We continue to add (full python) experimental models. In 1.15, we introduce the CTBN model (Continuous Time Bayesian Network) featuring, as usual, modelization and representation, inference (exact and sampling) and a learned algorithm.
* aGrUM
* Added `gum::NodeId gum::EssentialGraph::idFromName(const std::string& name)` and `const std::string&
gum::EssentialGraph::nameFromId(gum::NodeId node)`.
* pyAgrum
* Added `pyAgrum.EssentialGraph.idFromName(str)->int` and `pyAgrum.EssentialGraph.nameFromId(int)->str`
* Improved documentation of `pyAgrum.lib.explain`
* Better `pyAgrum.clg.CLG.toDot()` and `pyAgrum.clg.CLG._repr_html()`.
* New model Continuous Time Bayesian Network `pyAgrum.ctbn`.
* Formatted and adjustments in `pyAgrum.ctbn`.
* Updated documentations for python experimental models notebooks.
* Updated thumbnails for python experimental models notebooks.
* Added serialization (pickle) for CLG and CTBN (consistent with other models in pyAgrum).
* Improved `pyAgrum.lib.utils.{apply_}dot_layout`
* Added `pyAgrum.lib.utils.async_html2image` for exported HTML as png or pdf (notably for `pyAgrum.lib.notebook.getSideBySide` and `pyAgrum.lib.notebook.getPotential`).