From an email address to a quasi-random number [closed]

Question

My Aim:

I'd like to a have a function that takes an email address and outputs a quasi-random number of 1, 2, 3, or 4.

A little detail:

By quasi-random number I mean that given a typical population of email addresses, the probabilities of getting a value of 1, 2, 3, or 4 are roughly equal, and that obvious systematic properties of the email address such as the domain name do not affect the probability of getting a value of 1, 2, 3, or 4.

A little background:

I have an online experiment written in inquisit where participants log in on two occasions. I want to randomly assign participants to one of four groups. While this is easy to do for one session (I can just use a random number generator), I need some way of remembering the allocation across sessions. Thus, I thought that I could extract a quasi-random group allocation from the participant email. I'm also limited in the set of functions that I have at my disposal (see here for full list). The string functions are: tolower toupper capitalize concat search replaceall contains startswith endswith substring trim trimright trimleft length format evaluate

Initial Thoughts:

I thought about trying to extract a set of features of the email address that returned a value of 1, 2, 3, or 4 with roughly equal probabilities. Then, I could sum these properties and get the mod 4 plus 1 of that. Thus, assuming something like the central limit theorem, I might get close.

Possible features that came to my mind:

• length of string
• position of first "a", "b", etc.

Answer 1

Look up hash functions, for example at http://en.wikipedia.org/wiki/Hash_function

Answer 2

Why not just have a look-up table of numbers for each possible character in an email. Then concatenate the numbers to form a seed. For example,

A 1
B 2
C 3
....
@ 27
....

So abc@ccc, would be converted to 12327333. This would give you a unique seed for each person. You would then use this to generate the 1, 2, 3, 4.

---

From your question, it looks like you don't mind a "quick and dirty solution". One problem with my solution is that email addresses aren't random - for example you will probably get very few email addresses that contain the letter "z", but all email addresses contain "@".

Answer 3

As an addition to other excellent answers, I just will give a simple example in R language to show a very simple hash function, which should be good enough for this purpose. To get some email addresses as test data, I get a character vector with the emails of the maintainers of the (too many!) R packages installed on my computer:

library(stringr) # on CRAN 
last <- function(x) { return( x[length(x)] ) }

INST  <-  installed.packages(priority="NA", fields=c("Maintainer"))
Maintainer <- INST[, "Maintainer"]
Mlist <- str_split(Maintainer, "[[:blank:]]")
Maddr <- sapply(Mlist, FUN=last)
Maddr <- str_replace(Maddr, "[<>]", "")
Maddr <- unique(Maddr)

Then I define a simple function which gets some number from each character in the email address, adds them, computes the remainder modulo 4 and adds 1, so it returns always one of the results 1,2,3 or 4:

apply_to_each_char  <-  function(w, FUN) {
    ww <-  str_split(w, "")[[1]]
    res <- sapply(ww, FUN)
    } # END apply_to_each_char
charsum <- function(word) { # length-one char vector
    sum0 <- sum( apply_to_each_char(word, function(w) as.integer(charToRaw(w)) ))
    return( 1 + sum0 %% 4)
    } # end charsum

Then applying it:

hashes <- sapply(Maddr, charsum)
table(hashes)
hashes
  1   2   3   4 
542 511 562 552 

and we can observe that the resulting distribution is close to uniform.

Answer 4

You could try converting each character to an ascii number, multiplying them all together to force overflow, and then performing a modulus operation on the least significant digits. If this is not pseudo-random enough, you can perform a bit-shift the numbers a bit...

-Ralph Winters