# weighted_rand [![weighted_rand](https://github.com/ichi-h/weighted_rand/actions/workflows/weighted_rand.yml/badge.svg)](https://github.com/ichi-h/weighted_rand/actions/workflows/weighted_rand.yml) [![Crates.io](https://img.shields.io/crates/v/weighted_rand)](https://crates.io/crates/weighted_rand) [![docs.rs](https://img.shields.io/docsrs/weighted_rand)](https://docs.rs/weighted_rand) [![Crates.io](https://img.shields.io/crates/l/weighted_rand)](LICENSE-APACHE) A weighted random sampling crate using Walker's Alias Method. Walker's Alias Method (WAM) is one method for performing weighted random sampling. WAM weights each index of a array by giving two pieces of information: an alias to a different index and a probability to decide whether to jump to that index. WAM is a very fast algorithm, and its computational complexity of the search is O(1). The difference in complexity between WAM and the Cumulative Sum Method is as follows. | Algorithm | Building table | Search | | :-------------------: | :------------: | :------: | | Walker's Alias Method | O(N) | O(1) | | Cumulative Sum Method | O(N) | O(log N) | The API documentation is [here](https://docs.rs/weighted_rand). ## Usage Add this to your Cargo.toml: ```toml [dependencies] weighted_rand = "0.4" ``` ## Example ```rust use weighted_rand::builder::WalkerTableBuilder; fn main() { let fruit = ["Apple", "Banana", "Orange", "Peach"]; // Define the weights for each index corresponding // to the above list. // In the following case, the ratio of each weight // is "2 : 1 : 7 : 0", and the output probabilities // for each index are 0.2, 0.1, 0.7 and 0. let index_weights = [2, 1, 7, 0]; let builder = WalkerTableBuilder::new(&index_weights); let wa_table = builder.build(); for i in (0..10).map(|_| wa_table.next()) { println!("{}", fruit[i]); } } ``` Also, `index_weiaghts` supports `&[f32]`, like: ```rust use rand; use weighted_rand::builder::*; fn main() { // Coin with a 5% higher probability of heads than tails let cheating_coin = ["Heads!", "Tails!"]; let index_weights = [0.55, 0.45]; let builder = WalkerTableBuilder::new(&index_weights); let wa_table = builder.build(); // If you want to process something in a large number of // loops, we recommend using the next_rng method with an // external ThreadRng instance. let mut result = [""; 10000]; let mut rng = rand::thread_rng(); for r in &mut result { let j = wa_table.next_rng(&mut rng); *r = cheating_coin[j]; } // println!("{:?}", result); } ``` ## License Licensed under either of - Apache License, Version 2.0 ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0) - MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT) at your option. ## Contribution Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.