Have you ever thought how we can generate random numbers mathematically?

In this article, we are going to present simple algorithms for generating pseudo-random sequences and we will discuss about how we can measure the complexity (i.e. the randomness) of a sequence of numbers.

We will see that indeed, it is complex to measure complexity!

We will illustrate the mathematical idea of randomness by coding the algorithms and the formulas in javascript .

Even if you are not 100% familiar with javascript, you should be able to enjoy this article.

As usual, all the code snippets of this article are interactive - powered by the KLIPSE plugin. Feel free to modify the code in order to get a better feeling of the concepts.

Algorithms for generating random numbers

Generating a sequence from seed an next

The simplest way to generate a pseudo-random sequence is to start from a seed number and to provide a next function that receives a number and return the next number of the sequence.

Here is a function that generate a pseudo-random sequence of length n :

generateSequence = (n, seed, next) => { let seq = new Array(n); seq[0] = seed; for(let i=1; i< n; i++) { seq[i] = next(seq[i-1]) } return seq }

Linear Congruency

Linear congruency formula is parametrized by:

k : the coefficent

: the coefficent c : the offset

: the offset m : the period

Here is the code for linear congruency:

(Don’t be afraid by the fact that linearCong is a function that returns a function!)

linearCong = (k, c, m) => (prev) => (k*prev + c) % m

Let’s see linearCong in action:

linearCongExample = linearCong(19, 51, 101) linearCongExample(231)

Now, we can generate a pseudo-random sequence using the linear congruency formula as our next function:

(Don’t be afraid by the fact that we are using ES6 destructuring syntax!)

linearCongRandomSeq = ({size, seed, k, c, m}) => generateSequence(size, seed, linearCong(k,c,m))

Let’s see linearCongRandomSeq in action:

linearCongRandomSeq({size: 10, seed: 22, k: 19, c: 3, m:54})

Logistic Regression Formula

Logistic Regression is another possible formula for generating pseudo-random sequences:

Here is the formula (parametrized with r ):

regression = (r) => (prev) => r*prev*(1-prev)

As before, let’s generate a sequence using the logistic regression formula (with r =3.57):

generateSequence(5, 0.5, regression(3.57))

Feel free to play with the value of r

The problem with logistic regression is that the values are between 0 and 1 .

But its’s very simple to scale the values of the sequence and transform the values to integers, by mapping each value of the sequence:

logisiticRandomSeq = ({size, seed, r, max}) => generateSequence(size, seed, regression(r)).map(x => Math.round(max*x))

Let’s see it in action:

logisiticRandomSeq({size: 10, seed: 0.5, r: 3.57, max: 1e9})

Feel free to play with all the parameters - but be careful with size . If you take a value that is too bug, your browser might crash…

Javascript random function

In javascript , the language provides a Math.random function for generating random numbers between 0 and 1 .

Here is how we can plug Math.random into our random sequence generation mechanism:

rand = (max) => Math.round(max*Math.random()) mathRandomSeq = ({size, max}) => generateSequence(size, rand(max), () => rand(max))

Let’s see mathRandomSeq in action:

mathRandomSeq({size: 10, max: 1e9})

Now, that we have seen how to generate pseudo-random sequences, let’s see how we can measure the complexity of the sequence. By complexity, we mean the “level of randomness” of the sequence.

How to measure complexity?

Period

One simple way to assess the complexity of a sequence, is to count the number of unique elements - the period - of the sequence. If the elemets repeat themselves, the period will be lower than the size of the sequence.

In javascript , we have the mathematical sets available in the language.

In a set duplicate elements are removed.

Therefore, in order to count the number of unique elements, we can convert our sequence into a set and count the number of elements in the resulting set.

period = (seq) => (new Set(seq)).size

Period of Linear Congruency sequences

Let’s measure the period of psedo-random sequences generated using linear congruency:

period(linearCongRandomSeq({size: 100, seed: 22, k: 19, c: 3, m:54}))

Obviously, the period can never be greater than the value of m !

Exercise: Can you find a good combination of k , c and m ? By good combination, we mean a combination that generate a sequence whose period is very close to m .

Period of Logistic Regression sequences

Logistic regression sequences give better results, when we chose an appropriate value for r :

period(logisiticRandomSeq({size: 1e2, seed: 0.5, r: 3.57, max: 1e4}))

Period of javascript random sequences

Sequences generated with the javascript Math.random function seem to perfom even better:

period(mathRandomSeq({size: 1e2, max: 1e4}))

A more precise way of measuring complexity is by checking how much the values of the sequence share common properties. For instance, we can visualize the distribution of the values modulo n . If the sequence is perfectly random, then the values modulo n should be distributed uniformly.

Distribution of the values modulo 10

Let’s write a function that calculates the distribution of a sequence modulo 10 .

More precisely, the function receives an array of arbitrary length and return an array of length 10 where the i th element of the array contains the frequency (in percentage) of the values x of the sequence such that x mod 10 = i .

Here is the code using map and countBy provided by Ramda.js:

measureRandomness = (seq) => R.map(x => 100*x/seq.length, R.countBy(x => x % 10)(seq))

Let’s see it in action by measuring the randomness of the sequence of integers from 0 to 999:

measureRandomness(R.range(0, 1000))

Now, let’s visualize the randomness in a Column chart powered by google charts:

visualizeRandomness = (seq, name) => ({ dataTable: [["n", "%"], ...Object.entries(measureRandomness(seq) )], options: { showRowNumber: true, width: '100%', height: '100%', title: `The repartition of the elements of a sequence created by ${name}`, vAxis: { title: 'percentage', viewWindow : { min: 0, } }, hAxis: { title: 'bin' } }, chartType: "ColumnChart" })

Let’s visualize the randomness of our linear congruency sequence:

visualizeRandomness(linearCongRandomSeq({size: 10, seed: 22, k: 19, c: 3, m:54}), 'Linear Congruency')

Feel free to play with the parameters of linearCongRandomSeq and see how the chart updates auto-magically.

Let’s visualize the randomness of our logistic regression sequence:

visualizeRandomness(logisiticRandomSeq({size: 10, seed: 0.5, r: 3.57, max: 1e9}), 'Logistic Regression')

Feel free to play with the parameters of logisiticRandomSeq and see how the chart updates auto-magically.

Let’s visualize the randomness of the javacript random sequence:

visualizeRandomness(mathRandomSeq({size: 1e6, max: 1e9}), 'Math.random()')