Peewit intends to facilitate programming, running and result examination of machine learning experiments. It does not provide any sort of ML code but also makes little assumptions on the sort of experiments the user wants to accomplish. Can it be of any use then?
That is the question we want to pursue with peewit. The concept of the framework is based on two observations:
1) Machine learning experiment often have a regular tree structure.
2) Experimentors happen to puzzle about which numbers belong to what parameters the next day.
Peewit is restricted to experiments that fulfill a certain uniformity in the relation of the experimental components. It demands from the user to term things and rewards this by an increased live-time of names.
Be aware that the implementation still is raw: not all methods come with a doc-strings and erroneous calls are rarely caught by helpful error messages. At least, the examples are decorated with many explicatory comments.
- Changes to previous version:
Other available revisons
Version Changelog Date 0.10
v-cube with side-cubes
May 7, 2014, 16:04:18 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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