From d37e1de21b7fc85734ec757627319fa3c09a01ac Mon Sep 17 00:00:00 2001 From: jsboige Date: Wed, 28 Oct 2026 14:14:48 +0100 Subject: [PATCH 1/6] rename(#16231): git mv purs (1 notebooks) Table : C:\Users\Jesse\AppData\Local\Temp\t2_galois.tsv, pilotee par rename_notebooks.py. --- .../GALOIS-01-Probleme-Inverse-M23-Python.ipynb} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{Lean-22-Galois-Probleme-Inverse-M23.ipynb => GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb} (100%) diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb From 62664dda1e22e6fe3d548f30bb5999e55d4d6a04 Mon Sep 17 00:00:00 2001 From: jsboige Date: Wed, 28 Oct 2026 14:15:27 +0100 Subject: [PATCH 2/6] rename(#16231): referents reecrits par surface (4 fichiers) Cellules de code citees : jamais reecrites (re-execution C.2 due). Sorties commitees : jamais touchees. --- .../Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb | 2 +- _quarto.yml | 2 +- docs/curriculum/ia-symbolique.md | 2 +- docs/reference/rename-ledger.tsv | 1 + scripts/notebook_tools/pedagogy_density_baseline.json | 2 +- 5 files changed, 5 insertions(+), 4 deletions(-) diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb index 8fdd4c49df7..2a16e5cda3a 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb @@ -1610,7 +1610,7 @@ "end_time": "2026-09-20T02:00:08.303200+00:00", "environment_variables": {}, "exception": null, - "input_path": "Lean-22-Galois-Probleme-Inverse-M23.ipynb", + "input_path": "GALOIS-01-Probleme-Inverse-M23-Python.ipynb", "output_path": "16581_pm_output2.ipynb", "parameters": {}, "start_time": "2026-09-20T01:53:28.665774+00:00", diff --git a/_quarto.yml b/_quarto.yml index 84c5eb3e63e..529e1c459e9 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -2238,7 +2238,7 @@ project: - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-19c-Borne-Bernstein-Python.ipynb" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-19d-Inference-Bayesian-Beta-Binomial-Python.ipynb" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-20-Capstone-Digestions-Tao-Python.ipynb" - - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb" + - "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb" diff --git a/docs/curriculum/ia-symbolique.md b/docs/curriculum/ia-symbolique.md index 5987b5980ae..87b20d04784 100644 --- a/docs/curriculum/ia-symbolique.md +++ b/docs/curriculum/ia-symbolique.md @@ -154,7 +154,7 @@ Preuves formelles en Lean 4, logique probabiliste avec Tweety, web sémantique, | 80 | [Lean-19c : borne de Bernstein pour le BSC (Python)](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-19c-Borne-Bernstein-Python.ipynb) | BETA | Non | | 81 | [Lean-19d — Inférence bayésienne Beta-Binomial (Python)](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-19d-Inference-Bayesian-Beta-Binomial-Python.ipynb) | ALPHA | Non | | 82 | [Lean-20 : Capstone — digérer le travail formel de Tao…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-20-Capstone-Digestions-Tao-Python.ipynb) | BETA | Non | -| 83 | [Lean-22 : Le problème inverse de Galois — M₂₃ refermé…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb) | BETA | Non | +| 83 | [Lean-22 : Le problème inverse de Galois — M₂₃ refermé…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) | BETA | Non | | 84 | [Lean-23 : ERC-20 sous Lean 4 — l'invariant de…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb) | BETA | Non | | 85 | [Lean-23b — ERC-20 natif : l'invariant de conservation…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb) | BETA | Non | | 86 | [Lean-24 : le lake calibration_lean par ses énoncés —…](../../MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb) | BETA | Non | diff --git a/docs/reference/rename-ledger.tsv b/docs/reference/rename-ledger.tsv index 6b1d7af3ede..bd0ad1e54e9 100644 --- a/docs/reference/rename-ledger.tsv +++ b/docs/reference/rename-ledger.tsv @@ -403,3 +403,4 @@ MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12/ICT-12b-LearnedValence-Python.ipynb MyIA MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12/ICT-12c-PregnanceAnimat-Python.ipynb MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12-ValenceFieldsAndAnimats/ICT-12c-PregnanceAnimat-Python.ipynb 2026-10-28 myia-po-2027:CoursIA-2 MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12/ICT-12d-InhibitedActionAnimat-Python.ipynb MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12-ValenceFieldsAndAnimats/ICT-12d-InhibitedActionAnimat-Python.ipynb 2026-10-28 myia-po-2027:CoursIA-2 MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12/ICT-12e-Value-of-Information-Animat-Python.ipynb MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12-ValenceFieldsAndAnimats/ICT-12e-Value-of-Information-Animat-Python.ipynb 2026-10-28 myia-po-2027:CoursIA-2 +MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb 2026-10-28 myia-po-2027:CoursIA diff --git a/scripts/notebook_tools/pedagogy_density_baseline.json b/scripts/notebook_tools/pedagogy_density_baseline.json index 43cf3e3b1ac..6d07f04b8f2 100644 --- a/scripts/notebook_tools/pedagogy_density_baseline.json +++ b/scripts/notebook_tools/pedagogy_density_baseline.json @@ -1203,7 +1203,7 @@ "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-19c-Borne-Bernstein-Python.ipynb": 992.833, "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-19d-Inference-Bayesian-Beta-Binomial-Python.ipynb": 959.5, "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-20-Capstone-Digestions-Tao-Python.ipynb": 2598.4, - "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb": 