# [−][src]Crate 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...`

use hmmm::HMM; use ndarray::{array, Array1}; use rand::{SeedableRng, XorShiftRng}; fn main() { let training_ys = array![0, 0, 1, 0, 0, 1, 0]; let mut rng = XorShiftRng::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.

## Structs

HMM | This struct represents a trained HMM, including values for each parameter. |

HMMFilterItem | The item yielded by the |

HMMFilterIter | This is an iterator returned by |

HMMSample | The item yielded by |

HMMSampleIter | An iterator that returns random samples from an HMM |

WeightedChoiceFloat | Sample from a categorical distribution where the weight for each category is a float. |