{ "info": { "author": "Guillaume Gautier", "author_email": "guillaume.gautier@univ-lille1.fr", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Operating System :: MacOS", "Operating System :: Unix", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "DPPy: Sampling Determinantal Point Processes with Python\n========================================================\n\n|Documentation Status| |Build Status| |Coverage Status|\n\n.. |Documentation Status| image:: https://readthedocs.org/projects/dppy/badge/?version=latest\n :target: https://dppy.readthedocs.io/en/latest/?badge=latest\n\n.. |Build Status| image:: https://travis-ci.com/guilgautier/DPPy.svg?branch=master\n :target: https://travis-ci.com/guilgautier/DPPy\n\n.. |Coverage Status| image:: https://coveralls.io/repos/github/guilgautier/DPPy/badge.svg?branch=master\n :target: https://coveralls.io/github/guilgautier/DPPy?branch=master\n\n.. |Google Colab| image:: https://badgen.net/badge/Launch/on%20Google%20Colab/blue?icon=terminal\n :target: https://colab.research.google.com/github/guilgautier/DPPy/blob/master/notebooks/Tuto_DPPy.ipynb\n\n*\"Anything that can go wrong, will go wrong\"* \u2212 `Murphy's Law `_\n\nIntroduction\n------------\n\nDeterminantal point processes (DPPs) are specific probability\ndistributions over clouds of points that have been popular as models or\ncomputational tools across physics, probability, statistics, and more\nrecently of booming interest in machine learning. Sampling from DPPs is\na nontrivial matter, and many approaches have been proposed. DPPy is a\n`Python `__ library that puts together all\nexact and approximate sampling algorithms for DPPs.\n\nRequirements\n------------\n\nDPPy works with `Python 3.4+ `__.\n\nDependencies\n~~~~~~~~~~~~\n\n- `NumPy `__\n- `SciPy `__\n- `Matplotlib `__\n- `Networkx `__ to play with `uniform\n spanning\n trees `__\n- `CVXOPT `__ to use the ``zono_sampling`` MCMC\n sampler for finite DPPs. **CVXOPT itself requires**\n `GCC `__,\n\n - On MAC it comes with\n `Xcode `__\n - On UNIX, use your package manager (``apt``, ``yum`` etc)\n\n .. code:: bash\n\n sudo apt install -qq gcc g++\n\nInstallation\n------------\n\nDPPy is now available on `PyPI `__\n\n.. code:: bash\n\n pip install dppy\n\nHowever you may not work with the latest version, so\n\n1. If you have a GitHub account\n\n - Please consider forking DPPy\n - Use git to clone your copy of the repo\n\n .. code:: bash\n\n cd \n git clone https://github.com//DPPy.git\n\n2. If you only use git, clone this repository\n\n .. code:: bash\n\n cd \n git clone https://github.com/guilgautier/DPPy.git\n\n3. Otherwise simply dowload the project\n\n4. In any case, install the project with\n\n .. code:: bash\n\n cd DPPy\n pip install .\n\nTutorials in `Jupyter notebooks `_\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nYou can read and work on these interactive tutorial `Notebooks `_, directly from your\nweb browser, without having to download or install Python or anything.\nJust click, wait a little bit, and play with the notebook!\n\nContribute to the documentation\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nThe\n`documentation `__\nis generated locally with\n`Sphinx `__ and then built online\nby `ReadTheDocs `__.\n\nIf you wish to contribute to the documentation or just play with it\nlocally, you can:\n\n- Install Sphinx\n\n .. code:: bash\n\n pip install -U sphinx\n\n- Generate the docs locally\n\n .. code:: bash\n\n cd DPPy/docs\n make html\n\n- Open the local HTML version of the documentation located at\n ``DPPy/docs/_build/html/index.html``\n\n .. code:: bash\n\n open _build/html/index.html\n\nHow to cite this work?\n~~~~~~~~~~~~~~~~~~~~~~\n\nWe wrote a companion paper to\n`DPPy `__ for latter submission to\nthe `MLOSS `__ track of JMLR.\n\nThe companion paper is available on\n\n- `arXiv `__\n- `GitHub `__ for the lastest version\n\nIf you use this package, please consider citing it with this piece of\nBibTeX:\n\n.. code:: bibtex\n\n @article{GPBV18,\n archivePrefix = {arXiv},\n arxivId = {1809.07258},\n author = {Gautier, Guillaume and Polito, Guillermo and Bardenet, R{\\'{e}}mi and Valko, Michal},\n eprint = {1809.07258},\n journal = {ArXiv e-prints},\n title = {{DPPy: Sampling Determinantal Point Processes with Python}},\n keywords = {Computer Science - Machine Learning, Computer Science - Mathematical Software, Statistics - Machine Learning},\n url = {http://arxiv.org/abs/1809.07258},\n year = {2018},\n note = {Code at http://github.com/guilgautier/DPPy/ Documentation at http://dppy.readthedocs.io/}\n }\n\nReproducibility\n---------------\n\nWe 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