Random (Java Platform SE 6) (original) (raw)

java.util

Class Random

java.lang.Object java.util.Random

All Implemented Interfaces:

Serializable

Direct Known Subclasses:

SecureRandom

public class Random

extends Object

implements Serializable

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 3, 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 aprotected utility method that on each invocation can supply up to 32 pseudorandomly generated bits.

Many applications will find the method Math.random() simpler to use.

Since:

1.0

See Also:

Serialized Form

Random

public Random()

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.

Random

public Random(long seed)

Creates a new random number generator using a single long seed. The seed is the initial value of the internal state of the pseudorandom number generator which is maintained by method next(int).

The invocation new Random(seed) is equivalent to:

Random rnd = new Random(); rnd.setSeed(seed);

Parameters:

seed - the initial seed

See Also:

setSeed(long)

setSeed

public void setSeed(long seed)

Sets the seed of this random number generator using a singlelong 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 methodsetSeed is implemented by class Random by atomically updating the seed to

(seed ^ 0x5DEECE66DL) & ((1L << 48) - 1)

and clearing the haveNextNextGaussian flag used by nextGaussian().

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.

Parameters:

seed - the initial seed

next

protected int next(int bits)

Generates the next pseudorandom number. Subclasses should override this, as this is used by all other methods.

The general contract of next is that it returns anint value and if the argument bits is between1 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 by atomically updating the seed to

(seed * 0x5DEECE66DL + 0xBL) & ((1L << 48) - 1)

and returning

(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 3:Seminumerical Algorithms, section 3.2.1.

Parameters:

bits - random bits

Returns:

the next pseudorandom value from this random number generator's sequence

Since:

1.1

nextBytes

public void nextBytes(byte[] bytes)

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.

The method nextBytes is implemented by class Random as if by:

public void nextBytes(byte[] bytes) { for (int i = 0; i < bytes.length; ) for (int rnd = nextInt(), n = Math.min(bytes.length - i, 4); n-- > 0; rnd >>= 8) bytes[i++] = (byte)rnd; }

Parameters:

bytes - the byte array to fill with random bytes

Throws:

[NullPointerException](../../java/lang/NullPointerException.html "class in java.lang") - if the byte array is null

Since:

1.1

nextInt

public int nextInt()

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 if by:

public int nextInt() { return next(32); }

Returns:

the next pseudorandom, uniformly distributed int value from this random number generator's sequence

nextInt

public int nextInt(int n)

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 ofnextInt is that one int value in the specified range is pseudorandomly generated and returned. All n possibleint values are produced with (approximately) equal probability. The method nextInt(int n) is implemented by class Random as if by:

`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.

Parameters:

n - the bound on the random number to be returned. Must be positive.

Returns:

the next pseudorandom, uniformly distributed int value between 0 (inclusive) and n (exclusive) from this random number generator's sequence

Throws:

[IllegalArgumentException](../../java/lang/IllegalArgumentException.html "class in java.lang") - if n is not positive

Since:

1.2

nextLong

public long nextLong()

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.

The method nextLong is implemented by class Random as if by:

public long nextLong() { return ((long)next(32) << 32) + next(32); }

Because class Random uses a seed with only 48 bits, this algorithm will not return all possible long values.

Returns:

the next pseudorandom, uniformly distributed long value from this random number generator's sequence

nextBoolean

public boolean nextBoolean()

Returns the next pseudorandom, uniformly distributedboolean value from this random number generator's sequence. The general contract of nextBoolean is that oneboolean 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 if by:

public boolean nextBoolean() { return next(1) != 0; }

Returns:

the next pseudorandom, uniformly distributedboolean value from this random number generator's sequence

Since:

1.2

nextFloat

public float nextFloat()

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 onefloat 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 if by:

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 of 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.]

Returns:

the next pseudorandom, uniformly distributed float value between 0.0 and 1.0 from this random number generator's sequence

nextDouble

public double nextDouble()

Returns the next pseudorandom, uniformly distributeddouble value between 0.0 and1.0 from this random number generator's sequence.

The general contract of nextDouble is that onedouble value, chosen (approximately) uniformly from the range 0.0d (inclusive) to 1.0d (exclusive), is pseudorandomly generated and returned.

The method nextDouble is implemented by class Random as if by:

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 of randomly chosen bits, then the algorithm shown would choosedouble 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.]

Returns:

the next pseudorandom, uniformly distributed double value between 0.0 and 1.0 from this random number generator's sequence

See Also:

Math.random()

nextGaussian

public double nextGaussian()

Returns the next pseudorandom, Gaussian ("normally") distributeddouble value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence.

The general contract of nextGaussian is that onedouble value, chosen from (approximately) the usual normal distribution with mean 0.0 and standard deviation1.0, is pseudorandomly generated and returned.

The method nextGaussian is implemented by classRandom as if by a threadsafe version of the following:

`private double nextNextGaussian; private boolean haveNextNextGaussian = false;

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 = StrictMath.sqrt(-2 * StrictMath.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 3: 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 StrictMath.log and one call to StrictMath.sqrt.

Returns:

the next pseudorandom, Gaussian ("normally") distributeddouble value with mean 0.0 and standard deviation 1.0 from this random number generator's sequence

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