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`java.util.Random`

`Random`

serves as a separate, independent pseudorandom
generator of primitive values.
public classIf two`{ protected long`

Random`; protected double`

seed`; protected boolean`

nextNextGaussian`= false; public`

haveNextNextGaussian`(); public`

Random`(long seed); public void`

Random`(long seed); protected int`

setSeed`(int bits); public int`

next`(); public long`

nextInt`(); public float`

nextLong`(); public double`

nextFloat`(); public double`

nextDouble`(); }`

nextGaussian

`Random`

objects 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 all Java implementations. 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 use other algorithms, so long as they adhere to the general contracts for all the methods.
The algorithms implemented by class `Random`

use three state variables, which are `protected`

. They also use a `protected`

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

**21.9.1 ** `protected long `

;**seed**

A variable used by method `next`

(§21.9.7) to hold the state of the pseudorandom
number generator.

**21.9.2 ** `protected double `

;**nextNextGaussian**

A variable used by method `nextGaussian`

(§21.9.12) to hold a precomputed
value to be delivered by that method the next time it is called.

**21.9.3 ** `protected boolean `

= false;**haveNextNextGaussian**

A variable used by method `nextGaussian`

(§21.9.12) to keep track of whether it
has precomputed and stashed away the next value to be delivered by that method.

**21.9.4 ** `public `

()**Random**

This constructor initializes a newly created `Random`

number generator by using the
current time of day (§20.18.6) as a seed.

public Random() { this(System.currentTimeMillis()); }

**21.9.5 ** `public `

(long seed)**Random**

This constructor initializes a newly created `Random`

number generator by using the
argument `seed`

as a seed.

public Random(long seed) { setSeed(seed); }

**21.9.6 ** `public void `

(long seed)**setSeed**

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.
[In certain early versions of Java, the `setSeed`

method failed to reset the value of `haveNextNextGaussian`

to `false`

; this flaw could lead to failure to produce repeatable behavior.]

**21.9.7 ** `protected int `

(int bits)**next**

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

**21.9.8 ** `public int `

()**nextInt**

The general contract of `nextInt`

is that one `int`

value is pseudorandomly generated
and returned. All possible `int`

values are produced with (approximately)
equal probability.

The method `setSeed`

is implemented by class `Random`

as follows:

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

**21.9.9 ** `public long `

()**nextLong**

The general contract of `nextLong`

is that one `long`

value is pseudorandomly generated
and returned. All possible `long`

values are produced with (approximately)
equal probability.

The method `setSeed`

is implemented by class `Random`

as follows:

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

**21.9.10 ** `public float `

()**nextFloat**

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 possible `float`

values of the form
, where *m* is a positive integer less than , are produced with (approximately)
equal probability.

The method `setSeed`

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`

.]
**21.9.11 ** `public double `

()**nextDouble**

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 possible `float`

values of the form
, where *m* is a positive integer less than , are produced with (approximately)
equal probability.

The method `setSeed`

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.]
**21.9.12 ** `public double `

()**nextGaussian**

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 `setSeed`

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); double norm = Math.sqrt(-2 * Math.log(s)/s); nextNextGaussian = v2 * norm; haveNextNextGaussian = true; return v1 * norm; } }This uses the

`Math.log`

and one
call to `Math.sqrt`

.

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