don't regress. A package for neural conditional density estimation.
Project description
Description
Python package for conditional density estimation. It either wraps or implements diverse conditional density estimators.
Density estimation with normalizing flows
This package provides pass-through access to all the functionalities of nflows.
Installation
pyknos
requires Python 3.8 or higher. A GPU is not required, but can lead to speed-up
in some cases. We recommend using a
conda
virtual environment
(Miniconda installation instructions).
If conda
is installed on the system, an environment for installing pyknos
can be
created as follows:
$ conda create -n pyknos_env python=3.12 && conda activate pyknos_env
Independent of whether you are using conda
or not, pyknos
can be installed using pip
:
pip install pyknos
Examples
See the sbi
repository for examples of using pyknos.
Name
pyknós (πυκνός) is the transliterated Greek root for density (pyknótita) and also means sagacious.
Copyright notice
This program is free software: you can redistribute it and/or modify it under the terms of the Apache License 2.0., see LICENSE for more details.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
Acknowledgments
Thanks to Artur Bekasov, Conor Durkan and George Papamarkarios for their work on nflows.
The MDN implementation in this package is based on Conor M. Durkan's.
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