1116.625, + "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb": 1116.625, "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb": 2289.429, "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb": 1212.111, "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb": 2842.071, From 605016fd33b31b4c47cd704463373502b9a72629 Mon Sep 17 00:00:00 2001 From: jsboige Date: Wed, 28 Oct 2026 14:49:07 +0100 Subject: [PATCH 3/6] Rename(#17545): T2 GALOIS - lake sous GALOIS/, wiring CI, referents manuels, README sous-serie - lake galois_lean/ -> GALOIS/galois_lean/ (git mv purs, frere du carnet descendu) - lean-galois.yml : 11 chemins rewires ; _quarto.yml : chemin du README du lac - 13+1 liens relatifs refuses par l'organe (#19154) recalcules a la main (8 carnets + LEAN_INVENTORY + 2 README + galois_lean/README + CSV SC) - carnet descendu : titre GALOIS-01, nav ../MIMO + >> Lean-23, lien grothendieck_lean prefixe (garde navlinks : 0 lien casse) - GALOIS/README.md cree (ilot a une entree, pattern MIMO, pas de capstone) - translations : rekey 33 lignes + golden_set (4 chemins .lean du lac) - Lean-41 cell_08 : encoding=utf-8 ajoute au subprocess call (ratchet #13140, defect preexpose par le touch) + re-execution complete (SUCCESS 13s, 0 erreur) Co-Authored-By: Claude Sonnet 5.5 --- .github/workflows/lean-galois.yml | 22 +- ...ALOIS-01-Probleme-Inverse-M23-Python.ipynb | 6 +- .../SymbolicAI/Lean/GALOIS/README.md | 15 + .../Lean/{ => GALOIS}/galois_lean/Galois.lean | 0 .../Galois/AdicCompletionLocalRing.lean | 0 .../Galois/AdicCompletionLocalRing_en.lean | 0 .../Galois/LowerRamificationGroup.lean | 0 .../Galois/LowerRamificationGroup_en.lean | 0 .../galois_lean/Galois/M23Lean4Web.lean | 0 .../Lean/{ => GALOIS}/galois_lean/README.md | 2 +- .../galois_lean/lake-manifest.json | 0 .../{ => GALOIS}/galois_lean/lakefile.lean | 0 .../{ => GALOIS}/galois_lean/lean-toolchain | 0 .../SymbolicAI/Lean/LEAN_INVENTORY.md | 4 +- .../Lean/Lean-15-Grothendieck-Tribute.ipynb | 2 +- .../Lean-23-ERC20-Invariant-Companion.ipynb | 4 +- .../Lean/Lean-41-Capstone-MIMO-Python.ipynb | 196 ++++-- .../MIMO-01-Descente-Budget-Lean-Python.ipynb | 2 +- .../MIMO-04-Capstone-Digestion-Python.ipynb | 6 +- .../SymbolicAI/Lean/MIMO/README.md | 2 +- MyIA.AI.Notebooks/SymbolicAI/Lean/README.md | 7 +- .../Serre100/25-groupe-galois-calcule.ipynb | 10 +- ...7b-ERC20-Lean-Verification-Companion.ipynb | 2 +- _quarto.yml | 2 +- .../smartcontracts/smartcontracts.csv | 2 +- .../symbolicai-lean/symbolicai-lean.csv | 80 +-- translations/translation_golden_set.json | 584 +++++++++--------- 27 files changed, 544 insertions(+), 404 deletions(-) create mode 100644 MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/README.md rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/Galois.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/Galois/AdicCompletionLocalRing.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/Galois/AdicCompletionLocalRing_en.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/Galois/LowerRamificationGroup.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/Galois/LowerRamificationGroup_en.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/Galois/M23Lean4Web.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/README.md (96%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/lake-manifest.json (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/lakefile.lean (100%) rename MyIA.AI.Notebooks/SymbolicAI/Lean/{ => GALOIS}/galois_lean/lean-toolchain (100%) diff --git a/.github/workflows/lean-galois.yml b/.github/workflows/lean-galois.yml index e22595981d4..874ef742e7a 100644 --- a/.github/workflows/lean-galois.yml +++ b/.github/workflows/lean-galois.yml @@ -1,6 +1,6 @@ name: Lean Galois CI -# CI for galois_lean (SymbolicAI/Lean/galois_lean). +# CI for galois_lean (SymbolicAI/Lean/GALOIS/galois_lean). # Inverse Galois problem / M23 — proof layer for the Lean-19 notebook. # Rule lean-merge-discipline: lake build SUCCESS local OBLIGATOIRE avant merge. # @@ -19,10 +19,10 @@ on: push: branches: [main] paths: - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/**.lean' - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lakefile.lean' - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lakefile.toml' - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lean-toolchain' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/**.lean' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lakefile.lean' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lakefile.toml' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lean-toolchain' - '.github/workflows/lean-galois.yml' - '.github/workflows/lean-axiom.yml' - '.github/workflows/lean-build.yml' @@ -33,10 +33,10 @@ on: pull_request: types: [opened, synchronize, edited, reopened] paths: - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/**.lean' - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lakefile.lean' - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lakefile.toml' - - 'MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lean-toolchain' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/**.lean' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lakefile.lean' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lakefile.toml' + - 'MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lean-toolchain' # The gate must run on the PR that introduces or changes it (cf #8712). - '.github/workflows/lean-galois.yml' - '.github/workflows/lean-axiom.yml' @@ -56,7 +56,7 @@ jobs: ci: uses: jsboige/CoursIA/.github/workflows/lean-build.yml@main with: - project-path: MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean + project-path: MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean display-name: galois_lean sorry-baseline: "0" sorry-filter-mode: real @@ -87,7 +87,7 @@ jobs: needs: ci # c.306 parity with knot_lean (#14922) -- serialize the two compiles; ordering edge only, no measured clone-halving (c.329, ai-01 re-review #15150) uses: ./.github/workflows/lean-axiom.yml with: - project-path: MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean + project-path: MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean display-name: galois_lean target-modules: "*" allow-axioms: "" diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb index 2a16e5cda3a..86638d07423 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb @@ -14,11 +14,11 @@ "tags": [] }, "source": [ - "# Lean-22 : Le problème inverse de Galois — M₂₃ refermé le 9 août 2026\n", + "# GALOIS-01 : Le problème inverse de Galois — M₂₃ refermé le 9 août 2026\n", "\n", "**Série** : SymbolicAI / Lean — Théorie des groupes sporadiques et problème inverse de Galois\n", "\n", - "**Navigation** : [<< MIMO-04 Capstone](MIMO/MIMO-04-Capstone-Digestion-Python.ipynb) | [Index](README.md)\n", + "**Navigation** : [<< MIMO-04 Capstone](../MIMO/MIMO-04-Capstone-Digestion-Python.ipynb) | [Index](README.md) | [Lean-23 (ERC-20) >>](../Lean-23-ERC20-Invariant-Companion.ipynb)\n", "\n", "**Kernel** : Python 3 (vérifications exécutables sympy + extraction Steiner) + Lean 4 via WSL\n", "(`galois_lean`, source de vérité formelle — sections 3 et 4)\n", @@ -552,7 +552,7 @@ "4. **Spécialisation** (irréductibilité de Hilbert) : de ℚ(t) vers le ℚ du polynôme f₁.\n", "\n", "Le dépôt porte déjà le socle géométrique de cette chaîne dans\n", - "[`grothendieck_lean`](grothendieck_lean/README.md) : sites, faisceaux,\n", + "[`grothendieck_lean`](../grothendieck_lean/README.md) : sites, faisceaux,\n", "cohomologie — mais **pas** le π₁ étale ni l'existence de Riemann. La phrase qui doit rester\n", "noir sur blanc : **« M₂₃ est un groupe de Galois sur ℚ » n'est PAS formalisé dans ce dépôt** —\n", "il est prouvé dans le préprint, et vérifié computationnellement ci-dessous au niveau du\n", diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/README.md new file mode 100644 index 00000000000..7f454209a46 --- /dev/null +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/README.md @@ -0,0 +1,15 @@ +# GALOIS — le problème inverse de Galois, M₂₃ refermé + +Sous-série de la série [Lean](../README.md) — **îlot à une entrée** de la gradation #17545 (décision du 24/09, note de conception c.6408913452) : le problème inverse de Galois descend ici avec son lac [`galois_lean`](galois_lean/README.md). Pas de capstone dédié (décision du 11/10 : le capstone reste MIMO-only) — l'îlot se réduit à son carnet et à son lac, le lac y est exposé en entier (sections 3-4 et annexes A-B du carnet). + +L'ancien identifiant de série (`Lean-22` → `GALOIS-01`) est consigné dans `docs/reference/rename-ledger.tsv`. + +**Escalier depuis la série principale** : l'entrée se fait depuis [MIMO-04 — Capstone](../MIMO/MIMO-04-Capstone-Digestion-Python.ipynb), sortie de la sous-série MIMO ; l'îlot se referme sur GALOIS-01 et le parcours principal reprend à [Lean-23 (ERC-20)](../Lean-23-ERC20-Invariant-Companion.ipynb). + +| Carnet | Contenu | Durée | +|---|---|---| +| [GALOIS-01-Probleme-Inverse-M23-Python](GALOIS-01-Probleme-Inverse-M23-Python.ipynb) | Le problème inverse de Galois refermé (arXiv:2608.08538, 9 août 2026) : M₂₃ prouvé simple d'ordre 10 200 960 **à l'écran** (`card_M23`/`simple_M23` exécutés, `#print axioms` = liste blanche), design de Witt S(4,7,23) vérifié des deux côtés (253 heptades), polynôme f₁ de degré 23 manipulé pour de vrai (empreinte, irréductibilité, discriminant 383 chiffres, Frobenius mod p) — les deux énoncés distingués : prouvé vs cité | 45 min | + +**Prérequis d'entrée** : Python 3 (NumPy, sympy). Le lac `galois_lean` est résolu par `find_lean_project` — le lac est frère du carnet dans l'îlot, aucune configuration manuelle. + +**Statut du lac** : `galois_lean` est **sorry-free** (mode `real` : 0 `sorry` tactique, 0 `native_decide`, 0 `axiom` déclaré) — preuve M₂₃ *vendored* Apache-2.0 depuis [`KitaKen1/finite-simple-groups-lean`](https://github.com/KitaKen1/finite-simple-groups-lean), axiomes `{propext, Classical.choice, Quot.sound}` (whitelist §B), gardé par la CI `lean-galois.yml`. Détail : [`galois_lean/README.md`](galois_lean/README.md). diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/AdicCompletionLocalRing.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/AdicCompletionLocalRing.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/AdicCompletionLocalRing_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing_en.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/AdicCompletionLocalRing_en.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing_en.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/LowerRamificationGroup.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/LowerRamificationGroup.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/LowerRamificationGroup_en.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup_en.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/LowerRamificationGroup_en.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup_en.