/*
* @(#)Random.java 1.43 04/01/12
*
* Copyright 2004 Sun Microsystems, Inc. All rights reserved.
* SUN PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
*/
package java.util;
import java.io.*;
import java.util.concurrent.atomic.AtomicLong;
/**
* An instance of this class is used to generate a stream of
* pseudorandom numbers. The class uses a 48-bit seed, which is
* modified using a linear congruential formula. (See Donald Knuth,
* <i>The Art of Computer Programming, Volume 2</i>, Section 3.2.1.)
* <p>
* If two instances of <code>Random</code> are created with the same
* seed, and the same sequence of method calls is made for each, they
* will generate and return identical sequences of numbers. In order to
* guarantee this property, particular algorithms are specified for the
* class <tt>Random</tt>. Java implementations must use all the algorithms
* shown here for the class <tt>Random</tt>, for the sake of absolute
* portability of Java code. However, subclasses of class <tt>Random</tt>
* are permitted to use other algorithms, so long as they adhere to the
* general contracts for all the methods.
* <p>
* The algorithms implemented by class <tt>Random</tt> use a
* <tt>protected</tt> utility method that on each invocation can supply
* up to 32 pseudorandomly generated bits.
* <p>
* Many applications will find the <code>random</code> method in
* class <code>Math</code> simpler to use.
*
* @author Frank Yellin
* @version 1.43, 01/12/04
* @see java.lang.Math#random()
* @since JDK1.0
*/
public
class Random implements java.io.Serializable {
/** use serialVersionUID from JDK 1.1 for interoperability */
static final long serialVersionUID = 3905348978240129619L;
/**
* The internal state associated with this pseudorandom number generator.
* (The specs for the methods in this class describe the ongoing
* computation of this value.)
*
* @serial
*/
private AtomicLong seed;
private final static long multiplier = 0x5DEECE66DL;
private final static long addend = 0xBL;
private final static long mask = (1L << 48) - 1;
/**
* Creates a new random number generator. This constructor sets
* the seed of the random number generator to a value very likely
* to be distinct from any other invocation of this constructor.
*/
public Random() { this(++seedUniquifier + System.nanoTime()); }
private static volatile long seedUniquifier = 8682522807148012L;
/**
* Creates a new random number generator using a single
* <code>long</code> seed:
* <blockquote><pre>
* public Random(long seed) { setSeed(seed); }</pre></blockquote>
* Used by method <tt>next</tt> to hold
* the state of the pseudorandom number generator.
*
* @param seed the initial seed.
* @see java.util.Random#setSeed(long)
*/
public Random(long seed) {
this.seed = new AtomicLong(0L);
setSeed(seed);
}
/**
* Sets the seed of this random number generator using a single
* <code>long</code> seed. The general contract of <tt>setSeed</tt>
* is that it alters the state of this random number generator
* object so as to be in exactly the same state as if it had just
* been created with the argument <tt>seed</tt> as a seed. The method
* <tt>setSeed</tt> is implemented by class Random as follows:
* <blockquote><pre>
* synchronized public void setSeed(long seed) {
* this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
* haveNextNextGaussian = false;
* }</pre></blockquote>
* The implementation of <tt>setSeed</tt> by class <tt>Random</tt>
* happens to use only 48 bits of the given seed. In general, however,
* an overriding method may use all 64 bits of the long argument
* as a seed value.
*
* Note: Although the seed value is an AtomicLong, this method
* must still be synchronized to ensure correct semantics
* of haveNextNextGaussian.
*
* @param seed the initial seed.
*/
synchronized public void setSeed(long seed) {
seed = (seed ^ multiplier) & mask;
this.seed.set(seed);
haveNextNextGaussian = false;
}
/**
* Generates the next pseudorandom number. Subclass should
* override this, as this is used by all other methods.<p>
* The general contract of <tt>next</tt> is that it returns an
* <tt>int</tt> value and if the argument bits is between <tt>1</tt>
* and <tt>32</tt> (inclusive), then that many low-order bits of the
* returned value will be (approximately) independently chosen bit
* values, each of which is (approximately) equally likely to be
* <tt>0</tt> or <tt>1</tt>. The method <tt>next</tt> is implemented
* by class <tt>Random</tt> as follows:
* <blockquote><pre>
* synchronized protected int next(int bits) {
* seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
* return (int)(seed >>> (48 - bits));
* }</pre></blockquote>
* This is a linear congruential pseudorandom number generator, as
* defined by D. H. Lehmer and described by Donald E. Knuth in <i>The
* Art of Computer Programming,</i> Volume 2: <i>Seminumerical
* Algorithms</i>, section 3.2.1.
*
* @param bits random bits
* @return the next pseudorandom value from this random number generator's sequence.
