Use of discrete dynamical systems within recurrent neural networks
Project description
Python library to build and use discrete dynamical systems within recurrent neural networks. This library is a user-friendly tool made to use discrete dynamical systems such as Binary ECA, 3 states ECA and CML as a reservoir. Each type of reservoir has its hyper-parameters to enhance the reservoir performance.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for Discrete_Dynamical_Reservoir-0.1.5.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 34aa6bc314ebaef1b3a052a5fdedadccfafe3d94c9649a14350f14ad6dc01a3e |
|
MD5 | a25a59356d0514dc17e343b02f8d8e0b |
|
BLAKE2b-256 | 1406d875f926f7051239cddddedf11e887bddfdaa195eb39888e521e48227185 |
Close
Hashes for Discrete_Dynamical_Reservoir-0.1.5-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9e440f5ac42d50d5b938d66b8878a9b09c82711cf1598d4a61274e790f63e4fe |
|
MD5 | 4be81e06fe5a6b6aeee60ef775b2980b |
|
BLAKE2b-256 | fe76e19a1da2067c4fc08abb45b435155bc1fac265d39c5adf2c94f93810188e |