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[MRG+2] refactor linear_regression_raw #3008
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LGTM |
@@ -91,6 +91,7 @@ def test_continuous_regression_no_overlap(): | |||
tmin, tmax = -.1, .5 | |||
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raw = mne.io.Raw(raw_fname, preload=True) | |||
raw.apply_proj() |
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why?
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We compare it to the regular evoked object and that one seemingly gets a projection applied (?). So this makes the objects we compare more similar.
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Will check, hopefully.
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Yes, it does :)
I think I want to factor out contructing the evokeds too, cause that makes it fairly easy to actually plugin in sklearn objects here. And I'd like to add an example for how to combine this and sklearn for a really easy encoder model validation. Sorry for going back and forth on MRG/WIP all the time :) |
Ready now. |
+1 for merge |
The 2nd Travis build is stuck ... |
Restarted it for you, will merge if it comes back happy |
[MRG+2] refactor linear_regression_raw
Thanks @jona-sassenhagen |
Thanks for reviews everyone! |
Factor out a bunch of helpers. Prerequisite for