pylibhmm 0.6.0

Python bindings for libhmm v4 — scalar and multivariate HMMs with NumPy-friendly distributions

pylibhmm

Python bindings for libhmm v4 — a modern C++20 Hidden Markov Model library with 16 scalar and 3 multivariate emission distributions, canonical log-space inference, and training algorithms.

Features

Scalar HMMs

• All 16 emission distributions: Gaussian, StudentT, Gamma, LogNormal, Beta, Weibull, Exponential, Rayleigh, Pareto, Uniform, ChiSquared, VonMises, Discrete, Poisson, Binomial, NegativeBinomial
• Hmm, ForwardBackwardCalculator, ViterbiCalculator, BaumWelchTrainer, MapBaumWelchTrainer, ViterbiTrainer, SegmentalKMeansTrainer
• Posterior decoding (decode_posterior) and model selection (AIC / BIC / AICc)
• JSON model I/O (save_json / load_json / to_json / from_json) — recommended
• Legacy XML model I/O (save_hmm / load_hmm) — retained for existing files

Multivariate HMMs (v0.6.0 / libhmm v4)

• DiagonalGaussian, FullCovGaussian, IndependentComponents distributions
• HmmMV, MVForwardBackwardCalculator, MVBaumWelchTrainer
• kmeans_init for k-means++ seeded initialisation
• MV JSON I/O (save_json_mv / load_json_mv / to_json_mv / from_json_mv)
• NumPy-friendly: observations as 2-D float64 arrays, sequences as lists of 2-D arrays

General

• Python 3.11+ packaging via scikit-build-core + nanobind
• Real-data examples: DAX regime detection, elk movement, earthquake counts, S&P 500, wind direction

Quick start

Scalar HMM:

import numpy as np
import pylibhmm

hmm = pylibhmm.Hmm(2)
hmm.set_pi(np.array([0.75, 0.25], dtype=np.float64))
hmm.set_trans(np.array([[0.9, 0.1], [0.8, 0.2]], dtype=np.float64))

fair = pylibhmm.Discrete(6)
for i in range(6):
    fair.set_probability(i, 1.0 / 6.0)

loaded = pylibhmm.Discrete(6)
for i in range(5):
    loaded.set_probability(i, 0.125)
loaded.set_probability(5, 0.375)

hmm.set_distribution(0, fair)
hmm.set_distribution(1, loaded)

obs = np.array([0, 1, 5, 4, 2], dtype=np.float64)
fb = pylibhmm.ForwardBackwardCalculator(hmm, obs)
print(fb.log_probability)

pylibhmm.save_json(hmm, "model.json")
hmm2 = pylibhmm.load_json("model.json")

Multivariate HMM:

import numpy as np
import pylibhmm

# 3-state, 2-feature diagonal-Gaussian HMM
hmm = pylibhmm.HmmMV(3)
hmm.set_pi(np.array([0.5, 0.3, 0.2]))
hmm.set_trans(np.array([[0.8, 0.1, 0.1],
                         [0.1, 0.8, 0.1],
                         [0.1, 0.1, 0.8]]))

for i, (mean, var) in enumerate([
    ([0.0, 0.0], [1.0, 1.0]),
    ([3.0, 3.0], [1.5, 1.5]),
    ([6.0, 6.0], [2.0, 2.0]),
]):
    hmm.set_distribution(i, pylibhmm.DiagonalGaussian(
        np.array(mean), np.array(var)))

# observations: 2-D float64 array, shape (T, D)
obs = np.random.randn(200, 2).astype(np.float64)

# Initialise with k-means++ then train
pylibhmm.kmeans_init(hmm, [obs])
trainer = pylibhmm.MVBaumWelchTrainer(hmm, [obs])
trainer.train()

fb = pylibhmm.MVForwardBackwardCalculator(hmm, obs)
print(fb.log_probability)

pylibhmm.save_json_mv(hmm, "model_mv.json")
hmm2 = pylibhmm.load_json_mv("model_mv.json")

Build and install

Prerequisites:

• Python 3.11+
• CMake 3.20+
• C++20 compiler

Install locally:

pip install .

Run tests:

pip install ".[test]"
pytest

Dependency strategy

pylibhmm prefers a local sibling ../libhmm source tree if present. If not found, CMake falls back to FetchContent for libhmm tag v4.0.0.

Notes on wheel portability

libhmm defaults to machine-tuned SIMD flags (-march=native or CPU-selected MSVC /arch). That is ideal for local builds but requires extra care for portable binary wheels. See docs/COMPATIBILITY.md.

Documentation

• docs/API.md — bound API surface
• docs/DEVELOPMENT.md — contributor workflow
• docs/COMPATIBILITY.md — platform/version/SIMD notes
• WARP.md — Warp agent guide for this repository

License

MIT