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/M23Lean4Web.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/M23Lean4Web.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/M23Lean4Web.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/M23Lean4Web.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md similarity index 96% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/README.md rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md index b0152554223..335218b2206 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/README.md +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md @@ -1,6 +1,6 @@ # Problème inverse de Galois — M₂₃ groupe de Galois sur ℚ -Lake `galois_lean` : couche de preuve pour le notebook **[Lean-22](../Lean-22-Galois-Probleme-Inverse-M23.ipynb)** (problème inverse de Galois), autour du groupe sporadique de Mathieu M₂₃. +Lake `galois_lean` : couche de preuve pour le notebook **[Lean-22](../GALOIS-01-Probleme-Inverse-M23-Python.ipynb)** (problème inverse de Galois), autour du groupe sporadique de Mathieu M₂₃. ## Contexte diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lake-manifest.json b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lake-manifest.json similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lake-manifest.json rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lake-manifest.json diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lakefile.lean b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lakefile.lean similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lakefile.lean rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lakefile.lean diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lean-toolchain b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lean-toolchain similarity index 100% rename from MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/lean-toolchain rename to MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/lean-toolchain diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md index 39ba1f21e96..e447c8f7317 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md @@ -217,8 +217,8 @@ pour l'identification `23T5 = M23` que la couche proof-assistant ne reproduit pa tactique, 0 `native_decide`, 0 `axiom` déclaré. `Sporadic.card_M23` et `Sporadic.simple_M23` prouvés par chaîne de stabilisateurs à certificats (Schreier–Sims matérialisé). -- **Notebook câblé** : 1 notebook (Lean-22-Galois-Probleme-Inverse-M23). -- **Suivi** : préprint #2608.08538 (cf. `galois_lean/README.md`). +- **Notebook câblé** : 1 notebook (GALOIS/GALOIS-01-Probleme-Inverse-M23-Python). +- **Suivi** : préprint #2608.08538 (cf. `GALOIS/galois_lean/README.md`). - **i18n** : EN-sibling à venir (EPIC #4980). - **Caveat build** : `lakefile.toml` utilise la forme **bare** + agrégateur racine (la forme `globs = #[Galois.*]` déclenche un quirk trace *job-computation* sous lake diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb index 30ddc75460c..20027c33be7 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15-Grothendieck-Tribute.ipynb @@ -1650,7 +1650,7 @@ "\n", "Le théorème structurel est de Grothendieck (SGA 7) : la monodromie d'une famille lisse au-dessus d'un disque épointé est **quasi-unipotente** — un certain pouvoir de la transformation est unipotent, autrement dit toutes ses valeurs propres sont des racines de l'unité. C'est un résultat de finitude remarquable : quelle que soit la famille, la boucle ne peut engendrer qu'une torsion d'ordre fini ou quasi fini. Deligne en a fait l'arme centrale de *Weil II* (1980) : c'est en contrôlant la monodromie ℓ-adique qu'il porte l'analogie de Riemann sur les corps finis. Le polynôme d'Alexander du trèfle est précisément le polynôme caractéristique de cette action.\n", "\n", - "**État mesuré (18/10/2026)** : la série Lean manipule la monodromie sans jamais l'introduire — 4 occurrences d'usage ([Lean-22](Lean-22-Galois-Probleme-Inverse-M23.ipynb) ×1, [Lean-28](Lean-28-Complex-Structure-S6.ipynb) ×2 — le triangle (3,4,∞) « fournit les monodromies » qui donnent la présentation de π₁ — et [Riemann200/README](Riemann200/README.md) ×1), zéro définition. Mathlib 4 ne formalise pas davantage la quasi-unipotence : du hors-scope cohérent avec la table ci-dessus. La première marche — groupe fondamental d'un cercle épointé, action de monodromie d'un exemple explicite, quasi-unipotence calculée — est ouverte en grain dédié : [#21308](https://github.com/jsboige/CoursIA/issues/21308).\n" + "**État mesuré (18/10/2026)** : la série Lean manipule la monodromie sans jamais l'introduire — 4 occurrences d'usage ([Lean-22](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) ×1, [Lean-28](Lean-28-Complex-Structure-S6.ipynb) ×2 — le triangle (3,4,∞) « fournit les monodromies » qui donnent la présentation de π₁ — et [Riemann200/README](Riemann200/README.md) ×1), zéro définition. Mathlib 4 ne formalise pas davantage la quasi-unipotence : du hors-scope cohérent avec la table ci-dessus. La première marche — groupe fondamental d'un cercle épointé, action de monodromie d'un exemple explicite, quasi-unipotence calculée — est ouverte en grain dédié : [#21308](https://github.com/jsboige/CoursIA/issues/21308).