gmm-divergence 0.0.1a6

Utilities for estimating divergences between Gaussian mixture models.

gmm-divergence

Utilities for estimating divergences between Gaussian mixture models.

This package is under development. APIs, estimators, and numerical behavior may change between early releases.

Current Scope

The package currently includes:

• Typed Gaussian and Gaussian mixture representations
• Density and log-density evaluation for Gaussian mixtures
• Sampling from Gaussian mixtures
• KL divergence estimators based on closed-form Gaussian KL, Monte Carlo sampling, unscented sigma points, Gaussian approximations, and variational bounds
• Explicit sampling controls for drawn, reused, and stratified Monte Carlo samples
• Mixture-weight fitting with forward, reverse, bidirectional, Jensen-Shannon, and moment-matching objectives
• Covariance regularization utilities for diagonal loading, shrinkage, eigenvalue clipping, and low-rank approximation

Installation

This project is not yet intended for stable production use. A pre-release is available from PyPI:

python -m pip install gmm-divergence

Quick Example

import gmm_divergence as gd

p = gd.GaussianMixture.from_components(
    [
        gd.Gaussian.univariate(mean=-1.0, variance=0.5),
        gd.Gaussian.univariate(mean=1.5, variance=1.0),
    ],
    weights=[0.4, 0.6],
)
q = gd.GaussianMixture.from_components([
    gd.Gaussian.univariate(mean=-0.8, variance=0.7),
    gd.Gaussian.univariate(mean=1.8, variance=1.2),
])

result = gd.kl_divergence(
    p, q, method=gd.divergence.MonteCarlo(sampling=gd.sampling.Draw(50_000, rng=0))
)
print(result.value, result.monte_carlo_stats.standard_error)

The top-level module keeps the common distribution classes and primary helper functions. Configuration objects are grouped by domain, for example gd.divergence.MonteCarlo, gd.sampling.Draw, gd.fitting.ForwardKL, and gd.covariance.DiagonalLoading.

Fitting Mixture Weights

candidates = [
    gd.Gaussian.univariate(mean=-1.0, variance=0.5),
    gd.Gaussian.univariate(mean=1.5, variance=1.0),
]

fit = gd.fit_mixture_weights(
    p, candidates, objective=gd.fitting.MomentMatching(fit_second_moments=True)
)
print(fit.weights)