[WIP] Tree Wasserstein distances and Sliced Tree Wasserstein#821
Draft
Flastre wants to merge 7 commits into
Draft
[WIP] Tree Wasserstein distances and Sliced Tree Wasserstein#821Flastre wants to merge 7 commits into
Flastre wants to merge 7 commits into
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
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
Wasserstein distances on trees.
In ot/lp/solver_tree.py : the function tree_wasserstein return the distance and the transport plan between two distributions on a tree
In ot/lp/tree_barycenter : the function tree_barycenter returns the barycenter between multiples distributions on a tree
I plan on adding a sliced version of the tree wasserstein distance, and maybe some functions to generate trees (as explained in the first article)
References :
Tree-Sliced Variants of Wasserstein Distances
Tree-Wasserstein Barycenter for Large-Scale Multilevel Clustering and Scalable Bayes