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author | Gard Spreemann <gspr@nonempty.org> | 2020-02-07 18:01:58 +0100 |
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committer | Gard Spreemann <gspr@nonempty.org> | 2020-02-07 18:01:58 +0100 |
commit | 81816dae256a9f3c0653b1d21443c3c32da7a974 (patch) | |
tree | 744e6821d9d0f37ff91067a5556496335d353bb4 /next_release.md | |
parent | fb6e4ca215fc814e74873852f63af028abcc5c17 (diff) | |
parent | cbe3d0d2b16e19048928ae308851c4312cca42c8 (diff) |
Merge tag 'tags/gudhi-release-3.1.1' into dfsg/latest
Diffstat (limited to 'next_release.md')
-rw-r--r-- | next_release.md | 28 |
1 files changed, 3 insertions, 25 deletions
diff --git a/next_release.md b/next_release.md index 08b736b1..78270d15 100644 --- a/next_release.md +++ b/next_release.md @@ -1,28 +1,6 @@ -We are pleased to announce the release 3.1.0 of the GUDHI library. - -As a major new feature, the GUDHI library now offers 2 new Python modules: Persistence representations and Wasserstein distance. - -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.3.1.0.rc1.tar.gz). - -Below is a list of changes made since Gudhi 3.0.0: - -- [Persistence representations](https://gudhi.inria.fr/python/3.1.0.rc1/representations.html) (new Python module) - - Vectorizations, distances and kernels that work on persistence diagrams, compatible with scikit-learn. This module was originally available at https://github.com/MathieuCarriere/sklearn-tda and named sklearn_tda. - -- [Wasserstein distance](https://gudhi.inria.fr/python/3.1.0.rc1/wasserstein_distance_user.html) (new Python module) - - The q-Wasserstein distance measures the similarity between two persistence diagrams. - -- [Alpha complex](https://gudhi.inria.fr/doc/3.1.0.rc1/group__alpha__complex.html) (new C++ interface) - - Thanks to [CGAL 5.0 Epeck_d](https://doc.cgal.org/latest/Kernel_d/structCGAL_1_1Epeck__d.html) kernel, an exact computation version of Alpha complex dD is available and the default one (even in Python). - -- [Persistence graphical tools](https://gudhi.inria.fr/python/3.1.0.rc1/persistence_graphical_tools_user.html) (new Python interface) - - Axes as a parameter allows the user to subplot graphics. - - Use matplotlib default palette (can be user defined). - -- Miscellaneous - - Python `read_off` function has been renamed `read_points_from_off_file` as it only reads points from OFF files. - - See the list of [bug fixes](https://github.com/GUDHI/gudhi-devel/issues?utf8=%E2%9C%93&q=is%3Aissue+label%3A3.1.0+). +gudhi-3.1.1 is a bug-fix release. In particular, it fixes the installation of the Python representation module. +The [list of bugs that were solved since gudhi-3.1.0](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.1.1+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. @@ -33,4 +11,4 @@ We provide [bibtex entries](https://gudhi.inria.fr/doc/latest/_citation.html) fo Feel free to [contact us](https://gudhi.inria.fr/contact/) in case you have any questions or remarks. -For further information about downloading and installing the library ([C++](https://gudhi.inria.fr/doc/3.1.0.rc1/installation.html) or [Python](https://gudhi.inria.fr/python/3.1.0.rc1/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). +For further information about downloading and installing the library ([C++](https://gudhi.inria.fr/doc/3.1.1/installation.html) or [Python](https://gudhi.inria.fr/python/3.1.1/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). |