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NF: Added alignment of datasets based on STATIS with tests #362
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:return: | ||
""" | ||
if type(dss) == list: | ||
print("Processing each of %d list items as tables"%(len(dss))) |
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can this be a debug statement?
Thanks @swaroopgj, this looks very nice. I've added a few minor comments. |
Added more tests and incorporated suggestions by @nno |
and other stuff within helper functions for statis
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@@ -347,6 +347,7 @@ def _set_max_count(self, value): | |||
"needed for current refactoring carried out in tent/flexds") | |||
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debug.register('BM', "Benchmark") | |||
debug.register('STATIS', "Static algorithm") |
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typo: Stati[s]
also list this as new feature in the ChangeLog please |
Made changes as suggested by @yarikoptic |
Thanks @swaroopgj. |
Seems to be some fluke unrelated to these changes... Didn't look into details though to figure it out On October 26, 2015 8:30:48 PM EDT, "Nikolaas N. Oosterhof" notifications@github.com wrote:
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Was about to merge but then -- is this really the Statis or an alignment based on Statis (and thus better be StatisAlignment or smth like that)? also add it to suite.py |
It returns mappers for alignment. However, it does pretty much what is in the STATIS paper except plotting the results. The way I envisioned is that it returns mappers, but the object can be used to plot by storing required matrices. Clearly, more work needs to be added for that ;) Quoting my initial PR description: |
I implemented simple alignment based on STATIS in the lines of Hyperalignment based on [0].
It also computes similarity of representations across subjects and each subject's contribution to compromise similarity matrix.
I also added unittest for basic functionality similar to hyperalignment.
This could be extended to include bootstrapped permutations to produce MDS-like plots with confidence ellipses as shown in [0].
[0] Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multi-table principal component analysis and three way metric multidimensional scaling. Wiley Interdisciplinary Reviews: Computational Statistics, 4, 124-167.