6

Reference: link text

i cannot understand the following line , can anybody provide me some example for the below statement?

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

Lalchand
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  • Following that statement is quite straightforward. `Random r1, r2`, create and initialize with the same seed (see documentation). Call `next` 5 times on each. They'll return the same exact sequence of 5 "random" numbers. – polygenelubricants Aug 27 '10 at 11:22

6 Answers6

15

Since you asked for an example:

import java.util.Random;
public class RandomTest {
    public static void main(String[] s) {
        Random rnd1 = new Random(42);
        Random rnd2 = new Random(42);

        System.out.println(rnd1.nextInt(100)+" - "+rnd2.nextInt(100));
        System.out.println(rnd1.nextInt()+" - "+rnd2.nextInt());
        System.out.println(rnd1.nextDouble()+" - "+rnd2.nextDouble());
        System.out.println(rnd1.nextLong()+" - "+rnd2.nextLong());
    }
}

Both Random instances will always have the same output, no matter how often you run it, no matter what platform or what Java version you use:

30 - 30
234785527 - 234785527
0.6832234717598454 - 0.6832234717598454
5694868678511409995 - 5694868678511409995
Michael Borgwardt
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8

The random generator is deterministic. Given the same input to Random and the same usage of the methods in Random, the sequence of pseudo-random numbers returned to your program will be the same even in different runs on different machines.

This is why it is pseudo-random - the numbers returned behave statistically like random numbers except they can be reliably predicted. True random numbers are unpredictable.

Thorbjørn Ravn Andersen
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3

The Random class basically is a Psuedorandom Number Generator (also known as Deterministic random bit generator) that generates a sequence of numbers that approximates the properties of random numbers. It's not generally random but deterministic as it can be determined by small random states in the generator (such as seed). Because of the deterministic nature, you can generate identical result if you the sequence of methods and seeds are identical on 2 generators.

Buhake Sindi
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2

The numbers are not really random, given the same starting conditions (the seed) and the same sequence of operations, the same sequence of numbers will be generated. This is why it would not be a good iea to use the basic Random class as part of any cryptograhic or security related code since it may be possible for an attacker to figure out which sequnce is being generated and predict future numbers.

For a random number generator that emits non-deterministic values, take a look at SecureRandom.

See Random number generation, Computational methods on wikipedia for more info.

Daniel Renshaw
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This means that when you create the Random object (e.g. at the start of your program), you will probably want to start with a new seed. Mostly people choose some time related value, such as the number of ticks.

The fact that the number sequences are the same given the same seed is actually very convenient if you want to debug your program: make sure you log the seed value and if something is wrong you can restart the program in the debugger using that same seed value. This means you can replay the scenario exactly. This would be impossible if you would (could) use a true random number generator.

Emile
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0

With the same seed value, separate instances of Random will return/generate the same sequence of random numbers; more on this here: http://www.particle.kth.se/~lindsey/JavaCourse/Book/Part1/Tech/Chapter04/javaRandNums.html

Ruby Example:

class LCG; def initialize(seed=Time.now.to_i, a=2416, b=374441, m=1771075); @x, @a, @b, @m = seed % m, a, b, m; end; def next(); @x = (@a * @x + @b) % @m; end; end

irb(main):004:0> time = Time.now.to_i
=> 1282908389

irb(main):005:0> r = LCG.new(time)
=> #<LCG:0x0000010094f578 @x=650089, @a=2416, @b=374441, @m=1771075>
irb(main):006:0> r.next
=> 45940
irb(main):007:0> r.next
=> 1558831
irb(main):008:0> r.next
=> 1204687
irb(main):009:0> f = LCG.new(time)
=> #<LCG:0x0000010084cb28 @x=650089, @a=2416, @b=374441, @m=1771075>
irb(main):010:0> f.next
=> 45940
irb(main):011:0> f.next
=> 1558831
irb(main):012:0> f.next
=> 1204687

Based on the values a/b/m, the result will be the same for a given seed. This can be used to generate the same "random" number in two places and both sides can depend on getting the same result. This can be useful for encryption; although obviously, this algorithm isn't cryptographically secure.

nessence
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