* @since JDK1.1
*/
protected int next(int bits) {
long oldseed, nextseed;
AtomicLong seed = this.seed;
do {
oldseed = seed.get();
nextseed = (oldseed * multiplier + addend) & mask;
} while (!seed.compareAndSet(oldseed, nextseed));
return (int)(nextseed >>> (48 - bits));
}
private static final int BITS_PER_BYTE = 8;
private static final int BYTES_PER_INT = 4;
/**
* Generates random bytes and places them into a user-supplied
* byte array. The number of random bytes produced is equal to
* the length of the byte array.
*
* @param bytes the non-null byte array in which to put the
* random bytes.
* @since JDK1.1
*/
public void nextBytes(byte[] bytes) {
int numRequested = bytes.length;
int numGot = 0, rnd = 0;
while (true) {
for (int i = 0; i < BYTES_PER_INT; i++) {
if (numGot == numRequested)
return;
rnd = (i==0 ? next(BITS_PER_BYTE * BYTES_PER_INT)
: rnd >> BITS_PER_BYTE);
bytes[numGot++] = (byte)rnd;
}
}
}
/**
* Returns the next pseudorandom, uniformly distributed <code>int</code>
* value from this random number generator's sequence. The general
* contract of <tt>nextInt</tt> is that one <tt>int</tt> value is
* pseudorandomly generated and returned. All 2<font size="-1"><sup>32
* </sup></font> possible <tt>int</tt> values are produced with
* (approximately) equal probability. The method <tt>nextInt</tt> is
* implemented by class <tt>Random</tt> as follows:
* <blockquote><pre>
* public int nextInt() { return next(32); }</pre></blockquote>
*
* @return the next pseudorandom, uniformly distributed <code>int</code>
* value from this random number generator's sequence.
*/
public int nextInt() { return next(32); }
/**
* Returns a pseudorandom, uniformly distributed <tt>int</tt> value
* between 0 (inclusive) and the specified value (exclusive), drawn from
* this random number generator's sequence. The general contract of
* <tt>nextInt</tt> is that one <tt>int</tt> value in the specified range
* is pseudorandomly generated and returned. All <tt>n</tt> possible
* <tt>int</tt> values are produced with (approximately) equal
* probability. The method <tt>nextInt(int n)</tt> is implemented by
* class <tt>Random</tt> as follows:
* <blockquote><pre>
* public int nextInt(int n) {
* if (n<=0)
* throw new IllegalArgumentException("n must be positive");
*
* if ((n & -n) == n) // i.e., n is a power of 2
* return (int)((n * (long)next(31)) >> 31);
*
* int bits, val;
* do {
* bits = next(31);
* val = bits % n;
* } while(bits - val + (n-1) < 0);
* return val;
* }
* </pre></blockquote>
* <p>
* The hedge "approximately" is used in the foregoing description only
* because the next method is only approximately an unbiased source of
* independently chosen bits. If it were a perfect source of randomly
* chosen bits, then the algorithm shown would choose <tt>int</tt>
* values from the stated range with perfect uniformity.
* <p>
* The algorithm is slightly tricky. It rejects values that would result
* in an uneven distribution (due to the fact that 2^31 is not divisible
* by n). The probability of a value being rejected depends on n. The
* worst case is n=2^30+1, for which the probability of a reject is 1/2,
* and the expected number of iterations before the loop terminates is 2.
* <p>
* The algorithm treats the case where n is a power of two specially: it
* returns the correct number of high-order bits from the underlying
* pseudo-random number generator. In the absence of special treatment,
* the correct number of <i>low-order</i> bits would be returned. Linear
* congruential pseudo-random number generators such as the one
* implemented by this class are known to have short periods in the
* sequence of values of their low-order bits. Thus, this special case
* greatly increases the length of the sequence of values returned by
* successive calls to this method if n is a small power of two.
*
* @param n the bound on the random number to be returned. Must be
* positive.
* @return a pseudorandom, uniformly distributed <tt>int</tt>
* value between 0 (inclusive) and n (exclusive).
* @exception IllegalArgumentException n is not positive.
* @since 1.2
*/
public int nextInt(int n) {
if (n<=0)
throw new IllegalArgumentException("n must be positive");
if ((n & -n) == n) // i.e., n is a power of 2
return (int)((n * (long)next(31)) >> 31);
int bits, val;
do {
bits = next(31);
val = bits % n;
} while(bits - val + (n-1) < 0);
return val;
}
/**
* Returns the next pseudorandom, uniformly distributed <code>long</code>
* value from this random number generator's sequence. The general
* contract of <tt>nextLong</tt> is that one long value is pseudorandomly
* generated and returned. All 2<font size="-1"><sup>64</sup></font>
* possible <tt>long</tt> values are produced with (approximately) equal
* probability. The method <tt>nextLong</tt> is implemented by class
* <tt>Random</tt> as follows:
* <blockquote><pre>
* public long nextLong() {
* return ((long)next(32) << 32) + next(32);
* }</pre></blockquote>
*
* @return the next pseudorandom, uniformly distributed <code>long</code>
* value from this random number generator's sequence.
