Major restructuring of the package

.github/workflows/Tests.yml

@@ -47,8 +47,7 @@ jobs:
           cp -a $GITHUB_WORKSPACE /home/gap/.gap/pkg/
       - name: Prepare environment
         run: |
-          cp ./GradientDescentForCAP/dev/ci_gaprc /home/gap/.gap/gaprc
-          git clone --depth 1 -vv https://github.com/gap-packages/AutoDoc.git
+          cp ./GradientBasedLearningForCAP/dev/ci_gaprc /home/gap/.gap/gaprc
           git clone --depth 1 -vv https://github.com/homalg-project/homalg_project.git
           git clone --depth 1 -vv https://github.com/homalg-project/CAP_project.git
           git clone --depth 1 -vv https://github.com/homalg-project/CategoricalTowers.git
@@ -59,18 +58,20 @@ jobs:
           if [ -d "CAP_project/CompilerForCAP" ]; then make -C "CAP_project/CompilerForCAP" doc; fi
           if [ -d "CAP_project/MonoidalCategories" ]; then make -C "CAP_project/MonoidalCategories" doc; fi
           if [ -d "CAP_project/CartesianCategories" ]; then make -C "CAP_project/CartesianCategories" doc; fi
+          if [ -d "CAP_project/AdditiveClosuresForCAP" ]; then make -C "CAP_project/AdditiveClosuresForCAP" doc; fi
           if [ -d "CAP_project/FreydCategoriesForCAP" ]; then make -C "CAP_project/FreydCategoriesForCAP" doc; fi
           if [ -d "HigherHomologicalAlgebra/ToolsForHigherHomologicalAlgebra" ]; then make -C "HigherHomologicalAlgebra/ToolsForHigherHomologicalAlgebra" doc; fi
           if [ -d "homalg_project/homalg" ]; then make -C "homalg_project/homalg" doc; fi
           if [ -d "homalg_project/Modules" ]; then make -C "homalg_project/Modules" doc; fi
           if [ -d "CategoricalTowers/ToolsForCategoricalTowers" ]; then make -C "CategoricalTowers/ToolsForCategoricalTowers" doc; fi
           if [ -d "CategoricalTowers/Toposes" ]; then make -C "CategoricalTowers/Toposes" doc; fi
-      - name: Test GradientDescentForCAP
+          if [ -d "GradientBasedLearningForCAP" ]; then make -C "GradientBasedLearningForCAP" doc; fi
+      - name: Test GradientBasedLearningForCAP
         run: |
-          make -C GradientDescentForCAP --trace -j $(nproc) --output-sync ci-test
+          make -C GradientBasedLearningForCAP --trace -j $(nproc) --output-sync ci-test
       - name: Release package or simulate release
         run: |
-          cd GradientDescentForCAP
+          cd GradientBasedLearningForCAP
           python3 dev/process_coverage.py
           git config --global user.name "Bot"
           git config --global user.email "empty"
@@ -88,5 +89,5 @@ jobs:
         env:
           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
         run: |
-          cd GradientDescentForCAP
+          cd GradientBasedLearningForCAP
           ./dev/upload_codecov.sh

.gitignore

@@ -31,4 +31,4 @@
 /doc/*.xml
 /doc_tmp/
 /stats
-/tst/machinelearningforcap*.tst
+/tst/gradientbasedlearningforcap*.tst

PackageInfo.g

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: GPL-2.0-or-later
-# GradientDescentForCAP: Exploring categorical machine learning in CAP
+# GradientBasedLearningForCAP: Gradient Based Learning via Category Theory
 #
 # This file contains package meta data. For additional information on
 # the meaning and correct usage of these fields, please consult the
@@ -8,9 +8,9 @@
 #
 SetPackageInfo( rec(
 
-PackageName := "GradientDescentForCAP",
-Subtitle := "Exploring categorical machine learning in CAP",
-Version := "2025.11-01",
+PackageName := "GradientBasedLearningForCAP",
+Subtitle := "Gradient Based Learning via Category Theory",
+Version := "2026.01-01",
 Date := (function ( ) if IsBound( GAPInfo.SystemEnvironment.GAP_PKG_RELEASE_DATE ) then return GAPInfo.SystemEnvironment.GAP_PKG_RELEASE_DATE; else return Concatenation( ~.Version{[ 1 .. 4 ]}, "-", ~.Version{[ 6, 7 ]}, "-01" ); fi; end)( ),
 License := "GPL-2.0-or-later",
 
