Installation¶

mixle supports Python 3.10 and newer. The PyPI package and import package are both named mixle.

Base Install¶

pip install mixle

The base install includes the local NumPy/SciPy path and core distribution families. It is enough to score, sample, and fit ordinary distribution, combinator, mixture, and HMM models locally.

Extras¶

Install only the optional integrations you need:

Extra	Adds	Use when
`torch`	Torch engine, GPU/autograd, neural and Transformer leaves	using Neural and LLM Models or task distillation
`scientist`	Torch, Transformers, sentence-transformers, and datasets	running `mixle.scientist`, `laptop_scientist.py`, or foundation capability distillation workflows
`numba`	JIT hot paths and TBB support	large local fits need faster kernels
`spark` / `dask` / `mpi`	distributed encoded-data backends	fitting on clusters or multi-process data
`jax`	JAX and NumPyro-backed routes	differentiable or probabilistic-programming experiments
`data`	pandas, Arrow, SQL, Mongo, fsspec connectors	loading from structured external data sources
`umap`	model-based UMAP helpers	embedding records or posterior features
`sympy` / `sage`	symbolic export	inspecting closed-form density expressions
`grammar`	NetworkX-backed grammar models	graph grammar workflows

Common installs:

pip install "mixle[torch]"
pip install "mixle[scientist]"
pip install "mixle[spark]"
pip install "mixle[all]"

The scientist extra installs Python packages only. The assembled mixle.scientist workflow loads open-weight models from the local Hugging Face cache and sets offline defaults at import time; prepare those weights explicitly before depending on that workflow.

Development Install¶

From a repository checkout:

python -m venv .venv
. .venv/bin/activate
pip install -e ".[test,lint]"

For all optional integrations:

pip install -e ".[all,test,lint]"

Smoke Test¶

python - <<'PY'
from mixle.inference import optimize
from mixle.stats import GaussianEstimator

model = optimize([1.0, 1.2, 0.9, 1.1], GaussianEstimator(), out=None)
print(round(model.mu, 3))
PY

For the neural quickstart:

python examples/shared_embedding_example.py

Documentation Build¶

. .venv/bin/activate
pip install -r docs/requirements.txt
make -C docs apidoc
.venv/bin/sphinx-build -W -b html docs docs/_build/html

The generated HTML lands in docs/_build/html.