aGrUM/pyAgrum 1.0.0 released

Posted on mer. 20 avril 2022 in News


aGrUM/pyAgrum 1.0.0 is out.

Changelog for 1.0.0

The aGrUM's team is very proud to announce the release of aGrUM/pyAgrum 1.0.0!

This long journey sometimes strewn with pitfalls, but which often brought great satisfactions, began with this first commit (subversion):

add8dbff5 | 13 years ago | phw | test?

aGrUM/pyAgrum is now definitely no longer a test :-) Parallelization of inference in Bayesian networks is the important feature that allows us to symbolically assert a level of quality of aGrUM. The 1.0.0 release shows that we also believe the API will remain stable in a medium term.

pyAgrum continues its path as a wrapper of aGrUM but with more and more specific features. Some of them are intended to be implemented in C++ and eventually integrated in aGrUM (causality, etc.), others will certainly remain specificities of pyAgrum.

In the near future, for aGrUM and pyAgrum, we expect many more new features and releases ! Stay tuned!

  • aGrUM

    • VariableElimination, ShaferShenoy and LazyPropagation are now parallelized.
    • Better use of d-separation in ShaferShenoy and LazyPropagation.
    • Better initialization/registrations using Meyers singleton.
    • Better 0-dimensional Potential.
    • new gum::IBayesNet::check() to test if the BN is completely and well-defined.
  • pyAgrum

    • Better documentation and argument order for gum.BNLearner.__init__.
    • Better numpystyle for docstrings.
    • Better tests and notebooks.
    • Better signature for gum.Potential.arg{max|min}.
    • New gum.IBayesNet.check() to test if the BN is completely and well-defined.
    • More consistent API : with_labels default is True everywhere.