Weighted random selection from array

I have found this article to be the most useful at understanding this problem fully. This stackoverflow question may also be what you're looking for.

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I believe the optimal solution is to use the Alias Method (wikipedia). It requires O(n) time to initialize, O(1) time to make a selection, and O(n) memory.

Here is the algorithm for generating the result of rolling a weighted n-sided die (from here it is trivial to select an element from a length-n array) as take from this article. The author assumes you have functions for rolling a fair die (floor(random() * n)) and flipping a biased coin (random() < p).

Algorithm: Vose's Alias Method

Initialization:

1. Create arrays Alias and Prob, each of size n.
2. Create two worklists, Small and Large.
3. Multiply each probability by n.
4. For each scaled probability p_i:
   1. If p_i < 1, add i to Small.
   2. Otherwise (p_i ≥ 1), add i to Large.
5. While Small and Large are not empty: (Large might be emptied first)
   1. Remove the first element from Small; call it l.
   2. Remove the first element from Large; call it g.
   3. Set Prob[l]=p_l.
   4. Set Alias[l]=g.
   5. Set p_g := (p_g+p_l)−1. (This is a more numerically stable option.)
   6. If p_g<1, add g to Small.
   7. Otherwise (p_g ≥ 1), add g to Large.
6. While Large is not empty:
   1. Remove the first element from Large; call it g.
   2. Set Prob[g] = 1.
7. While Small is not empty: This is only possible due to numerical instability.
   1. Remove the first element from Small; call it l.
   2. Set Prob[l] = 1.

Generation:

1. Generate a fair die roll from an n-sided die; call the side i.
2. Flip a biased coin that comes up heads with probability Prob[i].
3. If the coin comes up "heads," return i.
4. Otherwise, return Alias[i].

Compute the discrete cumulative density function (CDF) of your list -- or in simple terms the array of cumulative sums of the weights. Then generate a random number in the range between 0 and the sum of all weights (might be 1 in your case), do a binary search to find this random number in your discrete CDF array and get the value corresponding to this entry -- this is your weighted random number.

The algorithm is straight forward

rand_no = rand(0,1)
for each element in array 
     if(rand_num < element.probablity)
          select and break
     rand_num = rand_num - element.probability