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Distributed srm #220

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merged 14 commits into from May 11, 2017

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@mihaic mihaic

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mjanderson09 commented Apr 28, 2017

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Show outdated Hide outdated brainiak/funcalign/srm.py
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Show outdated Hide outdated tests/funcalign/test_srm.py

@mihaic mihaic requested a review from TuKo Apr 28, 2017

mjanderson09 added some commits May 1, 2017

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one random seed per rank
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fixed over indent
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Jenkins, retest this please.
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mihaic commented May 1, 2017

Jenkins, retest this please.
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Jenkins, retest this please.
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mihaic commented May 2, 2017

Jenkins, retest this please.
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@mjanderson09 great job!
Please, consider adding the TS-SVD and TS-QR code as well. We should allow for reproducibility of the IEEE BigData algorithm. Also, all SRM flavors will benefit from it, and some other methods as well.

Don't need to do it now. You can do it in a different PR.

Thank you!
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TuKo commented May 2, 2017

@mjanderson09 great job!
Please, consider adding the TS-SVD and TS-QR code as well. We should allow for reproducibility of the IEEE BigData algorithm. Also, all SRM flavors will benefit from it, and some other methods as well.

Don't need to do it now. You can do it in a different PR.

Thank you!

mjanderson09 added some commits May 3, 2017

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Added old srm test
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Gets same answer as v0_4
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np.allclose and fixed file path
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mihaic approved these changes May 10, 2017
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@TuKo, what say you?
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mihaic commented May 10, 2017

@TuKo, what say you?
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@TuKo says NIPS, sorry...
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TuKo commented May 10, 2017

@TuKo says NIPS, sorry...

mjanderson09 added some commits May 11, 2017

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merging random number PR
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👍 Great work!
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TuKo commented May 11, 2017 •
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👍 Great work!
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@mihaic mihaic merged commit 3e039bd into brainiak:master May 11, 2017
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For the record, here is the effect of the changes on the accuracy in the image prediction example:

v0.4:
SRM: The average accuracy among all subjects is 0.657143 +/- 0.058576
Det. SRM: The average accuracy among all subjects is 0.657143 +/- 0.070891

master (i.e., per-subject random number generators):
SRM: The average accuracy among all subjects is 0.664286 +/- 0.076097
Det. SRM: The average accuracy among all subjects is 0.673214 +/- 0.068721

distributed_srm:
SRM: The average accuracy among all subjects is 0.664286 +/- 0.076097
Det. SRM: The average accuracy among all subjects is 0.673214 +/- 0.068721
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mihaic commented May 11, 2017

For the record, here is the effect of the changes on the accuracy in the image prediction example:

v0.4:
SRM: The average accuracy among all subjects is 0.657143 +/- 0.058576
Det. SRM: The average accuracy among all subjects is 0.657143 +/- 0.070891

master (i.e., per-subject random number generators):
SRM: The average accuracy among all subjects is 0.664286 +/- 0.076097
Det. SRM: The average accuracy among all subjects is 0.673214 +/- 0.068721

distributed_srm:
SRM: The average accuracy among all subjects is 0.664286 +/- 0.076097
Det. SRM: The average accuracy among all subjects is 0.673214 +/- 0.068721

danielsuo pushed a commit that referenced this pull request Nov 16, 2017

@mehrdadn @pcmoritz
int and long should be treated similarly (#220)
199b4ef

danielsuo pushed a commit that referenced this pull request Nov 16, 2017

@Wapaul1 @pcmoritz
Added functionality for retrieving variables from control dependencies ( 
#220)

* Added test for retriving variables from an optimizer

* Added comments to test

* Addressed comments

* Fixed travis bug

* Added fix to circular controls

* Added set for explored operations and duplicate prefix stripping

* Removed embeded ipython

* Removed prefix, use seperate graph for each network

* Removed redundant imports

* Addressed comments and added separate graph to initializer

* fix typos

* get rid of prefix in documentation
db72978
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