🤔 hmmm 🤔

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...

```rust use hmmm::HMM; use ndarray::{array, Array1}; use rand::{SeedableRng, XorShiftRng};

fn main() { let trainingys = array![0, 0, 1, 0, 0, 1, 0]; let mut rng = XorShiftRng::seedfromu64(1337); let hmm = HMM::train(&trainingys, 3, 2, &mut rng); let sampledys: Array1 = hmm.sampler(&mut rng) .map(|sample| sample.y) .take(10) .collect(); asserteq!(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