_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
agrum 3.0.0
Propagated dependencies: python-matplotlib@3.10.8 python-matplotlib-inline@0.1.7 python-numpy@2.3.1 python-pydot@4.0.1 python-scikit-learn@1.7.2
Channel: guix-science
Location: guix-science/packages/machine-learning.scm (guix-science packages machine-learning)
Home page: https://pyagrum.gitlab.io/
Licenses: LGPL 3+ Expat
Build system: cmake
Synopsis: C++ Library for Probabilistic Graphical Models
Description:

aGrUM is a C++ library for graphical models. It is designed for easily building applications using graphical models such as Bayesian networks, influence diagrams, credal networks, Markov random fields, decision trees, GAI networks, (Factored) Markov decision processes, etc.

Features:

  • Dedicated data structures

  • Lightweight directed/undirected graphs

  • Extensible multidimensional matrix

  • Bayesian Network algorithms

  • Research tools (random generation, introspection)

  • Integration tools (listeners, multiple formats)

Total packages: 1