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author | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-07-02 21:11:18 +0200 |
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committer | ROUVREAU Vincent <vincent.rouvreau@inria.fr> | 2020-07-02 21:11:18 +0200 |
commit | 0f364d372a6bce81d895d4ccd066174bad260e9e (patch) | |
tree | c9067237d5790125fbcbb8fd784064f2b413a5ee /.github | |
parent | eedb34f25d76cb3dc7ccb6b59a60217a26eedfcd (diff) | |
parent | 3c7a4d01ec758d68a219fae8981c9847cf8d7a0f (diff) |
Merge branch 'master' into edge_collapse_integration_vincent
Diffstat (limited to '.github')
-rw-r--r-- | .github/next_release.md | 35 | ||||
-rw-r--r-- | .github/test-requirements.txt | 2 |
2 files changed, 27 insertions, 10 deletions
diff --git a/.github/next_release.md b/.github/next_release.md index a2805a55..cc868db7 100644 --- a/.github/next_release.md +++ b/.github/next_release.md @@ -1,19 +1,36 @@ -We are pleased to announce the release 3.X.X of the GUDHI library. +We are pleased to announce the release 3.3.0 of the GUDHI library. -As a major new feature, the GUDHI library now offers ... +As a major new feature, the GUDHI library now offers a persistence-based clustering algorithm and weighted Rips complex using DTM. -We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz). +We are now using GitHub to develop the GUDHI library, do not hesitate to [fork the GUDHI project on GitHub](https://github.com/GUDHI/gudhi-devel). From a user point of view, we recommend to download GUDHI user version (gudhi.3.3.0.tar.gz). -Below is a list of changes made since GUDHI 3.X-1.X-1: +Below is a list of changes made since GUDHI 3.2.0: -- [Module](link) - - ... +- [DTM density estimator](https://gudhi.inria.fr/python/latest/point_cloud.html#module-gudhi.point_cloud.dtm) + - Python implementation of a density estimator based on the distance to the empirical measure defined by a point set. -- [Module](link) - - ... +- [DTM Rips complex](https://gudhi.inria.fr/python/latest/rips_complex_user.html#dtm-rips-complex) + - This Python implementation constructs a weighted Rips complex giving larger weights to outliers, + which reduces their impact on the persistence diagram + +- [Alpha complex](https://gudhi.inria.fr/python/latest/alpha_complex_user.html) - Python interface improvements + - 'fast' and 'exact' computations + - Delaunay complex construction by not setting filtration values + - Use the specific 3d alpha complex automatically to make the computations faster + +- [Clustering](https://gudhi.inria.fr/python/latest/clustering.html) + - Python implementation of [ToMATo](https://doi.org/10.1145/2535927), a persistence-based clustering algorithm + +- [Bottleneck distance](https://gudhi.inria.fr/python/latest/bottleneck_distance_user.html) + - Python interface to [hera](https://github.com/grey-narn/hera)'s bottleneck distance + +- Representations - Python interface change + - [Wasserstein metrics](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.metrics.WassersteinDistance) + is now [hera](https://github.com/grey-narn/hera) by default - Miscellaneous - - The [list of bugs that were solved since GUDHI-3.X-1.X-1](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.1.1+is%3Aclosed) is available on GitHub. + - The [list of bugs that were solved since GUDHI-3.2.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.3.0+is%3Aclosed) + is available on GitHub. All modules are distributed under the terms of the MIT license. However, there are still GPL dependencies for many modules. We invite you to check our [license dedicated web page](https://gudhi.inria.fr/licensing/) for further details. diff --git a/.github/test-requirements.txt b/.github/test-requirements.txt index fb1df134..98f1007e 100644 --- a/.github/test-requirements.txt +++ b/.github/test-requirements.txt @@ -7,7 +7,7 @@ scipy scikit-learn POT tensorflow -torch +torch<1.5 pykeops hnswlib eagerpy |