peewit intends to facilitate programming, running and result examination of machine learning experiments. It does not provide any sort of ML data types, engine, or interfaces, but it gives assistance in avoiding mess. The user is to decompose the experimental code into named nodes that correpsond to aspects or dimension of an experiment. Since this breakdown is readable for machines too, simple services can be precoded that help to keep track of the dimensions. peewit also puts some regime on output files, helps in recovering former versions, and handles simple parallelization.
Be aware that the implementation has deficiencies: it is weakly tested, many doc-string are missing, erroneous calls are not always caught by helpful error messages, and there is much left for smaller and greater amendments. At least, the examples are decorated with many explicatory comments.
The core modules depend on python 2.x and numpy only but for full functionality you further have to provide unison, ssh with agent, git, and matplotlib, as well as libsvm for the example project. Feel free to contact us for questions.
- Changes to previous version:
Other available revisons
Version Changelog Date 0.9
switched to python-3
February 11, 2013, 21:21:05 0.8
July 9, 2012, 23:32:20 0.7
November 4, 2011, 19:54:09 0.6
September 18, 2010, 16:22:01 0.5
August 18, 2010, 14:00:21 0.4
platform-independent path handling
July 21, 2010, 15:05:31 0.3
incremental e-tree definitions
May 12, 2010, 21:44:21 0.2
self-referential descent inputs
May 4, 2010, 13:30:36 0.1
services for arg-aggregations
April 26, 2010, 11:17:50 0.0
Initial Announcement on mloss.org.
April 23, 2010, 23:14:51
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