fast_loaded_dice_roller

About the project

This is a Rust library and example program meant to help popularize the usage of the novel Fast Loaded Dice Roller* discrete sampling algorithm. It is designed with generality, low dependencies, and efficiency in mind. Also, it is simple to use, featuring an optional default implementation of the required FairCoin trait, and heavy documentation of the internal FLDR algorithm for the curious.

Usage

Library

The library can be added to your existing projects with cargo add fast_loaded_dice_roller. You can include the optional template rand::RngCoin<R> implementation of the FairCoin trait by enabling the rand feature (e.g., cargo add fast_loaded_dice_roller --features="rand"), which has a dependency on the crate rand.

Example program

The example program can be built with cargo b --example generator --features="rand". The example can be run with default values, cargo r --example generator --features="rand", but also has the following usage:

Rust implementation of the novel Fast Loaded Dice Roller algorithm (https://arxiv.org/pdf/2003.03830.pdf)

Usage: generator [OPTIONS]

Options:
  -r, --roll-count <ROLL_COUNT>                        [default: 100000]
  -v, --verbose
  -p, --print-histogram
  -d, --distribution <DISTRIBUTION> <DISTRIBUTION>...
  -h, --help                                           Print help
  -V, --version                                        Print version

An example of its usage is cargo r --example generator --features="rand" -- -d 1 2 3 -r 6000.

Citation

I neither created nor discovered the FLDR algorithm. This crate is simply an implementation.

* Citation for the Fast Loaded Dice Roller algorithm:

@inproceedings{saad2020fldr,
title           = {The Fast Loaded Dice Roller: A Near-optimal Exact Sampler for Discrete Probability Distributions},
author          = {Saad, Feras A. and Freer, Cameron E. and Rinard, Martin C. and Mansinghka, Vikash K.},
booktitle       = {AISTATS 2020: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
volume          = 108,
series          = {Proceedings of Machine Learning Research},
address         = {Palermo, Sicily, Italy},
publisher       = {PMLR},
year            = 2020,
keywords        = {random variate generation, sampling, discrete random variables},
abstract        = {This paper introduces a new algorithm for the fundamental problem of generating a random integer from a discrete probability distribution using a source of independent and unbiased random coin flips. This algorithm, which we call the Fast Loaded Dice Roller (FLDR), has efficient complexity properties in space and time: the size of the sampler is guaranteed to be linear in the number of bits needed to encode the target distribution and the sampler consumes (in expectation) at most 6.5 bits of entropy more than the information-theoretically minimal rate, independently of the values or size of the target distribution. We present an easy-to-implement, linear-time preprocessing algorithm and a fast implementation of the FLDR using unsigned integer arithmetic. Empirical evaluations establish that the FLDR is 2x--10x faster than multiple baseline algorithms for exact sampling, including the widely-used alias and interval samplers. It also uses up to 10000x less space than the information-theoretically optimal sampler, at the expense of a less than 1.5x runtime overhead.},
}