Tuesday, 19 January 10

Today I needed a PRNG with a bised distribution, specifically following the Power Law distribution in order to simulate the "long tail" often seen in things like social networks data, search, and so forth.

I was not able to find ready to use code, but just a few formulas, so I wrote one in Ruby (I'm going to port this to C for Redis).

Maybe this will be useful to somebody else:

I was not able to find ready to use code, but just a few formulas, so I wrote one in Ruby (I'm going to port this to C for Redis).

Maybe this will be useful to somebody else:

# Power law (log tail) distribution # Copyright(C) 2010 Salvatore Sanfilippo # this code is under the public domain

# min and max are both inclusive # n is the distribution power: the higher, the more biased def powerlaw(min,max,n) max += 1 pl = ((max**(n+1) - min**(n+1))*rand() + min**(n+1))**(1.0/(n+1)) (max-1-pl.to_i)+min end

freq = {} 100000.times { n = powerlaw(0,100,2).to_i freq[n] = 0 if !freq[n] freq[n] += 1 } (0..100).each{|x| puts "#{x} => #{freq[x]}" }