3 unstable releases
0.2.0 | Sep 19, 2020 |
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0.1.1 | Feb 10, 2019 |
0.1.0 | Feb 10, 2019 |
#1376 in Algorithms
45KB
881 lines
🤔 hmmm 🤔
Hidden Markov Models in Rust.
This library contains a Rust implementation of a time-invariant Hidden Markov model with discrete observations. It includes maximum likelihood estimation via the Baum-Welch expectation-maximization algorithm and hidden state inference via the Viterbi algorithm.
See hmmm::HMM
for detailed documentation on how to work with this library.
Below, the HMM is trained to recognize the pattern 001001001...
use hmmm::HMM;
use ndarray::{array, Array1};
use rand::SeedableRng as _;
let training_ys = array![0, 0, 1, 0, 0, 1, 0];
let mut rng = rand::rngs::StdRng::seed_from_u64(1337);
let hmm = HMM::train(&training_ys, 3, 2, &mut rng);
let sampled_ys: Array1<usize> = hmm.sampler(&mut rng)
.map(|sample| sample.y)
.take(10)
.collect();
assert_eq!(array![0, 0, 1, 0, 0, 1, 0, 0, 1, 0], sampled_ys);
Building
This project uses cargo-make
. See Makefile.toml
for a full list of build commands, but the
main useful command for this project is cargo make all
.
There is a small amount of benchmarking functionality gated by the benchmark
feature.
Notes
Sections 17.3 and 17.4 of Machine Learning a Probabilistic Perspective by Kevin Murphy, 2012 were invaluable as a reference, as was section 13.2 of Pattern Recognition and Machine Learning by Christopher Bishop, 2016.
I have attempted to make the math notation readable both as rendered HTML and from the source code. The notation is strongly inspired by the Wikipedia page on the Baum-Welch algorithm.
License: MIT/Apache-2.0
Dependencies
~2.5MB
~51K SLoC