Chris Martin

Randomization pipeline

One disappointing aspect of my CS experience is that I don’t get to write many interesting algorithms for real life. I think I came into this field expecting to see a lot of abstract computing problems requiring clever solutions. What I’ve experienced, however, is that most computing exercises tends to lean heavily to one side of the theory-practice spectrum. A math-centric discussion of a clever algorithm may deal with proofs of correctness or asymptotic runtime, but will rarely consider its practical application.

In the software engineering world, we focus on higher-level architecture, and sometimes use the term clever pejoratively. The software goal is to abstract out the algorithms entirely so we can leave those pesky implementation details to libraries written by people with smaller Erdős numbers and greater eccentricities.

There is certainly nothing wrong with this mentality. Writing javascript for the web without a framework may be more fun for a while, but it is ultimately foolish. You won’t be able to address nearly as many browser bugs within the scope of your project as the library maintainers have, and you don’t want to waste time replicating that effort anyway. Although it can be temptingly enjoyable to delve into the murky depths of writing low-level code and implementing your own generic data structures, I’ve had a hard time finding opportunities to get my hands dirty with anything outside of purely academic endeavours.

So I get excited when I encounter a real problem with a nontrivial solution involving math and progamming. I want to select a random file from some part of the filesystem, so I need a script (let’s call it choose) that can pick a random line from its input stream, so that I can get a random file with:

find -type f | choose

This problem poses no actual difficulty, of course - the obvious solution is to read all n lines, pick a random number i uniformly on [1, n], and print the ith line:



while read x; do
  i=$((i + 1))

echo "${all[$((RANDOM % i))]}"

But this just doesn’t feel like it follows the Way of Unix, because it requires holding the entire list in memory just to eventually write a single line. We’re piping streams without using the pipelining to our advantage. I’d like to be able to do this using constant space.

The Shuffle

My initial reaction is that it probably is not possible, but I think back to a neat little in-place array randomizer introduced in algorithms class. This is not all that related my current problem, but I’d mention it as sort of a source of inspiration. It’s a simple array shuffle:

function shuffle(S)
    for a in 1..|S|
        b := randomly select integer from [i, n]
        swap(S, a, b)

It’s not a difficult exercise to show that this produces a uniformly random distribution of array permutations. Let pr(xi, j) be the probability that element starting at position i ends up at position j. For the distribution to be uniform, this probability must be 1 / n for all i and j.

First, an element can only end up in position 1 if it is selected on the first iteration, with probability 1 / n. Then with strong induction we can show:

\[ \begin{align*} \textrm{pr}(x,j) & = \left( 1 - \sum_{i=1}^{j-1}(\textrm{pr}(x,i)) \right) \left( \frac{1}{n-j+1} \right) \\ & = \left( 1 - \frac{j-1}{n} \right) \left( \frac{1}{n-j+1} \right) = \frac{1}{n} \end{align*} \]

The math wasn’t really necessary there, because this process is fairly intuitive. You can think of it as a random version of an in-place insertion sort, wherein the array is divided into randomized and unrandomized segments (instead of sorted and unsorted.)

This is also useful for generating a random subset of size r:

function subset(S, r)
    for a in 1..r
        b := randomly select integer from [i, n]
        swap(S, a, b)
    return S[1..r]

That was quite a digression. But the point was, you can generate uniform randomness in some odd, unobvious ways.

One Random Element

But to get back to the task at hand: picking a random element from an input stream in constant space. I can only think of one reasonable way to write this algorithm:

function choose(input)
    i = 1
    chosen := nil
    for x in input
        if (true with probability f_i)
            chosen := x
        i := i + 1
    return chosen

It holds onto a single entry (chosen) at a time. Each time new entry x is read, it becomes the chosen entry with some probability dependent only on i (because the value of i is the only information available).

First, an expression for pi, the probability that the algorithm chooses xi. This event occurs when xi is swapped in, and no subsequent elements are swapped in to replace it:

\[ p_i = f_i \prod_{k=i+1}^n (1 - f_k) \]

f needs to be defined such that pi = 1 / n for all 1 ≤ i ≤ n. An equivalent statement is that pi = pi+1 for all 1 ≤ i < n. This information is enough to construct a recurrence relation for f.

\[ \begin{align*} p_i & = p_{i+1} \\ f_i \prod_{k=i+1}^n (1 - f_k) & = f_{i+1} \prod_{k=i+2}^n (1 - f_k) \\ f_i (1 - f_{i+1}) \prod_{k=i+2}^n (1 - f_k) & = f_{i+1} \prod_{k=i+2}^n (1 - f_k) \\ f_i (1 - f_{i+1}) & = f_{i+1} \\ f_{i+1} & = \frac{f_i}{f_i + 1} \end{align*} \]

The first entry needs to be recorded no matter what, so f1 = 1. Solving the recurrence for f gives fi = 1 / i.

