https://github.com/josacar/walker_method

A Crystal implementation of Walker's Alias Method for quickly sampling from an array with a given probability distribution
https://github.com/josacar/walker_method

algorithm crystal crystal-language hacktoberfest probability shards walker-alias

Last synced: about 2 months ago
JSON representation

A Crystal implementation of Walker's Alias Method for quickly sampling from an array with a given probability distribution

• Host: GitHub
• URL: https://github.com/josacar/walker_method
• Owner: josacar
• License: mit
• Created: 2020-05-28T14:25:43.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-03-24T18:36:37.000Z (almost 4 years ago)
• Last Synced: 2024-10-25T01:25:44.647Z (3 months ago)
• Topics: algorithm, crystal, crystal-language, hacktoberfest, probability, shards, walker-alias
• Language: Crystal
• Homepage:
• Size: 7.81 KB
• Stars: 4
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

README

        
# WalkerMethod

[![Build Status](https://travis-ci.org/josacar/walker_method.svg?branch=master)](https://travis-ci.org/josacar/walker_method)

Walker's Alias Method is an O(1) algorithm for selecting elements from an array given a weighted distribution.

For example, let's say you want to return `:win` 60% of the time, `:lose` 20% of the time, and `:tie` 20% of the time.  You could fill an array with

6 examples of `:win` and 2 examples each of `:lose` and `:tie`, then pick a random element.  You could also use ranges, picking a random number

between 0.0 and 1.0 and returning `:win` when the number is below 0.6, `:lose` if the number is below 0.8, and `:tie` otherwise.  But, these algorithms are still O(n).

You can do better by using a heap or binary search tree, but Walker's Alias Method is better still, with a constant runtime once an O(n)

pre-computation phase has completed.  For this example, you could do:

    selector = WalkerMethod.new([:win, :lose, :tie], [60, 20, 20])

    selector.random

This implementation is a port of http://code.activestate.com/recipes/576564-walkers-alias-method-for-random-objects-with-diffe/ from Python to Crystal.  There is also a [Node.js implementation](https://github.com/ThoughtLeadr/Walker-Random-Node).

## Speed

It's FAST and simple.  It samples from a 50,000 word frequency dictionary 10,000 times in 1.2 milliseconds.

## Installation

Add this to your application's `shard.yml`:

```yaml

dependencies:

  walker_method:

    github: josacar/walker_method

```

## Usage

```crystal

# Sampling from an English word frequency dictionary care of http://invokeit.wordpress.com/frequency-word-lists

words = ["you", "the", "i", "to", "a", "and", "it", "of", "that", "in", "is", "me", "what", "this", "for", "my", "on", "your", "we", "have", "do", "no", "don't", "are", "be"]

freqs = [4621939, 3957465, 3476773, 2873389, 2551033, 1775393, 1693042, 1531878, 1323823, 1295198, 1242191, 1208959, 1071825, 961194, 898671, 877684, 867296, 834953, 819499, 812625, 799991, 788200, 764177, 743194, 743014]

selector = WalkerMethod.new(words, weights)

selector.random

# => "and"

```

## Thanks to

Thanks to original gem contributors of [https://github.com/cantino/walker_method](https://github.com/cantino/walker_method)

## Contributing

1. Fork it

2. Create your feature branch (`git checkout -b my-new-feature`)

3. Commit your changes (`git commit -am 'Add some feature'`)

4. Push to the branch (`git push origin my-new-feature`)

5. Create new Pull Request