# RANDU: The case of the bad RNG

**R – Why?**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

The German Federal Office for Information Security (BSI) has established

criteria for quality random number generator (rng):

- A sequence of random numbers has a high probability of containing no identical consecutive elements.
- A sequence of numbers which is indistinguishable from true random’ numbers (tested using statistical tests.
- It should be impossible to calculate, or guess, from any given sub-sequence, any previous or future values in the sequence.
- It should be impossible, for all practical purposes, for an attacker to calculate, or guess the values used in the random number algorithm.

Points 3 and 4 are crucial for many applications. Everytime you make a

phone call, contact to a wireless point, pay using your credit card random

numbers are used.

Designing a good random number generator is hard and as a general rule you should never try to. **R** comes with many good quality random generators. The default generator is the Mersenne-Twister. This rng has a huge period of (how many random numbers are generated before we have a repeat).

## Linear congruential generators

A linear congruential generator (lcg) is a relatively simple rng (popular in the 60’s and 70’s). It has a simple form of

where $latexr_0$ is the initial number, known as the *seed*, and (a,b,m) are the *multiplier*, *additive constant* and *modulo* respectively. The parameters are all integers.

The modulo operation means that at most different numbers can be generated

before the sequence must repeat – namely the integers . The

actual number of generated numbers is , called the *period* of

the generator.

The key to random number generators is in setting the parameters.

### RANDU

RANDU was a lcg with parameters and . Unfortunately this is a **spectacularly** bad choice of

parameters. On noting that , then

So

On expanding the square, we get

Note: all these calculations should be to the mod . So there is a large

correlation between the three points!

If compare randu to a standard rng (code in a gist)

It’s obvious that randu doesn’t produce good random numbers. Plotting , and in 3d

### Generating the graphics

The code is all in a gist and can be run via

```
devtools::source_gist("https://gist.github.com/csgillespie/0ba4bbd8da0d1264b124")
```

You can then get the 3d plot via

```
scatterplot3d::scatterplot3d(randu[,1], randu[,2], randu[,3],
angle=154)
## Interactive version
threejs::scatterplot3js(randu[,1], randu[,2], randu[,3])
```

**leave a comment**for the author, please follow the link and comment on their blog:

**R – Why?**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.