Bash Implementation

RANDOM=$$   # seed the rng with the pid
chosen=''   # will hold selected element
i=1         # initialize loop counter

# read one line at a time
while read x; do

  # select x with probability 1/i
  if [ "$(($RANDOM % $i))" = "0" ]; then

  # increment loop counter
  i=$((i + 1))


echo $chosen

Tested it with a cute little histogram script called bars:

(for i in {1..10000}; do (echo `seq 1 5 | ./choose`); done) | ./bars -r 0 -3 30

    1| 1047| ******************************
    2| 1002| *****************************
    3|  988| ****************************
    4| 1035| ******************************
    5|  977| ****************************
    6|  982| ****************************
    7|  988| ****************************
    8|  976| ****************************
    9|  996| *****************************
   10| 1009| *****************************

More Random Elements

So the next logical question is: Can this be generalized to choose some r elements instead of just one?

function choose(input, r)
    i = 1
    chosen := collection of size r
    for x in input
        if (true with probability f_i))
        i := i + 1
    return chosen

The new algorithm is strikingly similar to the first, but chosen now needs to be some sort of data structure which holds up to r elements. For the same reason that f1 = 1 in the previous version, in this case f1 ... fr must all be 1 (the first r elements must all be saved).

Once full, however, this data stucture needs to make some decision about which element to evict when a new one is added. There are two reasonable choices - it could choose randomly, or behave as a FIFO queue. The latter solution seemed to be more elegant, but the math involved is not pretty (just trying to write an expression for pi is a dreadful mess).

When the replacement occurs randomly, however, determining pi is just as easy as it was last time. The only difference is that now the probability that an item with be evicted by a subsequent item xk, which has changed from fk to fk / r.

\[ \begin{align*} p_i & = f_i \prod_{k=i+1}^n \left(1 - \frac{f_k}{r}\right) \\ & = f_i \, (r^{i-n}) \prod_{k=i+1}^n (r - f_k) \end{align*} \]

A recurrence for fi also follows in the same manner.

\[ \begin{align*} p_i & = p_{i+1} \\ f_i (r^{i-n}) \prod_{k=i+1}^n (r - f_k) & = f_{i+1} (r^{(i+1)-n}) \prod_{k=i+2}^n (r - f_k) \\ f_i (r^{i-n}) (r - f_{i+1}) \prod_{k=i+2}^n (r - f_k) & = f_{i+1} (r) (r^{i-n}) \prod_{k=i+2}^n (r - f_k) \\ f_i (r - f_{i+1}) & = f_{i+1} (r) \\ f_{i+1} & = \frac{f_i r}{f_i + r} \end{align*} \]

fr = 1 as stated earlier. So for i ≤ r, this recurrence tells us that fi = r / i.

More Bash

r=${1:-1}   # number of items (default 1)
RANDOM=$$   # seed the rng with the pid
chosen=()   # will contain selected elements
i=1         # initialize loop counter

# read one line at a time
while read x; do

  # add 1..r sequentially
  if [ $i -le $r ]; then

  # add the rest with probability r/i
  elif [ "$(($RANDOM % $i))" -lt "$r" ]; then
    chosen[$(($RANDOM % $r))]="$x"


  # increment loop counter
  i=$((i + 1))


for ((i = 0; i < ${#chosen[@]}; i++)); do
  echo "${chosen[i]}"

This histogram is for the selection of 2 elements from 1 to 5:

(for i in {1..10000}; do (echo `seq 1 5 | ./choose 2 | sort | tr "\n" " "`); done) | ./bars -r 0

  1 2| 1016| ******************************
  1 3| 1007| *****************************
  1 4|  998| *****************************
  1 5|  997| *****************************
  2 3|  981| *****************************
  2 4| 1015| ******************************
  2 5|  987| *****************************
  3 4|  994| *****************************
  3 5|  975| ****************************
  4 5| 1030| ******************************

A Few Notes

Much of this math lacks rigor. Specifically, I sort of made up a definition of a uniformly random subset. This text only considers the criterion that each element be included with the correct probability, but it ignores any notion of independence. For instance, a poorly designed array shuffling algorithm that merely shifts indices (moves each xi to position (i + random) % n) would satisfy the former but not the latter requirement.

This implementation is not good for large sets, because $RANDOM is limited to 32767. It won’t fail, but as the set number approaches this magnitude, probabilities will be off.

My testing has not shown this algorithm to provide any speedup over the simple version. Picking a random number from seq 10000 takes about twice as long.

I did not design this with much mind to efficiency. For example, if r = 1, there is no need to generate a random number to determine an element’s place in the chosen array.

The script generates a lot of random numbers - two for each element. If “true” randomness is important, this algorithm requires a great deal of entropy.


Hopefully something here was useful aside from learning some new Bash tricks.

I think this is a good strategy if you ever need to pick something random from a set elements read from IO in a situation where space is a concern.

So, like all clever tricks, you will likely never have good reason to use it.

I write about Haskell and related topics; you can find my works online on Type Classes and in print from The Joy of Haskell.