weighted_rand 0.3.0

A weighted random sampling crate using Walker's Alias Method.
Documentation

weighted_rand

weighted_rand Crates.io docs.rs Crates.io

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 threshold (or a probability) to decide whether to jump to that index.

The API documentation is here.

Usage

Add this to your Cargo.toml:

[dependencies]

weighted_rand = "0.3"

Example

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 = vec![2, 1, 7, 0];

    let builder = WalkerTableBuilder::new(&index_weights);
    let wa_table = builder.build();

    for _ in 0..10 {
        let i = wa_table.next(); // Will output 0, 1, or 2
        println!("{}", fruit[i]);
    }
}

Also, index_weiaghts supports Vec<f32>, like:

use weighted_rand::builder::*;

fn main() {
    // Coins with a 5% higher probability of heads than tails
    let cheating_coin = ["Heads!", "Tails!"];
    let index_weights = vec![0.55, 0.45];

    let builder = WalkerTableBuilder::new(&index_weights);
    let wa_table = builder.build();

    for _ in 0..10 {
        let i = wa_table.next();
        println!("{}", cheating_coin[i]);
    }
}

License

Licensed under either of

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.