@@ -34,13 +34,13 @@ Persons := [
 # BEGIN URLS
 SourceRepository := rec(
     Type := "git",
-    URL := "https://github.com/homalg-project/GradientDescentForCAP",
+    URL := "https://github.com/homalg-project/GradientBasedLearningForCAP",
 ),
 IssueTrackerURL := Concatenation( ~.SourceRepository.URL, "/issues" ),
-PackageWWWHome  := "https://homalg-project.github.io/pkg/GradientDescentForCAP",
-PackageInfoURL  := "https://homalg-project.github.io/GradientDescentForCAP/PackageInfo.g",
-README_URL      := "https://homalg-project.github.io/GradientDescentForCAP/README.md",
-ArchiveURL      := Concatenation( "https://github.com/homalg-project/GradientDescentForCAP/releases/download/v", ~.Version, "/GradientDescentForCAP-", ~.Version ),
+PackageWWWHome  := "https://homalg-project.github.io/pkg/GradientBasedLearningForCAP",
+PackageInfoURL  := "https://homalg-project.github.io/GradientBasedLearningForCAP/PackageInfo.g",
+README_URL      := "https://homalg-project.github.io/GradientBasedLearningForCAP/README.md",
+ArchiveURL      := Concatenation( "https://github.com/homalg-project/GradientBasedLearningForCAP/releases/download/v", ~.Version, "/GradientBasedLearningForCAP-", ~.Version ),
 # END URLS
 
 ArchiveFormats := ".tar.gz .zip",
@@ -58,16 +58,16 @@ Status := "dev",
 AbstractHTML   :=  "",
 
 PackageDoc := rec(
-  BookName  := "GradientDescentForCAP",
+  BookName  := "GradientBasedLearningForCAP",
   ArchiveURLSubset := ["doc"],
   HTMLStart := "doc/chap0.html",
   PDFFile   := "doc/manual.pdf",
   SixFile   := "doc/manual.six",
-  LongTitle := "Exploring categorical machine learning in CAP",
+  LongTitle := "Gradient Based Learning via Category Theory",
 ),
 
 Dependencies := rec(
-  GAP := ">= 4.15.1",
+  GAP := ">= 4.13.0",
   NeededOtherPackages := [
                    [ "GAPDoc", ">= 1.5" ],
                    [ "CAP", ">= 2024.09-12" ],

README.md

@@ -1,7 +1,7 @@
 <!-- BEGIN HEADER -->
-# GradientDescentForCAP&ensp;<sup><sup>[![View code][code-img]][code-url]</sup></sup>
+# GradientBasedLearningForCAP&ensp;<sup><sup>[![View code][code-img]][code-url]</sup></sup>
 
-### Exploring categorical machine learning in CAP
+### Gradient Based Learning via Category Theory
 
 | Documentation | Latest Release | Build Status | Code Coverage |
 | ------------- | -------------- | ------------ | ------------- |
@@ -22,25 +22,25 @@ To obtain current versions of all dependencies, `git clone` (or `git pull` to up
 | 3. | [**CategoricalTowers**](https://github.com/homalg-project/CategoricalTowers#readme) | https://github.com/homalg-project/CategoricalTowers.git |
 
 [html-img]: https://img.shields.io/badge/🔗%20HTML-stable-blue.svg
-[html-url]: https://homalg-project.github.io/GradientDescentForCAP/doc/chap0_mj.html
+[html-url]: https://homalg-project.github.io/GradientBasedLearningForCAP/doc/chap0_mj.html
 
 [pdf-img]: https://img.shields.io/badge/🔗%20PDF-stable-blue.svg
-[pdf-url]: https://homalg-project.github.io/GradientDescentForCAP/download_pdf.html
+[pdf-url]: https://homalg-project.github.io/GradientBasedLearningForCAP/download_pdf.html
 
-[version-img]: https://img.shields.io/endpoint?url=https://homalg-project.github.io/GradientDescentForCAP/badge_version.json&label=🔗%20version&color=yellow
-[version-url]: https://homalg-project.github.io/GradientDescentForCAP/view_release.html
+[version-img]: https://img.shields.io/endpoint?url=https://homalg-project.github.io/GradientBasedLearningForCAP/badge_version.json&label=🔗%20version&color=yellow
+[version-url]: https://homalg-project.github.io/GradientBasedLearningForCAP/view_release.html
 
-[date-img]: https://img.shields.io/endpoint?url=https://homalg-project.github.io/GradientDescentForCAP/badge_date.json&label=🔗%20released%20on&color=yellow
-[date-url]: https://homalg-project.github.io/GradientDescentForCAP/view_release.html
+[date-img]: https://img.shields.io/endpoint?url=https://homalg-project.github.io/GradientBasedLearningForCAP/badge_date.json&label=🔗%20released%20on&color=yellow
+[date-url]: https://homalg-project.github.io/GradientBasedLearningForCAP/view_release.html
 
-[tests-img]: https://github.com/homalg-project/GradientDescentForCAP/actions/workflows/Tests.yml/badge.svg?branch=master
-[tests-url]: https://github.com/homalg-project/GradientDescentForCAP/actions/workflows/Tests.yml?query=branch%3Amaster
+[tests-img]: https://github.com/homalg-project/GradientBasedLearningForCAP/actions/workflows/Tests.yml/badge.svg?branch=master
+[tests-url]: https://github.com/homalg-project/GradientBasedLearningForCAP/actions/workflows/Tests.yml?query=branch%3Amaster
 
-[codecov-img]: https://codecov.io/gh/homalg-project/GradientDescentForCAP/branch/master/graph/badge.svg
-[codecov-url]: https://app.codecov.io/gh/homalg-project/GradientDescentForCAP
+[codecov-img]: https://codecov.io/gh/homalg-project/GradientBasedLearningForCAP/branch/master/graph/badge.svg
+[codecov-url]: https://app.codecov.io/gh/homalg-project/GradientBasedLearningForCAP
 
 [code-img]: https://img.shields.io/badge/-View%20code-blue?logo=github
-[code-url]: https://github.com/homalg-project/GradientDescentForCAP#top
+[code-url]: https://github.com/homalg-project/GradientBasedLearningForCAP#top
 <!-- END FOOTER -->
 
 ### Running the Package with Docker
@@ -93,7 +93,7 @@ In this example, we consider a training dataset consisting of the three points
 
 We aim to compute a line that fits $\mathcal{D}$.
 ```julia
-gap> LoadPackage( "GradientDescentForCAP" );
+gap> LoadPackage( "GradientBasedLearningForCAP" );
 true
 
 gap> Para := CategoryOfParametrisedMorphisms( SkeletalSmoothMaps );
@@ -288,7 +288,7 @@ To facilitate this classification, we use a one-hot encoding scheme for the labe
 That is, the training set (the set of labeled exmaples) is a finite subset of $\mathbb{R}^2 \times \mathbb{R}^3 \simeq \mathbb{R}^5$.
 
 ```julia
-gap> LoadPackage( "GradientDescentForCAP" );
+gap> LoadPackage( "GradientBasedLearningForCAP" );
 true
 
 gap> Para := CategoryOfParametrisedMorphisms( SkeletalSmoothMaps );

dev/.release

@@ -14,7 +14,7 @@ sed "s;Date := .*;Date := \"$(date -I)\",;" PackageInfo.g > PackageInfo.g.bak
 mv PackageInfo.g.bak PackageInfo.g
 
 # replace links to packages which are possibly referenced in the documentation, keep this in sync with `Tests.yml.j2`
-for package in CAP_project/CAP CAP_project/CompilerForCAP CAP_project/MonoidalCategories CAP_project/CartesianCategories CAP_project/FreydCategoriesForCAP HigherHomologicalAlgebra/ToolsForHigherHomologicalAlgebra homalg_project/homalg homalg_project/Modules CategoricalTowers/ToolsForCategoricalTowers CategoricalTowers/Toposes; do
+for package in CAP_project/CAP CAP_project/CompilerForCAP CAP_project/MonoidalCategories CAP_project/CartesianCategories CAP_project/AdditiveClosuresForCAP CAP_project/FreydCategoriesForCAP HigherHomologicalAlgebra/ToolsForHigherHomologicalAlgebra homalg_project/homalg homalg_project/Modules CategoricalTowers/ToolsForCategoricalTowers CategoricalTowers/Toposes GradientBasedLearningForCAP; do
 
