New release
GUDHI version 3.6.0
We are pleased to announce the release 3.6.0 of the GUDHI library.
As a major new feature, the GUDHI library now offers automatic differentiation for the computation of persistence diagrams, Cubical complex persistence scikit-learn like interface, datasets fetch methods, and weighted version for alpha complex in any dimension D.
Do not hesitate to fork the GUDHI project on GitHub. From a user point of view, we recommend to download GUDHI user version (gudhi.3.X.X.tar.gz).
GUDHI 3.6.0 Release Notes
Below is a list of changes made since GUDHI 3.5.0:
- TensorFlow 2 models that can handle automatic differentiation for the computation of persistence diagrams:
- Cubical complex
- Cubical complex persistence scikit-learn like interface
- Datasets
datasets.remote.fetch_bunny
anddatasets.remote.fetch_spiral_2d
allows to fetch datasets from GUDHI-data
- Alpha complex
- python weighted version for alpha complex is now available in any dimension D.
alpha_complex = gudhi.AlphaComplex(off_file='/data/points/tore3D_300.off')
is deprecated, please use read_points_from_off_file instead.
- Edge collapse
- rewriting of the module to improve performance
- Čech complex
- rewriting of the module to improve performance
- Representations
- A more flexible Betti curve class capable of computing exact curves
- C++ documentation
- upgrade and improve performance with new doxygen features
- Simplex tree
__deepcopy__
,copy
and copy constructors for python moduleexpansion_with_blockers
python interface
- Installation
- Boost ≥ 1.66.0 is now required (was ≥ 1.56.0).
- Python >= 3.5 and cython >= 0.27 are now required.
- Miscellaneous
- The list of bugs that were solved since GUDHI-3.5.0 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 for further details.
We kindly ask users to cite the GUDHI library as appropriately as possible in their papers, and to mention the use of the GUDHI library on the web pages of their projects using GUDHI and provide us with links to these web pages.
We provide bibtex entries for the modules of the User and Reference Manual, as well as for publications directly related to the GUDHI library.
Feel free to contact us in case you have any questions or remarks.
For further information about downloading and installing the library (C++ or Python), please visit the GUDHI web site.
RELEASE
GUDHI release