ENH: optimize memory for euclidean-distance estimation #6
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Contributor
Author
|
I made also a similar improvement in compute_var_cov_pt function inside compute_DCBC_pt. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
The computation of the euclidean distance can be very memory intensive if a and b are large. This is because the matrix dist has a shape of (N,N), where N is the number of samples (e.g., cortical nodes), and its size grows quadratically with N. In long analysis pipelines, the system can run in insufficient memory, killing the process. Here, there's an updated version of the euclidean_distance function that delivers identical results compared with the original one.