[doc] Updated doc to match 2.0 release

doc/conf.py

@@ -21,7 +21,7 @@ sys.path.append(os.path.abspath('./ext'))
 # -- Project information -----------------------------------------------------
 
 project = 'CAISAR'
-copyright = '2023, The CAISAR Development Team'
+copyright = '2024, The CAISAR Development Team'
 author = 'The CAISAR Development Team'
 
 # The version info for the project you're documenting, acts as replacement for
@@ -29,9 +29,9 @@ author = 'The CAISAR Development Team'
 # built documents.
 #
 # The short X.Y version.
-version = '1.0'
+version = '2.0'
 # The full version, including alpha/beta/rc tags.
-release = '1.0'
+release = '2.0'
 
 
 # -- General configuration ---------------------------------------------------

doc/contributing.rst

@@ -27,6 +27,7 @@ The root of the repository has the following folders:
 * ``stdlib/`` contains WhyML files used to define
   theories for the CAISAR interpreted language.
 * ``tests/`` are non-regression tests written in `dune cram test syntax <https://dune.readthedocs.io/en/stable/tests.html>`_.
+* ``utils/`` are helper functions used internally by CAISAR (for instance, logging utilities).
 
 
 Setting up a development environment

doc/index.rst

@@ -18,12 +18,13 @@ The CAISAR Platform
   François Bobot,
   Julien Girard
 
-:Version: |version|, December 2023
-:Copyright: 2020--2023 CEA (Commissariat à l'énergie atomique et aux énergies alternatives)
+:Version: |version|, June 2024
+:Copyright: 2020--2024 CEA (Commissariat à l'énergie atomique et aux énergies alternatives)
 
 .. _Confiance.ai: https://www.confiance.ai/
+.. _DeepGreen: https://www.deepgreen.ai/
 
-This work has been partly supported by the `Confiance.ai`_ program.
+This work has been partly supported by the `Confiance.ai`_ program and the `DeepGreen`_ project.
 
 .. toctree::
    :maxdepth: 2

doc/interpretation.rst

@@ -109,12 +109,12 @@ Functions
 
 .. code-block:: whyml
 
-  (** Returns a symbol for the neural network with filename n. **)
-  function read_model (n: string) : model nn
+  (** Returns a symbol for the machine learning model with filename n. **)
+  function read_model (n: string) : model
 
-  (** Returns a vector that represents the application of neural network
+  (** Returns a vector that represents the application of a model
   on an input vector. **)
-  function (@@) (n: model nn) (v: vector 'a) : vector 'a
+  function (@@) (n: model) (v: vector 'a) : vector 'a
 
 Dataset
 *******