Kernel centres semi-stratified selection without replacement#21
Open
mierzejk wants to merge 3 commits intohoxo-m:masterfrom
Open
Kernel centres semi-stratified selection without replacement#21mierzejk wants to merge 3 commits intohoxo-m:masterfrom
mierzejk wants to merge 3 commits intohoxo-m:masterfrom
Conversation
- use np.random.choice to sample input data without replacement; - use np.percentile so samples are stratified with respect to (possibly multivariate) x values.
Contributor
Author
|
Please note that all commits covered by this pull request are also included in #23 Aggregated pull request. |
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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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 pull request revises the way kernel centres are selected. The following changes have been introduced:
xvalues.I call this manner of centre selection semi-stratified, because the final result is concatenated independently from every column of the second array dimension, where indices are chosen randomly from quantiles returned by np.percentile.
Resolves the following issues: