Uncommons Maths is a Java library consisting of a comprehensive random numbers package and other useful mathematical utility classes. It was originally part of the Watchmaker Framework for Evolutionary Computation but, due to its usefulness in other domains, it has now been converted into a standalone project (Apache Licence).

This article briefly describes what’s available in this first public release. I am most definitely not a mathematician and, as such, this library is written by a programmer for programmers (but if mathematicians find it useful that’s good too). It includes classes that are useful in real world programs and is not intended to ever cover the full spectrum of mathematics. However, I hope that it will expand in scope over time in this spirit of pragmatism. To that end, suggestions and contributions are actively encouraged.

### Random Number Generators

The Uncommons Maths library provides three easy-to-use, statistically-sound, high-performance pseudorandom number generators (RNGs). They are:

**MersenneTwisterRNG**- A Java port of the fast and reliable Mersenne Twister RNG originally developed by Makoto Matsumoto and Takuji Nishimura. This is faster
^{1}than java.util.Random and does not have the statistical flaws^{2}of that RNG. **CellularAutomatonRNG**- A Java port of Tony Pasqualoni’s ultra-fast Cellular Automaton RNG. It uses a 256-cell automaton to generate random values. To the best of my knowledge, this is the fastest
^{1}available pure Java RNG that completes the Diehard test suite without any problems. **AESCounterRNG**- This is a cryptographically-strong
^{3}non-linear RNG that is around 10x faster^{1}than java.security.SecureRandom. Reverse-engineering the generator state from observations of its output would involve cracking the AES block cipher.

- A benchmark comparing the performance of these three RNGs and the two JDK RNGs can be found here (under the title “RNG Performance”).
- This applet demonstrates the non-randomness of java.util.Random.
- The algorithm is not the only security consideration for RNGs. The source, secrecy and integrity of the seed data is also vital. For highly sensitive applications, consider using something like Fortuna.

### Probability Distributions

Using the included probability distribution wrappers, these RNGs (and the standard JDK ones) can be used to generate values from Uniform, Normal, Binomial, Poisson and Exponential distributions.

### Permutations & Combinations

Uncommons Maths also includes generics-enabled combination and permutation generators. These are based on Java classes originally written by Michael Gilleland.

### Statistics

Uncommons Maths provides a statistical data set class that can calculate a variety of descriptive statistics (variance, median, standard deviation, arithmetic and geometric means, etc.) for a set of values.

### Other

Other useful features in Uncommons Maths include utility methods to complement those in java.lang.Math, and utility classes for manipulating binary data.