./wip/py-dana, Framework for distributed, asynchronous and adaptive computing

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Branch: CURRENT, Version: 0.3.1, Package name: py312-dana-0.3.1, Maintainer: jihbed.research

DANA (Distributed (asynchronous) Numerical and Adaptive computing) is a python
computing framework based on numpy and scipy libraries whose primary goals
relate to computational neuroscience and artificial neural networks. However,
this framework can be used in several different areas like physic simulations,
cellular automata or image processing.

The computational paradigm supporting the DANA framework is grounded on the
notion of a unit that is a set of arbitrary values that can vary along time
under the influence of other units and learning. Each unit can be linked to any
other unit (including itself) using a weighted link and a group is a structured
set of such homogeneous units.


Required to run:
[graphics/py-matplotlib] [math/py-numpy] [lang/python37]

Required to build:
[pkgtools/cwrappers]

Master sites:

RMD160: 73705457509de15ab8c95d58cb292e29bb69887b
Filesize: 107.714 KB

Version history: (Expand)

CVS history: (Expand)


   2012-10-07 13:54:18 by Aleksej Saushev | Files touched by this commit (46) 
Log message:
Drop superfluous PKG_DESTDIR_SUPPORT, "user-destdir" is default these days.
Mark packages that don't or might probably not have staged installation.
   2011-03-20 23:51:12 by Kamel Derouiche | Files touched by this commit (4) | Imported package
Log message:
Import py26-dana-0.3.1 as wip/py-dana.

DANA (Distributed (asynchronous) Numerical and Adaptive computing) is a python
computing framework based on numpy and scipy libraries whose primary goals
relate to computational neuroscience and artificial neural networks. However,
this framework can be used in several different areas like physic simulations,
cellular automata or image processing.

The computational paradigm supporting the DANA framework is grounded on the
notion of a unit that is a set of arbitrary values that can vary along time
under the influence of other units and learning. Each unit can be linked to any
other unit (including itself) using a weighted link and a group is a structured
set of such homogeneous units.