java.util.Random Maven / Gradle / Ivy
/*
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This work corresponds to the API signatures of JSR 219: Foundation
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*/
package java.util;
/**
* 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,
* The Art of Computer Programming, Volume 2, Section 3.2.1.)
*
* If two instances of Random 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 Random. Java implementations must use all the algorithms
* shown here for the class Random, for the sake of absolute
* portability of Java code. However, subclasses of class Random
* are permitted to use other algorithms, so long as they adhere to the
* general contracts for all the methods.
*
* The algorithms implemented by class Random use a
* protected utility method that on each invocation can supply
* up to 32 pseudorandomly generated bits.
*
* Many applications will find the random method in
* class Math simpler to use.
*
* @author Frank Yellin
* @version 1.34, 02/02/00
* @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 long seed;
private double nextNextGaussian;
private boolean haveNextNextGaussian;
/**
* Creates a new random number generator. Its seed is initialized to
* a value based on the current time:
*
* public Random() { this(System.currentTimeMillis()); }
* Two Random objects created within the same millisecond will have
* the same sequence of random numbers.
*
* @see java.lang.System#currentTimeMillis()
*/
public Random() { }
/**
* Creates a new random number generator using a single
* long seed:
*
* public Random(long seed) { setSeed(seed); }
* Used by method next to hold
* the state of the pseudorandom number generator.
*
* @param seed the initial seed.
* @see java.util.Random#setSeed(long)
*/
public Random(long seed) { }
/**
* Sets the seed of this random number generator using a single
* long seed. The general contract of setSeed
* 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 seed as a seed. The method
* setSeed is implemented by class Random as follows:
*
* synchronized public void setSeed(long seed) {
* this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1);
* haveNextNextGaussian = false;
* }
* The implementation of setSeed by class Random
* 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.
*/
public synchronized void setSeed(long seed) { }
/**
* Generates the next pseudorandom number. Subclass should
* override this, as this is used by all other methods.
* The general contract of next is that it returns an
* int value and if the argument bits is between 1
* and 32 (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
* 0 or 1. The method next is implemented
* by class Random as follows:
*
* synchronized protected int next(int bits) {
* seed = (seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1);
* return (int)(seed >>> (48 - bits));
* }
* This is a linear congruential pseudorandom number generator, as
* defined by D. H. Lehmer and described by Donald E. Knuth in The
* Art of Computer Programming, Volume 2: Seminumerical
* Algorithms, section 3.2.1.
*
* @param bits random bits
* @return the next pseudorandom value from this random number generator's sequence.
* @since JDK1.1
*/
protected synchronized int next(int bits) {
return 0;
}
/**
* 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) { }
/**
* Returns the next pseudorandom, uniformly distributed int
* value from this random number generator's sequence. The general
* contract of nextInt is that one int value is
* pseudorandomly generated and returned. All 232
* possible int values are produced with
* (approximately) equal probability. The method nextInt is
* implemented by class Random as follows:
*
* public int nextInt() { return next(32); }
*
* @return the next pseudorandom, uniformly distributed int
* value from this random number generator's sequence.
*/
public int nextInt() {
return 0;
}
/**
* Returns a pseudorandom, uniformly distributed int value
* between 0 (inclusive) and the specified value (exclusive), drawn from
* this random number generator's sequence. The general contract of
* nextInt is that one int value in the specified range
* is pseudorandomly generated and returned. All n possible
* int values are produced with (approximately) equal
* probability. The method nextInt(int n) is implemented by
* class Random as follows:
*
* 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;
* }
*
*
* 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 int
* values from the stated range with perfect uniformity.
*
* 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.
*
* 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 low-order 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 int
* value between 0 (inclusive) and n (exclusive).
* @exception IllegalArgumentException n is not positive.
