diff options
Diffstat (limited to '.github/next_release.md')
-rw-r--r-- | .github/next_release.md | 68 |
1 files changed, 33 insertions, 35 deletions
diff --git a/.github/next_release.md b/.github/next_release.md index d3c9ce68..14546066 100644 --- a/.github/next_release.md +++ b/.github/next_release.md @@ -1,50 +1,47 @@ -We are pleased to announce the release 3.2.0 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 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. +As a major new feature, the GUDHI library now offers a persistence-based clustering algorithm, weighted Rips complex using DTM +and edge collapse. -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). +The GUDHI library is hosted on GitHub, 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 3.1.1: +Below is a list of changes made since GUDHI 3.2.0: -- 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 +- [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. -- [Persistence representations](https://gudhi.inria.fr/python/latest/representations.html) - - Interface to Wasserstein distances. +- [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 -- 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. +- [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 -- [Wassertein distance](https://gudhi.inria.fr/python/latest/wasserstein_distance_user.html) - - 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. +- [Clustering](https://gudhi.inria.fr/python/latest/clustering.html) + - Python implementation of [ToMATo](https://doi.org/10.1145/2535927), a persistence-based clustering algorithm -- [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 +- [Edge Collapse](https://gudhi.inria.fr/doc/latest/group__edge__collapse.html) of a filtered flag complex + - This C++ implementation reduces a filtration of Vietoris-Rips complex from its graph to another smaller + flag filtration with the same persistence. -- [Alpha complex](https://gudhi.inria.fr/doc/latest/group__alpha__complex.html) - - Improve computations (cache circumcenters computation and point comparison improvement) +- [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 -- [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 +- Persistence representations + - [Atol](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.vector_methods.Atol) + is integrated in finite vectorisation methods. This + [article](https://www.fujitsu.com/global/about/resources/news/press-releases/2020/0316-01.html) talks about + applications using Atol. This module was originally available at + [https://github.com/martinroyer/atol](https://github.com/martinroyer/atol) + - 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-3.2.0](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%3A3.3.0+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. @@ -56,3 +53,4 @@ 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/latest/installation.html) or [Python](https://gudhi.inria.fr/python/latest/installation.html)), please visit the [GUDHI web site](https://gudhi.inria.fr/). + |