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authorVincent Rouvreau <10407034+VincentRouvreau@users.noreply.github.com>2020-05-18 22:06:42 +0200
committerGitHub <noreply@github.com>2020-05-18 22:06:42 +0200
commit5ed252a8758723f85840aee9874a191bd6cb1afc (patch)
treec5d7043cc57e8d6c3271b1f158b66eb3899a97a6
parent6b7af922694eebfb0682a40038161bb043aba59d (diff)
parent3e52b65a55b615929556597acc963246f76475ff (diff)
Merge pull request #316 from VincentRouvreau/gudhi_3_2_0_release_note
Release note for version 3.2.0
-rw-r--r--.github/next_release.md42
-rw-r--r--CMakeGUDHIVersion.txt4
2 files changed, 37 insertions, 9 deletions
diff --git a/.github/next_release.md b/.github/next_release.md
index 83b98a1c..d3c9ce68 100644
--- a/.github/next_release.md
+++ b/.github/next_release.md
@@ -1,21 +1,50 @@
-We are pleased to announce the release 3.X.X of the GUDHI library.
+We are pleased to announce the release 3.2.0 of the GUDHI library.
As a major new feature, the GUDHI library now offers a Python interface to [Hera](https://bitbucket.org/grey_narn/hera/src/master/) to compute the Wasserstein distance.
[PyBind11](https://github.com/pybind/pybind11) is now required to build the Python module.
-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.3.2.0.tar.gz).
Below is a list of changes made since GUDHI 3.1.1:
+- Point cloud utilities
+ - A new module [Time Delay Embedding](https://gudhi.inria.fr/python/latest/point_cloud.html#time-delay-embedding)
+ to embed time-series data in the R^d according to [Takens' Embedding Theorem](https://en.wikipedia.org/wiki/Takens%27s_theorem)
+ and obtain the coordinates of each point.
+ - A new module [K Nearest Neighbors](https://gudhi.inria.fr/python/latest/point_cloud.html#k-nearest-neighbors)
+ that wraps several implementations for computing the k nearest neighbors in a point set.
+ - A new module [Distance To Measure](https://gudhi.inria.fr/python/latest/point_cloud.html#distance-to-measure)
+ to compute the distance to the empirical measure defined by a point set
+
+- [Persistence representations](https://gudhi.inria.fr/python/latest/representations.html)
+ - Interface to Wasserstein distances.
+
+- Rips complex
+ - A new module [Weighted Rips Complex](https://gudhi.inria.fr/python/latest/rips_complex_user.html#weighted-rips-complex)
+ to construct a simplicial complex from a distance matrix and weights on vertices.
+
- [Wassertein distance](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html)
- - An another implementation comes from Hera (BSD-3-Clause) which is based on [Geometry Helps to Compare Persistence Diagrams](http://doi.acm.org/10.1145/3064175) by Michael Kerber, Dmitriy Morozov, and Arnur Nigmetov.
+ - An [another implementation](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html#hera)
+ comes from Hera (BSD-3-Clause) which is based on [Geometry Helps to Compare Persistence Diagrams](http://doi.acm.org/10.1145/3064175)
+ by Michael Kerber, Dmitriy Morozov, and Arnur Nigmetov.
- `gudhi.wasserstein.wasserstein_distance` has now an option to return the optimal matching that achieves the distance between the two diagrams.
+ - A new module [Barycenters](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html#barycenters)
+ to estimate the Frechet mean (aka Wasserstein barycenter) between persistence diagrams.
-- [Module](link)
- - ...
+- [Simplex tree](https://gudhi.inria.fr/python/latest/simplex_tree_ref.html)
+ - Extend filtration method to compute extended persistence
+ - Flag and lower star persistence pairs generators
+ - A new interface to filtration, simplices and skeleton getters to return an iterator
+
+- [Alpha complex](https://gudhi.inria.fr/doc/latest/group__alpha__complex.html)
+ - Improve computations (cache circumcenters computation and point comparison improvement)
+
+- [Persistence graphical tools](https://gudhi.inria.fr/python/latest/persistence_graphical_tools_user.html)
+ - New rendering option proposed (use LaTeX style, add grey block, improved positioning of labels, etc.).
+ - Can now handle (N x 2) numpy arrays as input
- Miscellaneous
- - The [list of bugs that were solved since GUDHI-3.1.1](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.2.0+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%3A3.2.0+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.
@@ -27,4 +56,3 @@ 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/latest/installation.html) or [Python](https://gudhi.inria.fr/python/latest/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/).
-
diff --git a/CMakeGUDHIVersion.txt b/CMakeGUDHIVersion.txt
index 0f827b9e..cf9fd4f7 100644
--- a/CMakeGUDHIVersion.txt
+++ b/CMakeGUDHIVersion.txt
@@ -1,6 +1,6 @@
set (GUDHI_MAJOR_VERSION 3)
-set (GUDHI_MINOR_VERSION 1)
-set (GUDHI_PATCH_VERSION 1)
+set (GUDHI_MINOR_VERSION 2)
+set (GUDHI_PATCH_VERSION 0.rc2)
set(GUDHI_VERSION ${GUDHI_MAJOR_VERSION}.${GUDHI_MINOR_VERSION}.${GUDHI_PATCH_VERSION})
message(STATUS "GUDHI version : ${GUDHI_VERSION}")