gmr

library2.0.3pypypi✓ verified 84d ago

Gaussian Mixture Regression (GMR) for learning and inferring multivariate probability distributions from data. Version 2.0.3 supports Python >=3.7, NumPy 2 compatibility, and sklearn-compatible GaussianMixtureRegressor. Low release cadence (last release 2024-06-03).

ai-ml data

pip install gmr

github ↗package ↗

INSTALL

IMPORT

SIG · GMR

gmr

ai-mlpythonv2.0.3

Install

7.4s avg

Import

2485ms

Disk

230MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v2.0.3 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 2.542s · 230.8MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 7.4s · import 2.428s · 222MB

230MB installed

● package 230MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

GMM

✓ from gmr import GMM

✗ from gmr.gmm import GMM

Flat import structure; submodule not exposed directly.

MVN

✓ from gmr import MVN

✗ from gmr.mvn import MVN

MVN is also exported from top-level.

GaussianMixtureRegressor

✓ from gmr import GaussianMixtureRegressor

✗ from gmr.sklearn import GaussianMixtureRegressor

Brought to top-level in 1.6.

Basic usage: create GMM, fit data, condition on a slice.

import numpy as np from gmr import GMM, MVN # Create a simple GMM with 2 components random init random_state = np.random.RandomState(0) gmm = GMM(n_components=2, random_state=random_state) X = np.random.randn(100, 3) gmm.from_samples(X) # Condition on first dimension mvn_conditional = gmm.condition(np.array([0.5]), [0]) print("Conditional mean:", mvn_conditional.mean)

Debug

Known issues

breaking`GMM.condition` now returns an `MVN` object (not tuple of mean/covariance). Code assuming tuple will break.

fix

Access `.mean` and `.covariance` attributes on the returned MVN.

affects: >=1.6

breakingProbability density scaling fixed in v2.0.0; marginal densities may differ from v1.x by factor that ensures integration to 1.

fix

If you relied on original scaling, normalize outputs or use v1.6.1.

affects: >=2.0.0

gotcha`GMM.from_samples` does not check if the data has sufficient rank; singular covariance matrices cause runtime errors unless using oracle approximating shrinkage (enabled by default).

fix

Use `oracle_approximating_shrinkage=True` (default) or regularize via `GMM.priors`. See docstring.

affects: >=1.6

deprecatedPython 2.7 support ended silently; no compatibility guarantees for Python 2.

fix

Upgrade to Python >=3.7 and gmr >=2.0.0.

affects: <2.0.0

Errors

Common errors & fixes

AttributeError: module 'gmr' has no attribute 'GMM'

Incorrect import path (tried `from gmr import GMM` but installed old version <1.6 or import shadowed by file named gmr.py).

fix

Install gmr>=1.6 (`pip install gmr==2.0.3`) and ensure no local file named gmr.py.

ValueError: operands could not be broadcast together with shapes ...

Condition indices or values vector length mismatch with data dimensions.

fix

Verify that condition indices are within range [0, n_features-1] and values length equals len(indices).

LinAlgError: Singular matrix

Covariance matrix became singular due to duplicate data points or insufficient samples per component.

fix

Increase `n_components`, add noise to data, or set `random_state` to a different seed.

Upgrade

Version history

2.0.3latest on PyPI · released Jan 12, 2026

Audit

Dependencies

numpyrequiredCore dependency for array operations

scikit-learnoptionalFor GaussianMixtureRegressor and clustering utilities

scipyoptionalOptional for some examples

Agent activity

11 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/AlexanderFabisch/gmr ↗

packagehttps://pypi.org/project/gmr/ ↗