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.

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

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

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)); 
}

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

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);

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)); 
}

[In early versions of Java, the result was incorrectly calculated as:

 
return next(30) / ((float)(1 << 30));

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; 
} 
}

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

 
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.


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. 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); 
}

 
synchronized public void setSeed(long seed) { 
this.seed = (seed ^ 0x5DEECE66DL) & ((1L << 48) - 1); 
haveNextNextGaussian = false; 
}