\n" ] }, { diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb index f9291cf19f9..8237b50ac47 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb @@ -16,7 +16,7 @@ "source": [ "# Lean-23 : ERC-20 sous Lean 4 — l'invariant de conservation prouvé\n", "\n", - "**Navigation** : [Index](README.md) | [Lean-22 (Galois) <<](Lean-22-Galois-Probleme-Inverse-M23.ipynb) | [SC-7 SmartContract (Solidity ERC-20) >>](../SmartContracts/02-Solidity-Advanced/SC-07-Token-Standards-Python.ipynb)\n", + "**Navigation** : [Index](README.md) | [Lean-22 (Galois) <<](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) | [SC-7 SmartContract (Solidity ERC-20) >>](../SmartContracts/02-Solidity-Advanced/SC-07-Token-Standards-Python.ipynb)\n", "\n", "Ce notebook presente le lake **`erc20_lean`** de ce dépot (issue #4047, livrable phase 1) : le port formel de l'**invariant de conservation d'un jeton ERC-20** en Lean 4 avec Mathlib. La propriété foncière d'un jeton fongible — aucun token ne peut être créé ou détruit par un `transfer` (seuls `mint`/`burn` le font, symétriquement sur l'offre) — est ici prouvée **mécaniquement** sur une machine à états finie de **trois transitions gardées**.\n", "\n", @@ -948,7 +948,7 @@ "- **Lake `erc20_lean`** (`MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/`) — la version Lean 4 + Mathlib : `ERC20/State.lean` (modèle), `ERC20/Ops.lean` (transitions), `ERC20/Invariant.lean` (préservation).\n", "- **EPIC #4980** — convention i18n Lean (aggrégateur bilingue inline FR-EN dans `ERC20.lean`, sibling pair dans `ERC20_en.lean`).\n", "- **Rule C.6** (mandat user 2026-08-19) — préférence pour un compagnon kernel `lean4-wsl` à côté du notebook Python ; le pattern actuel (subprocess `lake env lean`) reste mergeable, le kernel Lean natif est une suite à explorer.\n", - "- Notebooks associés : `[Lean-22 (Galois)](Lean-22-Galois-Probleme-Inverse-M23.ipynb)`, `[Lean-21 (MIMO)](MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`, `[Lean-13 (Kochen-Specker)](Lean-13-Kochen-Specker.ipynb)`, `[A* Optimalite (Search-03e)](../../Search/Part1-Foundations/Search-03e-AStar-Optimality.ipynb)`." + "- Notebooks associés : `[Lean-22 (Galois)](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)`, `[Lean-21 (MIMO)](MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`, `[Lean-13 (Kochen-Specker)](Lean-13-Kochen-Specker.ipynb)`, `[A* Optimalite (Search-03e)](../../Search/Part1-Foundations/Search-03e-AStar-Optimality.ipynb)`." ] } ], diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb index 10df2e60077..2bb515510d3 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb @@ -3,7 +3,16 @@ { "cell_type": "markdown", "id": "49bb51d9", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001617, + "end_time": "2026-10-28T13:43:15.756608+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.754991+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "# Lean-41 : Capstone — escalier vers la sous-série MIMO (détection par flips, budget de descente et digestion)\n", "\n", @@ -19,7 +28,16 @@ { "cell_type": "markdown", "id": "53eefd26", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.00111, + "end_time": "2026-10-28T13:43:15.759176+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.758066+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 1. Le geste : la détection MIMO par flips de coordonnées\n", "\n", @@ -37,7 +55,16 @@ { "cell_type": "markdown", "id": "bcbca5ba", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001075, + "end_time": "2026-10-28T13:43:15.761376+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.760301+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 2. Table de routage : quatre carnets, un lac\n", "\n", @@ -54,7 +81,16 @@ { "cell_type": "markdown", "id": "30be0c78", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001022, + "end_time": "2026-10-28T13:43:15.763497+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.762475+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 3. Le versant formel : `mimo_lean`, un lac sorry-free\n", "\n", @@ -74,23 +110,31 @@ "id": "efcbd03d", "metadata": { "execution": { - "iopub.execute_input": "2026-10-14T19:41:21.307872Z", - "iopub.status.busy": "2026-10-14T19:41:21.307590Z", - "iopub.status.idle": "2026-10-14T19:41:21.423877Z", - "shell.execute_reply": "2026-10-14T19:41:21.422814Z" - } + "iopub.execute_input": "2026-10-28T13:43:15.766702Z", + "iopub.status.busy": "2026-10-28T13:43:15.766434Z", + "iopub.status.idle": "2026-10-28T13:43:15.897233Z", + "shell.execute_reply": "2026-10-28T13:43:15.896464Z" + }, + "papermill": { + "duration": 0.133723, + "end_time": "2026-10-28T13:43:15.898285+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.764562+00:00", + "status": "completed" + }, + "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Python: 3.13.7 (CPython)\n", - "NumPy: 2.4.2\n", - " MIMO-01-Descente-Budget-Lean-Python.ipynb: 114782 octets\n", - " MIMO-02-Detection-Flips-Lean-Python.ipynb: 164437 octets\n", - " MIMO-03-Converse-Native-Lean.ipynb: 351555 octets\n", - " MIMO-04-Capstone-Digestion-Python.ipynb: 48689 octets\n", + "Python: 3.13.15 (CPython)\n", + "NumPy: 2.5.2\n", + " MIMO-01-Descente-Budget-Lean-Python.ipynb: 114817 octets\n", + " MIMO-02-Detection-Flips-Lean-Python.ipynb: 164359 octets\n", + " MIMO-03-Converse-Native-Lean.ipynb: 351827 octets\n", + " MIMO-04-Capstone-Digestion-Python.ipynb: 48942 octets\n", "\n", "Lac mimo_lean: 13 fichiers .lean\n" ] @@ -124,7 +168,16 @@ { "cell_type": "markdown", "id": "b5ce039a", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001373, + "end_time": "2026-10-28T13:43:15.901511+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.900138+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "### Lecture du résultat\n", "\n", @@ -134,7 +187,16 @@ { "cell_type": "markdown", "id": "f4f13a5d", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001401, + "end_time": "2026-10-28T13:43:15.904663+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.903262+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 4. Surface de la sous-série : on mesure, on ne suppose rien\n", "\n", @@ -151,11 +213,19 @@ "id": "48ab04ec", "metadata": { "execution": { - "iopub.execute_input": "2026-10-14T19:41:21.426469Z", - "iopub.status.busy": "2026-10-14T19:41:21.426011Z", - "iopub.status.idle": "2026-10-14T19:41:21.441345Z", - "shell.execute_reply": "2026-10-14T19:41:21.440287Z" - } + "iopub.execute_input": "2026-10-28T13:43:15.908823Z", + "iopub.status.busy": "2026-10-28T13:43:15.908405Z", + "iopub.status.idle": "2026-10-28T13:43:15.920774Z", + "shell.execute_reply": "2026-10-28T13:43:15.920079Z" + }, + "papermill": { + "duration": 0.015594, + "end_time": "2026-10-28T13:43:15.921638+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.906044+00:00", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -207,11 +277,19 @@ "id": "38459cf4", "metadata": { "execution": { - "iopub.execute_input": "2026-10-14T19:41:21.443599Z", - "iopub.status.busy": "2026-10-14T19:41:21.443267Z", - "iopub.status.idle": "2026-10-14T19:41:21.673225Z", - "shell.execute_reply": "2026-10-14T19:41:21.672033Z" - } + "iopub.execute_input": "2026-10-28T13:43:15.925598Z", + "iopub.status.busy": "2026-10-28T13:43:15.925374Z", + "iopub.status.idle": "2026-10-28T13:43:16.150961Z", + "shell.execute_reply": "2026-10-28T13:43:16.149641Z" + }, + "papermill": { + "duration": 0.229435, + "end_time": "2026-10-28T13:43:16.152613+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:15.923178+00:00", + "status": "completed" + }, + "tags": [] }, "outputs": [ { @@ -269,7 +347,7 @@ "# Invocation unique de l'organe canonique sur le lac entier\n", "proc = subprocess.run(\n", " ['python', str(script), '--lake', str(lac_abs), '--json', '--no-vacuous'],\n", - " capture_output=True, text=True\n", + " capture_output=True, text=True, encoding=\"utf-8\", errors=\"replace\"\n", ")\n", "data = _json.loads(proc.stdout)\n", "lake_summary = data['lakes'][0]\n", @@ -297,7 +375,16 @@ { "cell_type": "markdown", "id": "1008adf1", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.00315, + "end_time": "2026-10-28T13:43:16.160544+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:16.157394+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "### Lecture du résultat\n", "\n", @@ -307,7 +394,16 @@ { "cell_type": "markdown", "id": "1372b9e1", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001758, + "end_time": "2026-10-28T13:43:16.164134+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:16.162376+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 5. L'écart promesse / organe : `56` vs `57`\n", "\n", @@ -323,7 +419,16 @@ { "cell_type": "markdown", "id": "0e81eb67", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001964, + "end_time": "2026-10-28T13:43:16.169466+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:16.167502+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 6. Itinéraire de lecture : trois parcours au choix\n", "\n", @@ -333,13 +438,22 @@ "2. **Curieux (2h30)** : `MIMO-01` → `MIMO-02` (le seuil et la simulation). On quitte en ayant vu le seuil `2·log N` confrontée au Monte-Carlo.\n", "3. **Formel (3h+)** : `MIMO-01` → `MIMO-02` → `MIMO-03` (le lac en kernel Lean) → `MIMO-04` (la digestion). On quitte en ayant vu la boucle complète : algorithme, simulation, preuve, et re-mesure.\n", "\n", - "Le parcours principal reprend ensuite à [Lean-22 — Galois](Lean-22-Galois-Probleme-Inverse-M23.ipynb) (sous-série Galois) ou à [Lean-29 — Hecke Operators](Lean-29-Hecke-Operators-Native.ipynb) (sous-série Hecke).\n" + "Le parcours principal reprend ensuite à [Lean-22 — Galois](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) (sous-série Galois) ou à [Lean-29 — Hecke Operators](Lean-29-Hecke-Operators-Native.ipynb) (sous-série Hecke).\n" ] }, { "cell_type": "markdown", "id": "64eca5b6", - "metadata": {}, + "metadata": { + "papermill": { + "duration": 0.001727, + "end_time": "2026-10-28T13:43:16.172972+00:00", + "exception": false, + "start_time": "2026-10-28T13:43:16.171245+00:00", + "status": "completed" + }, + "tags": [] + }, "source": [ "## 7. Trois exercices\n", "\n", @@ -367,9 +481,21 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.13.7" + "version": "3.13.15" + }, + "papermill": { + "default_parameters": {}, + "duration": 3.504646, + "end_time": "2026-10-28T13:43:16.409316+00:00", + "environment_variables": {}, + "exception": null, + "input_path": "Lean-41-Capstone-MIMO-Python.ipynb", + "output_path": "Lean-41-Capstone-MIMO-Python.ipynb", + "parameters": {}, + "start_time": "2026-10-28T13:43:12.904670+00:00", + "version": "2.7.0" } }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-01-Descente-Budget-Lean-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-01-Descente-Budget-Lean-Python.ipynb index dbbc41baf20..a6e9aa5b8e2 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-01-Descente-Budget-Lean-Python.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-01-Descente-Budget-Lean-Python.ipynb @@ -2120,7 +2120,7 @@ "- **D. Papailiopoulos** (2026) - *Detection MIMO by coordinate flips : Proposition 9.1* (papier source des theoremes `Descent.lean`).