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===============
cynet
===============
.. figure:: https://pypistats.com/badge/cynet.png
:alt: cynet PyPI Downloads
.. image:: http://zed.uchicago.edu/logo/logozed1.png
:height: 400px
:scale: 50 %
:alt: alternate text
:align: center
.. class:: no-web no-pdf
:Info: See <https://arxiv.org/abs/1406.6651> for theoretical background
:Author: ZeD@UChicago <zed.uchicago.edu>
:Description: Implementation of the Deep Granger net inference algorithm, described in https://arxiv.org/abs/1406.6651, for learning spatio-temporal stochastic processes (*point processes*). **cynet** learns a network of generative local models, without assuming any specific model structure.
cynet
===============
.. figure:: https://pypistats.com/badge/cynet.png
:alt: cynet PyPI Downloads
.. image:: http://zed.uchicago.edu/logo/logozed1.png
:height: 400px
:scale: 50 %
:alt: alternate text
:align: center
.. class:: no-web no-pdf
:Info: See <https://arxiv.org/abs/1406.6651> for theoretical background
:Author: ZeD@UChicago <zed.uchicago.edu>
:Description: Implementation of the Deep Granger net inference algorithm, described in https://arxiv.org/abs/1406.6651, for learning spatio-temporal stochastic processes (*point processes*). **cynet** learns a network of generative local models, without assuming any specific model structure.
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