WeightedRandom

Crystal library for a fast O(1) weighted random samples generation using Alias algorithm.

Note that initialization is O(N) and next_choice call is O(1). But a straight-forward O(N) Linear Scan could be faster for a few weights, or when you need just a few samples (especially one).

The case with just 2 weights (a biased coin) is optimized.

Installation

1. Add the dependency to your shard.yml:

   dependencies:
     weightedrandom:
       github: dimagog/weightedrandom
   

2. Run shards install

Usage

The correct usage pattern is to create a new WeightedRandom object once and call next_choice on it many-many times.

require "WeightedRandom"

NOTE: Only Integer weights are supported for now. Fractional weights support can be added with few modifications to the Alias algorithm to account for imprecise math.

When you simply need indices of the weights

r = WeightedRandom.new([1, 2])
r.next_choice

The next_choice above will randomly generate 0s and 1s. 1s will be twice more likely than 0s.

A common case is when weights are percentages:

r = WeightedRandom.new([5, 70, 25])
r.next_choice

Here 5% of calls to next_choice will return 0, 70% will return 1, and 25% of calls will return 2.

To be explicit that you are creating an indexed choice you can use WeightedRandom.indexed instead of WeightedRandom.new.

When weights have labels

r = WeightedRandom.new({"a" => 1, "b" => 2})
r.next_choice

The next_choice above will randomly generate "a" or "b". "b"s will be twice more likely than "a"s.

To be explicit that you are creating a keyed choice you can use WeightedRandom.keyed instead of WeightedRandom.new.

Contributors

• Dmitry Kakurin - creator and maintainer