\n", "- **`Descent.lean`** (`MyIA.AI.Notebooks/SymbolicAI/Lean/mimo_lean/`) - la formalisation : `Run` (inductif sur List sigma), `lastState`, lemmes 1-3 (`run_tail_cost_lt`, `run_nodup`, `run_length_le_cost`), `descent_flips_le_barrier`, `descent_target_before_ceiling`. Aucun `sorry`, aucun `axiom NAME := ...` global, aucun `native_decide`.\n", "- **`lean4-wsl` kernel** - Repare c.380, valide c.426, reutilise pour ce notebook (le pattern << lecture directe des sources >> permet l'execution Python portable sans `lake env lean`).\n", - "- **Notebooks Lean associes** : `[Lean-24 (Calibration)](../Lean-24-Calibration-Native-Companion.ipynb)`, `[Lean-23 (ERC-20)](../Lean-23-ERC20-Invariant-Companion.ipynb)`, `[Lean-22 (Galois)](../Lean-22-Galois-Probleme-Inverse-M23.ipynb)`, `[MIMO-02 (Detection Flips)](MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](../ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`.\n", + "- **Notebooks Lean associes** : `[Lean-24 (Calibration)](../Lean-24-Calibration-Native-Companion.ipynb)`, `[Lean-23 (ERC-20)](../Lean-23-ERC20-Invariant-Companion.ipynb)`, `[Lean-22 (Galois)](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)`, `[MIMO-02 (Detection Flips)](MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](../ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`.\n", "- **EPIC #4980** - convention i18n Lean (lac `mimo_lean` est FR-only ; un sibling pair `_en.lean` est une suite a explorer mais hors scope de ce notebook).\n", "- **Regle C.1** - pas d'erreur volontaire dans les cellules d'exercice (stub `pass` ou `print(\"Exercice a completer\")`).\n", "- **Regle C.7 (count_code_sorry)** - l'instrument canonique `scripts/lean/count_code_sorry.py --json` (champ `distinct_code_sorry`), jamais `grep -c sorry`. Cf [anti-regression.md](../../../../.claude/rules/anti-regression.md)." diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-04-Capstone-Digestion-Python.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-04-Capstone-Digestion-Python.ipynb index de447322524..d149ea07b9f 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-04-Capstone-Digestion-Python.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-04-Capstone-Digestion-Python.ipynb @@ -16,7 +16,7 @@ "source": [ "# MIMO-04 : Capstone — digérer la détection MIMO par flips\n", "\n", - "**Navigation** : [Index](README.md) | [<< MIMO-03 (Converse-Native)](MIMO-03-Converse-Native-Lean.ipynb) | [Lean-22 (Galois) >>](../Lean-22-Galois-Probleme-Inverse-M23.ipynb)\n", + "**Navigation** : [Index](README.md) | [<< MIMO-03 (Converse-Native)](MIMO-03-Converse-Native-Lean.ipynb) | [Lean-22 (Galois) >>](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)\n", "\n", "Ce carnet **digère** la sous-série MIMO plutôt que d'en ajouter une marche : il\n", "reprend le théorème de tête, la borne de queue de norme et le seuil `2·log N`\n", @@ -1207,8 +1207,8 @@ "end_time": "2026-10-20T07:21:21.257847+00:00", "environment_variables": {}, "exception": null, - "input_path": "D:\\dev\\CoursIA-c430-20373-reopen\\MyIA.AI.Notebooks\\SymbolicAI\\Lean\\MIMO\\MIMO-04-Capstone-Digestion-Python.ipynb", - "output_path": "D:\\dev\\CoursIA-c430-20373-reopen\\MyIA.AI.Notebooks\\SymbolicAI\\Lean\\MIMO\\MIMO-04-Capstone-Digestion-Python_output_20261020_092116.ipynb", + "input_path": "MIMO-04-Capstone-Digestion-Python.ipynb", + "output_path": "MIMO-04-Capstone-Digestion-Python.ipynb", "parameters": {}, "start_time": "2026-10-20T07:21:16.141626+00:00", "version": "2.7.0" diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/README.md index da1837160da..e920c004a9a 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/README.md +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/README.md @@ -4,7 +4,7 @@ Sous-série de la série [Lean](../README.md) (décision de gradation #17545, 24 Les numéros de la table ci-dessous renvoient à l'ancien identifiant de série (`Lean-21c` → `MIMO-01`, `Lean-21` → `MIMO-02`, `Lean-21b` → `MIMO-03` — table de correspondance dans `docs/reference/rename-ledger.tsv`). -**Escalier depuis la série principale** : le carnet d'entrée est [Lean-20 — Capstone](../Lean-20-Capstone-Digestions-Tao-Python.ipynb) ; la sous-série se referme sur MIMO-04, puis le parcours principal reprend à [Lean-22 — Galois](../Lean-22-Galois-Probleme-Inverse-M23.ipynb). +**Escalier depuis la série principale** : le carnet d'entrée est [Lean-20 — Capstone](../Lean-20-Capstone-Digestions-Tao-Python.ipynb) ; la sous-série se referme sur MIMO-04, puis le parcours principal reprend à [Lean-22 — Galois](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb). | Carnet | Contenu | Durée | |---|---|---| diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md index 27905cbfd30..bc174774b38 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/README.md @@ -179,7 +179,7 @@ Tous les notebooks incluent une **barre de navigation** en haut et en bas permet | MIMO-02 | [MIMO-02-Detection-Flips-Lean-Python](MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb) | Détection MIMO par flips de coordonnées (Papailiopoulos 2026) : le seuil 2·log N — descente simulée et comptage de flips, probabilité d'échappement du bruit (Monte-Carlo vs `e^{−np}`), `#check` réels des quatre phases et du converse complet `ml_error_prob_ge_threshold` (P(erreur ML) ≥ 1 − e^{−(2·log N − log log N)}) du companion `mimo_lean` (sorry-free, lake externe SLT pour Hanson–Wright) | 45 min | | MIMO-03 | [MIMO-03-Converse-Native-Lean](MIMO/MIMO-03-Converse-Native-Lean.