Discrete Hidden Markov Models with Numba
Project description
Hmmkay
🤔👌
Discrete Hidden Markov Models with Numba
Hmmkay is a basic library for discrete Hidden Markov Models that relies on numba's just-in-time compilation. It supports decoding, likelihood scoring, fitting (parameter estimation), and sampling.
Hmmkay accepts sequences of arbitrary length, e.g. 2d numpy arrays or lists of iterables. Hmmkay internally converts lists of iterables into Numba typed lists of numpy arrays.
Installation
pip install hmmkay
Requires Python 3.6 or higher.
Examples
Scoring and decoding:
from hmmkay.utils import make_observation_sequences
sequences = make_observation_sequences(n_seq=100, n_observable_states=4, random_state=0)
hmm.fit(sequences)
from hmmkay.utils import make_proba_matrices
from hmmkay import HMM
init_probas, transition_probas, emission_probas = make_proba_matrices(
n_hidden_states=2,
n_observable_states=4,
random_state=0
)
hmm = HMM(init_probas, transition_probas, emission_probas)
sequences = [[0, 1, 2, 3], [0, 2]]
hmm.log_likelihood(sequences)
-8.336
hmm.decode(sequences) # most likely sequences of hidden states
[array([1, 0, 0, 1], dtype=int32), array([1, 0], dtype=int32)]
Fitting:
from hmmkay.utils import make_observation_sequences
sequences = make_observation_sequences(n_seq=100, n_observable_states=4, random_state=0)
hmm.fit(sequences)
Sampling:
hmm.sample(n_obs=2, n_seq=3) # return sequences of hidden and observable states
(array([[0, 1],
[1, 1],
[0, 0]]), array([[0, 2],
[2, 3],
[0, 0]]))
Documentation
Docs are online at https://hmmkay.readthedocs.io/en/latest/
Benchmark
It should be faster than hmmlearn. Here's the result of the benchmark.py
script on my laptop:
Status
Highly experimental, API subjet to change without deprecation.
Development
The following packages are required for testing:
pip install pytest hmmlearn scipy
For benchmarks:
pip install matplotlib hmmlearn
For docs:
pip install sphinx sphinx_rtd_theme
For development, use pre-commit hooks for black and flake8.
Project details
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