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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](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).
For further information, please visit the [GUDHI web site](https://gudhi.inria.fr/).
# 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](https://gudhi.inria.fr/python/latest/cubical_complex_tflow_itf_ref.html)
- [lower-star persistence on simplex trees](https://gudhi.inria.fr/python/latest/ls_simplex_tree_tflow_itf_ref.html)
- [Rips complex](https://gudhi.inria.fr/python/latest/rips_complex_tflow_itf_ref.html)
- [Cubical complex](https://gudhi.inria.fr/python/latest/cubical_complex_sklearn_itf_ref.html)
- Cubical complex persistence scikit-learn like interface
- [Datasets](https://gudhi.inria.fr/python/latest/datasets.html)
- `datasets.remote.fetch_bunny` and `datasets.remote.fetch_spiral_2d` allows to fetch datasets from [GUDHI-data](https://github.com/GUDHI/gudhi-data)
- [Alpha complex](https://gudhi.inria.fr/python/latest/alpha_complex_user.html)
- 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](https://gudhi.inria.fr/python/latest/point_cloud.html#gudhi.read_points_from_off_file) instead.
- [Edge collapse](https://gudhi.inria.fr/doc/latest/group__edge__collapse.html)
- rewriting of the module to improve performance
- [Čech complex](https://gudhi.inria.fr/doc/latest/group__cech__complex.html)
- rewriting of the module to improve performance
- [Representations](https://gudhi.inria.fr/python/latest/representations.html#gudhi.representations.vector_methods.BettiCurve)
- A more flexible Betti curve class capable of computing exact curves
- [C++ documentation](https://gudhi.inria.fr/doc/latest/)
- upgrade and improve performance with new doxygen features
- [Simplex tree](https://gudhi.inria.fr/python/latest/simplex_tree_ref.html)
- `__deepcopy__`, `copy` and copy constructors for python module
- `expansion_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](https://github.com/GUDHI/gudhi-devel/issues?q=label%3A3.6.0+is%3Aclosed) is available on GitHub.
## Contributors
- @albert-github
- @gspr
- @Hind-M
- @MathieuCarriere
- @mglisse
- @Soriano-Trigueros
- @VincentRouvreau
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