*/
public long nextLong() {
// it's okay that the bottom word remains signed.
return ((long)(next(32)) << 32) + next(32);
}
/**
* Returns the next pseudorandom, uniformly distributed
* <code>boolean</code> value from this random number generator's
* sequence. The general contract of <tt>nextBoolean</tt> is that one
* <tt>boolean</tt> value is pseudorandomly generated and returned. The
* values <code>true</code> and <code>false</code> are produced with
* (approximately) equal probability. The method <tt>nextBoolean</tt> is
* implemented by class <tt>Random</tt> as follows:
* <blockquote><pre>
* public boolean nextBoolean() {return next(1) != 0;}
* </pre></blockquote>
* @return the next pseudorandom, uniformly distributed
* <code>boolean</code> value from this random number generator's
* sequence.
* @since 1.2
*/
public boolean nextBoolean() {return next(1) != 0;}
/**
* Returns the next pseudorandom, uniformly distributed <code>float</code>
* value between <code>0.0</code> and <code>1.0</code> from this random
* number generator's sequence. <p>
* The general contract of <tt>nextFloat</tt> is that one <tt>float</tt>
* value, chosen (approximately) uniformly from the range <tt>0.0f</tt>
* (inclusive) to <tt>1.0f</tt> (exclusive), is pseudorandomly
* generated and returned. All 2<font size="-1"><sup>24</sup></font>
* possible <tt>float</tt> values of the form
* <i>m x </i>2<font size="-1"><sup>-24</sup></font>, where
* <i>m</i> is a positive integer less than 2<font size="-1"><sup>24</sup>
* </font>, are produced with (approximately) equal probability. The
* method <tt>nextFloat</tt> is implemented by class <tt>Random</tt> as
* follows:
* <blockquote><pre>
* public float nextFloat() {
* return next(24) / ((float)(1 << 24));
* }</pre></blockquote>
* The hedge "approximately" is used in the foregoing description only
* because the next method is only approximately an unbiased source of
* independently chosen bits. If it were a perfect source or randomly
* chosen bits, then the algorithm shown would choose <tt>float</tt>
* values from the stated range with perfect uniformity.<p>
* [In early versions of Java, the result was incorrectly calculated as:
* <blockquote><pre>
* return next(30) / ((float)(1 << 30));</pre></blockquote>
* This might seem to be equivalent, if not better, but in fact it
* introduced a slight nonuniformity because of the bias in the rounding
* of floating-point numbers: it was slightly more likely that the
* low-order bit of the significand would be 0 than that it would be 1.]
*
* @return the next pseudorandom, uniformly distributed <code>float</code>
* value between <code>0.0</code> and <code>1.0</code> from this
* random number generator's sequence.
*/
public float nextFloat() {
int i = next(24);
return i / ((float)(1 << 24));
}
/**
* Returns the next pseudorandom, uniformly distributed
* <code>double</code> value between <code>0.0</code> and
* <code>1.0</code> from this random number generator's sequence. <p>
* The general contract of <tt>nextDouble</tt> is that one
* <tt>double</tt> value, chosen (approximately) uniformly from the
* range <tt>0.0d</tt> (inclusive) to <tt>1.0d</tt> (exclusive), is
* pseudorandomly generated and returned. All
* 2<font size="-1"><sup>53</sup></font> possible <tt>float</tt>
* values of the form <i>m x </i>2<font size="-1"><sup>-53</sup>
* </font>, where <i>m</i> is a positive integer less than
* 2<font size="-1"><sup>53</sup></font>, are produced with
* (approximately) equal probability. The method <tt>nextDouble</tt> is
* implemented by class <tt>Random</tt> as follows:
* <blockquote><pre>
* public double nextDouble() {
* return (((long)next(26) << 27) + next(27))
* / (double)(1L << 53);
* }</pre></blockquote><p>
* The hedge "approximately" is used in the foregoing description only
* because the <tt>next</tt> method is only approximately an unbiased
* source of independently chosen bits. If it were a perfect source or
* randomly chosen bits, then the algorithm shown would choose
* <tt>double</tt> values from the stated range with perfect uniformity.
* <p>[In early versions of Java, the result was incorrectly calculated as:
* <blockquote><pre>
* return (((long)next(27) << 27) + next(27))
* / (double)(1L << 54);</pre></blockquote>
* This might seem to be equivalent, if not better, but in fact it
* introduced a large nonuniformity because of the bias in the rounding
* of floating-point numbers: it was three times as likely that the
* low-order bit of the significand would be 0 than that it would be
* 1! This nonuniformity probably doesn't matter much in practice, but
* we strive for perfection.]