     # adjust links to other manuals
     # Note that we cannot use sed's `-i` option for in-place editing, as

dev/upload_codecov.sh

@@ -21,9 +21,16 @@ curl -O https://uploader.codecov.io/latest/linux/codecov.SHA256SUM.sig
 gpgv codecov.SHA256SUM.sig codecov.SHA256SUM
 shasum -a 256 -c codecov.SHA256SUM
 
+# read the token
+if [ -z "$CODECOV_TOKEN" ]; then
+  echo -e "\033[0;33mCODECOV_TOKEN is not set. Proceeding without token.\033[0m"
+else
+  echo -e "\033[0;32mUsing CODECOV_TOKEN from environment variable.\033[0m"
+fi
+
 # execute
 chmod +x codecov
 while ! ./codecov -Z -v -s ../ -t $CODECOV_TOKEN; do
-    echo "Codecov upload failed, retrying in 20s"
-    sleep 20
+    echo "Codecov upload failed, retrying in 60s"
+    sleep 60
 done

doc/Doc.autodoc

@@ -1 +1,101 @@
-@Chapter Examples and Tests
+@Chapter Introduction
+
+This package provides tools for exploring categorical machine learning using the CAP (Categories, Algorithms, Programming) system.
+It implements automatic differentiation using the lens pattern and provides constructs for building and training neural networks.
+
+@Section Overview
+
+The package implements the following main concepts:
+
+* **Examples**: Examples for creating and training neural networks.
+
+* **Expressions**: A symbolic expression system for representing mathematical formulas.
+
+* **Skeletal Category of Smooth Maps**: A category where objects are Euclidean spaces $\mathbb{R}^n$ and morphisms are smooth maps with their Jacobian matrices.
+
+* **Category of Parametrised Morphisms**: A category that represents morphisms with learnable parameters, used to model neural network layers.
+
+* **Neural Networks**: High-level operations for constructing and training neural networks.
+
+* **Category of Lenses**: A category that models bidirectional data flow, essential for backpropagation in neural networks.
+
+* **Fitting Parameters**: Explain how to learn the parameters in order to minimize a parametrised morphism.
+
+* **CAP Operation**: The new categorical operations needed in this package.
+
+* **Tools**: Few GAP operations and helper functions.
+
+
+@Chapter Examples for neural networks
+
+@Section Binary-class neural network with binary cross-entropy loss function
+@Section Multi-class neural network with cross-entropy loss function
+@Section Neural network with quadratic loss function
+
+@Chapter Expressions
+
+@Section Constructors
+@Section Attributes
+@Section Operations
+@Section Global Functions
+@Section Examples
+@Section GAP Categories
+
+@Chapter Skeletal Category of Smooth Maps
+
+@Section Constructors
+@Section Attributes
+@Section Operations
+@Section Available Smooth Maps
+@Section Supported CAP Operations
+@Section Examples
+@Section GAP Categories
+
+@Chapter Category of Parametrised Morphisms
+
+@Section Definition
+@Section Constructors
+@Section Attributes
+@Section Operations
+@Section Available Parametrised Morphisms
+@Section Supported CAP Operations
+@Section Examples
+@Section GAP Categories
+
+@Chapter Neural Networks
+
+@Section Definition
+@Section Operations
+@Section Examples
+
+@Chapter Category of Lenses
+
+@Section Definition
+@Section Constructors
+@Section Attributes
+@Section Operations
+@Section Available Lenses
+@Section Optimizers
+@Section Supported CAP Operations
+@Section Examples
+@Section GAP Categories
+
+
+@Chapter Fitting Parameters
+
+@Section Introduction
+@Section Notes on Batching
+@Section Operations
+@Section Examples
+
+@Chapter CAP Operations for GradientBasedLearningForCAP
+
+@Section Basic Operations
+@Section Add-Methods
+
+@Chapter Tools
+
+@Section Mathematical Operations
+@Section List Operations
+@Section Helper Functions
+@Section Python Integration

doc/clean

@@ -1,2 +1,2 @@
-#!/bin/bash
+#!/bin/sh
 rm -f *.{aux,bbl,blg,brf,css,html,idx,ilg,ind,js,lab,log,out,pdf,pnr,six,tex,toc,txt,xml,xml.bib}