); |
452 |
double multiplier = Math.sqrt(-2 * Math.log(s)/s); |
453 |
nextNextGaussian = v2 * multiplier; |
454 |
haveNextNextGaussian = true; |
455 |
return v1 * multiplier; |
456 |
} |
457 |
} |
458 |
|
459 |
/** |
460 |
* Serializable fields for Random. |
461 |
* |
462 |
* @serialField seed long; |
463 |
* seed for random computations |
464 |
* @serialField nextNextGaussian double; |
465 |
* next Gaussian to be returned |
466 |
* @serialField haveNextNextGaussian boolean |
467 |
* nextNextGaussian is valid |
468 |
*/ |
469 |
private static final ObjectStreamField[] serialPersistentFields = { |
470 |
new ObjectStreamField("seed", Long.TYPE), |
471 |
new ObjectStreamField("nextNextGaussian", Double.TYPE), |
472 |
new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE) |
473 |
}; |
474 |
|
475 |
/** |
476 |
* Reconstitute the <tt>Random</tt> instance from a stream (that is, |
477 |
* deserialize it). The seed is read in as long for |
478 |
* historical reasons, but it is converted to an AtomicLong. |
479 |
*/ |
480 |
private void readObject(java.io.ObjectInputStream s) |
481 |
throws java.io.IOException, ClassNotFoundException { |
482 |
|
483 |
ObjectInputStream.GetField fields = s.readFields(); |
484 |
long seedVal; |
485 |
|
486 |
seedVal = (long) fields.get("seed", -1L); |
487 |
if (seedVal < 0) |
488 |
throw new java.io.StreamCorruptedException( |
489 |
"Random: invalid seed"); |
490 |
seed = seedVal; |
491 |
nextNextGaussian = fields.get("nextNextGaussian", 0.0); |
492 |
haveNextNextGaussian = fields.get("haveNextNextGaussian", false); |
493 |
} |
494 |
|
495 |
|
496 |
/** |
497 |
* Save the <tt>Random</tt> instance to a stream. |
498 |
* The seed of a Random is serialized as a long for |
499 |
* historical reasons. |
500 |
* |
501 |
*/ |
502 |
synchronized private void writeObject(ObjectOutputStream s) throws IOException { |
503 |
// set the values of the Serializable fields |
504 |
ObjectOutputStream.PutField fields = s.putFields(); |
505 |
fields.put("seed", seed); |
506 |
fields.put("nextNextGaussian", nextNextGaussian); |
507 |
fields.put("haveNextNextGaussian", haveNextNextGaussian); |
508 |
|
509 |
// save them |
510 |
s.writeFields(); |
511 |
|
512 |
} |
513 |
|
514 |
} |