*
* @return the next pseudorandom, uniformly distributed
* <code>double</code> value between <code>0.0</code> and
* <code>1.0</code> from this random number generator's sequence.
*/
public double nextDouble() {
long l = ((long)(next(26)) << 27) + next(27);
return l / (double)(1L << 53);
}
private double nextNextGaussian;
private boolean haveNextNextGaussian = false;
/**
* Returns the next pseudorandom, Gaussian ("normally") distributed
* <code>double</code> value with mean <code>0.0</code> and standard
* deviation <code>1.0</code> from this random number generator's sequence.
* <p>
* The general contract of <tt>nextGaussian</tt> is that one
* <tt>double</tt> value, chosen from (approximately) the usual
* normal distribution with mean <tt>0.0</tt> and standard deviation
* <tt>1.0</tt>, is pseudorandomly generated and returned. The method
* <tt>nextGaussian</tt> is implemented by class <tt>Random</tt> as follows:
* <blockquote><pre>
* synchronized public double nextGaussian() {
* if (haveNextNextGaussian) {
* haveNextNextGaussian = false;
* return nextNextGaussian;
* } else {
* double v1, v2, s;
* do {
* v1 = 2 * nextDouble() - 1; // between -1.0 and 1.0
* v2 = 2 * nextDouble() - 1; // between -1.0 and 1.0
* s = v1 * v1 + v2 * v2;
* } while (s >= 1 || s == 0);
* double multiplier = Math.sqrt(-2 * Math.log(s)/s);
* nextNextGaussian = v2 * multiplier;
* haveNextNextGaussian = true;
* return v1 * multiplier;
* }
* }</pre></blockquote>
* This uses the <i>polar method</i> of G. E. P. Box, M. E. Muller, and
* G. Marsaglia, as described by Donald E. Knuth in <i>The Art of
* Computer Programming</i>, Volume 2: <i>Seminumerical Algorithms</i>,
* section 3.4.1, subsection C, algorithm P. Note that it generates two
* independent values at the cost of only one call to <tt>Math.log</tt>
* and one call to <tt>Math.sqrt</tt>.
*
* @return the next pseudorandom, Gaussian ("normally") distributed
* <code>double</code> value with mean <code>0.0</code> and
* standard deviation <code>1.0</code> from this random number
* generator's sequence.
*/
synchronized public double nextGaussian() {
// See Knuth, ACP, Section 3.4.1 Algorithm C.
if (haveNextNextGaussian) {
haveNextNextGaussian = false;
return nextNextGaussian;
} else {
double v1, v2, s;
do {
v1 = 2 * nextDouble() - 1; // between -1 and 1
v2 = 2 * nextDouble() - 1; // between -1 and 1
s = v1 * v1 + v2 * v2;
} while (s >= 1 || s == 0);
double multiplier = Math.sqrt(-2 * Math.log(s)/s);
nextNextGaussian = v2 * multiplier;
haveNextNextGaussian = true;
return v1 * multiplier;
}
}
/**
* Serializable fields for Random.
*
* @serialField seed long;
* seed for random computations
* @serialField nextNextGaussian double;
* next Gaussian to be returned
* @serialField haveNextNextGaussian boolean
* nextNextGaussian is valid
*/
private static final ObjectStreamField[] serialPersistentFields = {
new ObjectStreamField("seed", Long.TYPE),
new ObjectStreamField("nextNextGaussian", Double.TYPE),
new ObjectStreamField("haveNextNextGaussian", Boolean.TYPE)
};
/**
* Reconstitute the <tt>Random</tt> instance from a stream (that is,
* deserialize it). The seed is read in as long for
* historical reasons, but it is converted to an AtomicLong.
*/
private void readObject(java.io.ObjectInputStream s)
throws java.io.IOException, ClassNotFoundException {
ObjectInputStream.GetField fields = s.readFields();
long seedVal;
seedVal = (long) fields.get("seed", -1L);
if (seedVal < 0)
throw new java.io.StreamCorruptedException(
"Random: invalid seed");
seed = new AtomicLong(seedVal);
nextNextGaussian = fields.get("nextNextGaussian", 0.0);
haveNextNextGaussian = fields.get("haveNextNextGaussian", false);
}
/**
* Save the <tt>Random</tt> instance to a stream.
* The seed of a Random is serialized as a long for
* historical reasons.
*
*/
synchronized private void writeObject(ObjectOutputStream s) throws IOException {
// set the values of the Serializable fields
ObjectOutputStream.PutField fields = s.putFields();
fields.put("seed", seed.get());
fields.put("nextNextGaussian", nextNextGaussian);
fields.put("haveNextNextGaussian", haveNextNextGaussian);
// save them
s.writeFields();
}
}
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