Metadata-Version: 2.1
Name: pyknos
Version: 0.14.1
Summary: Conditional density estimation.
Home-page: https://github.com/mackelab/pyknos
Author: Álvaro Tejero-Cantero
Author-email: alvaro@minin.es
License: AGPLv3
Description: 
        [![PyPI version](https://badge.fury.io/py/pyknos.svg)](https://badge.fury.io/py/pyknos)
        [![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/mackelab/sbi/blob/master/CONTRIBUTING.md)
        [![GitHub license](https://img.shields.io/github/license/mackelab/pyknos)](https://github.com/mackelab/sbi/blob/master/LICENSE.txt)
        
        ## 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](https://github.com/bayesiains/nflows).
        
        ## Setup
        
        Clone the repo and install all the dependencies using the
        `environment.yml` file to create a conda environment: `conda env
        create -f environment.yml`. If you already have a `pyknos` environment
        and want to refresh dependencies, just run `conda env update -f
        environment.yml --prune`.
        
        Alternatively, you can install via `setup.py` using `pip install -e
        ".[dev]"` (the dev flag installs development and testing
        dependencies).
        
        ## Examples
        
        Examples are collected in notebooks in `examples/`.
        
        ## Binary files and Jupyter notebooks
        
        ### Using
        
        We use git lfs to store large binary files. Those files are not
        downloaded by cloning the repository, but you have to pull them
        separately. To do so follow installation instructions here
        [https://git-lfs.github.com/](https://git-lfs.github.com/). In
        particular, in a freshly cloned repository on a new machine, you will
        need to run both `git-lfs install` and `git-lfs pull`.
        
        ### Contributing
        
        We use a filename filter to identify large binary files. Once you
        installed and pulled git lfs you can add a file to git lfs by
        appending `_gitlfs` to the basename, e.g., `oldbase_gitlfs.npy`. Then
        add the file to the index, commit, and it will be tracked by git lfs.
        
        Additionally, to avoid large diffs due to Jupyter notebook outputs we
        are using `nbstripout` to remove output from notebooks before every
        commit. The `nbstripout` package is downloaded automatically during
        installation of `pyknos`. However, **please make sure to set up the
        filter yourself**, e.g., through `nbstriout --install` or with
        different options as described
        [here](https://github.com/kynan/nbstripout).
        
        ## 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 GNU Affero General Public License as published by
        the Free Software Foundation, either version 3 of the License, or
        (at your option) any later version.
        
        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.
        
        You should have received a copy of the GNU Affero General Public License
        along with this program.  If not, see <https://www.gnu.org/licenses/>.
        
        ## Acknowledgements
        
        Thanks to Artur Bekasov, Conor Durkan and George Papamarkarios for
        their work on [nflows](https://github.com/bayesiains/nflows).
        
        The MDN implementation in this package is by Conor M. Durkan.
Keywords: conditional density estimation PyTorch normalizing flows mdn
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Adaptive Technologies
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
