aGrUM/pyAgrum 1.0.0 released

ANNOUNCE: aGrUM 1.0.0

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.