This package implements a Mixtures of Gaussian Process Experts model with a GP-based gating network.
Inference exploits factorisation through sparse GPs and trains a variational lower bound stochastically.
It also provides the building blocks for implementing other Mixtures of Gaussian Process Experts models.
mosvgpe uses GPflow 2.5/TensorFlow 2.4+ for running computations, which allows fast execution on GPUs, and uses Python ≥ 3.8.
It was originally created by Aidan Scannell.
This is a Python package that should be installed into a virtual environment. Start by cloning the repo from Github:
git clone https://github.com/aidanscannell/mosvgpe.gitThe package can then be installed into a virtual environment by adding it as a local dependency.
Create a new virtualenv and activate it, for example,
python -m venv mosvgpe-env
source mosvgpe-env/bin/activatecd into the root of this package and install it and its dependencies with,
pip install .If you want to develop the mosvgpe codebase then install it in “editable” or “develop” mode with:
pip install -e .See ./examples.