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diff --git a/RELEASES.md b/RELEASES.md new file mode 100644 index 0000000..58712c8 --- /dev/null +++ b/RELEASES.md @@ -0,0 +1,88 @@ +# POT Releases + +## 0.4 Community edition +*15 Sep 2017* + +This release contains a lot of contribution from new contributors. + + +#### Features + +* Automatic notebooks and doc update (PR #27) +* Add gromov Wasserstein solver and Gromov Barycenters (PR #23) +* emd and emd2 can now return dual variables and have max_iter (PR #29 and PR #25) +* New domain adaptation classes compatible with scikit-learn (PR #22) +* Proper tests with pytest on travis (PR #19) +* PEP 8 tests (PR #13) + +#### Closed issues + +* emd convergence problem du to fixed max iterations (#24) +* Semi supervised DA error (#26) + +## 0.3.1 +*11 Jul 2017* + +* Correct bug in emd on windows + +## 0.3 Summer release +*7 Jul 2017* + +* emd* and sinkhorn* are now performed in parallel for multiple target distributions +* emd and sinkhorn are for OT matrix computation +* emd2 and sinkhorn2 are for OT loss computation +* new notebooks for emd computation and Wasserstein Discriminant Analysis +* relocate notebooks +* update documentation +* clean_zeros(a,b,M) for removimg zeros in sparse distributions +* GPU implementations for sinkhorn and group lasso regularization + + +## V0.2 +*7 Apr 2017* + +* New dimensionality reduction method (WDA) +* Efficient method emd2 returns only tarnsport (in paralell if several histograms given) + + + +## V0.1.11 New years resolution +*5 Jan 2017* + +* Add sphinx gallery for better documentation +* Small efficiency tweak in sinkhorn +* Add simple tic() toc() functions for timing + + +## V0.1.10 +*7 Nov 2016* +* numerical stabilization for sinkhorn (log domain and epsilon scaling) + +## V0.1.9 DA classes and mapping +*4 Nov 2016* + +* Update classes and examples for domain adaptation +* Joint OT matrix and mapping estimation + +## V0.1.7 +*31 Oct 2016* + +* Original Domain adaptation classes + + + +## PyPI version 0.1.3 + +* pipy works + +## First pre-release +*28 Oct 2016* + +It provides the following solvers: +* OT solver for the linear program/ Earth Movers Distance. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm. +* Bregman projections for Wasserstein barycenter [3] and unmixing. +* Optimal transport for domain adaptation with group lasso regularization +* Conditional gradient and Generalized conditional gradient for regularized OT. + +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. |