* @since 1.2
*/
public int nextInt(int n) {
return 0;
}
/**
* Returns the next pseudorandom, uniformly distributed long
* value from this random number generator's sequence. The general
* contract of nextLong is that one long value is pseudorandomly
* generated and returned. All 264
* possible long values are produced with (approximately) equal
* probability. The method nextLong is implemented by class
* Random as follows:
*
* public long nextLong() {
* return ((long)next(32) << 32) + next(32);
* }
*
* @return the next pseudorandom, uniformly distributed long
* value from this random number generator's sequence.
*/
public long nextLong() {
return -1;
}
/**
* Returns the next pseudorandom, uniformly distributed
* boolean value from this random number generator's
* sequence. The general contract of nextBoolean is that one
* boolean value is pseudorandomly generated and returned. The
* values true and false are produced with
* (approximately) equal probability. The method nextBoolean is
* implemented by class Random as follows:
*
* public boolean nextBoolean() {return next(1) != 0;}
*
* @return the next pseudorandom, uniformly distributed
* boolean value from this random number generator's
* sequence.
* @since 1.2
*/
public boolean nextBoolean() {
return false;
}
/**
* Returns the next pseudorandom, uniformly distributed float
* value between 0.0 and 1.0 from this random
* number generator's sequence.
* The general contract of nextFloat is that one float
* value, chosen (approximately) uniformly from the range 0.0f
* (inclusive) to 1.0f (exclusive), is pseudorandomly
* generated and returned. All 224
* possible float values of the form
* m x 2-24, where
* m is a positive integer less than 224
* , are produced with (approximately) equal probability. The
* method nextFloat is implemented by class Random as
* follows:
*
* public float nextFloat() {
* return next(24) / ((float)(1 << 24));
* }
* 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 float
* values from the stated range with perfect uniformity.
* [In early versions of Java, the result was incorrectly calculated as:
*
* return next(30) / ((float)(1 << 30));
* 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 float
* value between 0.0 and 1.0 from this
* random number generator's sequence.
*/
public float nextFloat() {
return 0.0f;
}
/**
* Returns the next pseudorandom, uniformly distributed
* double value between 0.0 and
* 1.0 from this random number generator's sequence.
* The general contract of nextDouble is that one
* double value, chosen (approximately) uniformly from the
* range 0.0d (inclusive) to 1.0d (exclusive), is
* pseudorandomly generated and returned. All
* 253 possible float
* values of the form m x 2-53
* , where m is a positive integer less than
* 253, are produced with
* (approximately) equal probability. The method nextDouble is
* implemented by class Random as follows:
*
* public double nextDouble() {
* return (((long)next(26) << 27) + next(27))
* / (double)(1L << 53);
* }
* 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
* double values from the stated range with perfect uniformity.
*
[In early versions of Java, the result was incorrectly calculated as:
*
* return (((long)next(27) << 27) + next(27))
* / (double)(1L << 54);
* 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
* double value between 0.0 and
* 1.0 from this random number generator's sequence.
*/
public double nextDouble() {
return 0.0d;
}
/**
* Returns the next pseudorandom, Gaussian ("normally") distributed
* double value with mean 0.0 and standard
* deviation 1.0 from this random number generator's sequence.
*
* The general contract of nextGaussian is that one
* double value, chosen from (approximately) the usual
* normal distribution with mean 0.0 and standard deviation
* 1.0, is pseudorandomly generated and returned. The method
* nextGaussian is implemented by class Random as follows:
*
* 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;
* }
* }
* This uses the polar method of G. E. P. Box, M. E. Muller, and
* G. Marsaglia, as described by Donald E. Knuth in The Art of
* Computer Programming, Volume 2: Seminumerical Algorithms,
* section 3.4.1, subsection C, algorithm P. Note that it generates two
* independent values at the cost of only one call to Math.log
* and one call to Math.sqrt.
*
* @return the next pseudorandom, Gaussian ("normally") distributed
* double value with mean 0.0 and
* standard deviation 1.0 from this random number
* generator's sequence.
*/
public synchronized double nextGaussian() {
return 0.0d;
}
}