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du lac `mimo_lean` : le lac importé et exécuté dans un kernel Lean 4 réel — la frontière SLT exhibée par `#check` (ce qui est prouvé vs emprunté à `YuanheZ/lean-stat-learning-theory`), les six déclarations de `NormTails` (concentration de Lipschitz gaussienne), les seize briques du converse Hanson–Wright (dont `hanson_wright_noise` et la queue chi-carré `chisq_norm_concentration`), les treize du pont ML (`Bridge`), `#print axioms` sur les théorèmes clés — uniquement les axiomes standards, zéro `sorry` | 40 min | | MIMO-04 | [MIMO-04-Capstone-Digestion-Python](MIMO/MIMO-04-Capstone-Digestion-Python.ipynb) | **Capstone** de la sous-série [MIMO](MIMO/README.md), et sa sortie : les énoncés du lac exécutés à la main sur une instance réelle — les cinq faits de `Descent.lean` vérifiés un par un (le coût **entier** est ce qui rend `run_length_le_cost` vraie), le témoin d'échec `DescentFailure` exhibé sur un canal identité sans bruit (la règle stricte n'accepte aucun flip, la règle relâchée cycle de période 2), la borne de queue de `NormTails` vérifiée par Monte-Carlo sur douze couples `(M, t)`, le seuil `√(2·log(2N/ε))` dérivé du union bound, et la **surface du lac recomptée depuis les sources** — où l'organe canonique rend `56` là où les sources en portent `57`, son motif de nom s'arrêtant au premier caractère grec — kernel `python3`, 3 exercices | 35 min | -| 22 | [Lean-22-Galois-Probleme-Inverse-M23](Lean-22-Galois-Probleme-Inverse-M23.ipynb) | Le problème inverse de Galois refermé (arXiv:2608.08538, 9 août 2026) : M₂₃ prouvé simple d'ordre 10 200 960 **à l'écran** (`card_M23`/`simple_M23` exécutés, `#print axioms` = liste blanche), design de Witt S(4,7,23) vérifié des deux côtés (253 heptades), polynôme f₁ de degré 23 manipulé pour de vrai (empreinte, irréductibilité, discriminant 383 chiffres, Frobenius mod p) — les deux énoncés distingués : prouvé vs cité | 45 min | +| GALOIS-01 | [GALOIS-01-Probleme-Inverse-M23-Python](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) | Le problème inverse de Galois refermé (arXiv:2608.08538, 9 août 2026) : M₂₃ prouvé simple d'ordre 10 200 960 **à l'écran** (`card_M23`/`simple_M23` exécutés, `#print axioms` = liste blanche), design de Witt S(4,7,23) vérifié des deux côtés (253 heptades), polynôme f₁ de degré 23 manipulé pour de vrai (empreinte, irréductibilité, discriminant 383 chiffres, Frobenius mod p) — les deux énoncés distingués : prouvé vs cité | 45 min | | 23 | [Lean-23-ERC20-Invariant-Companion](Lean-23-ERC20-Invariant-Companion.ipynb) | L'invariant de conservation d'un jeton ERC-20 (`Σ balances = totalSupply`) : traces jouets en Python, lectures statiques des 17 déclarations du lake `erc20_lean`, propreté axiomatique par absence de `sorry`, Monte-Carlo sur la tolérance numérique (#11710) | 40 min | | 23b | [Lean-23b-Lean-ERC20-Native-Companion](Lean-23b-Lean-ERC20-Native-Companion.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du lake `erc20_lean` : les 17 déclarations **résolues par le kernel** (`#check`), l'invariant et les transitions `mint`/`burn`/`transfer` **évalués** sur un état concret (`by decide`), `#print axioms` natif sur les 5 théorèmes phares, la pyramide op → trace `Reachable` → invariant type-checkée avec ses gardes (#11721) | 30 min | | 24 | [Lean-24-Calibration-Native-Companion](Lean-24-Calibration-Native-Companion.ipynb) | Compagnon **natif** (kernel `lean4-wsl`) du lake `calibration_lean` : les cibles de calibration du prouveur multi-agents (Epic #1453, P1-P5) importées et exécutées — chaque définition/théorème interrogé par `#check`/`#eval`/`#print axioms`, sorties du compilateur Lean | 35 min | @@ -234,7 +234,7 @@ A l'issue de la série, vous saurez : - **Formaliser les invariants de nœuds** : PD-codes, mouvements de Reidemeister et tricolorabilité de Fox, en s'appuyant sur le companion `knot_lean` (transfert de tricolorabilité le long d'un twist R1 connecté, preuve forward sorry-free + backward partielle). Notebooks 17a, 17b. -- **Lire le paysage galoisien moderne** : la preuve formelle que **M₂₃ (groupe sporadique de Mathieu d'ordre 10 200 960) est simple** est *vendored* dans le companion `galois_lean/` (PR #10486, août 2026, Apache-2.0) ; la réalisation galoisienne — *M₂₃ groupe de Galois sur ℚ* — est **prouvée** dans le préprint (Huang–Jackson–Lee–Poonen–Pries–Zhang, arXiv:2608.08538, 9 août 2026 : polynôme explicite f₁ de degré 23, identification `23T5`) mais **non formalisée** — le notebook [Lean-22](Lean-22-Galois-Probleme-Inverse-M23.ipynb) exécute la preuve formelle côté groupe et vérifie f₁ computationnellement, les deux énoncés soigneusement distingués (Epic #10478). +- **Lire le paysage galoisien moderne** : la preuve formelle que **M₂₃ (groupe sporadique de Mathieu d'ordre 10 200 960) est simple** est *vendored* dans le companion `GALOIS/galois_lean/` (PR #10486, août 2026, Apache-2.0) ; la réalisation galoisienne — *M₂₃ groupe de Galois sur ℚ* — est **prouvée** dans le préprint (Huang–Jackson–Lee–Poonen–Pries–Zhang, arXiv:2608.08538, 9 août 2026 : polynôme explicite f₁ de degré 23, identification `23T5`) mais **non formalisée** — le notebook [Lean-22](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) exécute la preuve formelle côté groupe et vérifie f₁ computationnellement, les deux énoncés soigneusement distingués (Epic #10478). - **Construire le témoin d'une incohérence** : le Dutch book de de Finetti — si les prix violent l'inclusion-exclusion, un livret (+1,+1,−1,−1) encaisse l'écart uniformément dans tous les états (miroir exact du lake `decision_theory_lean`, arithmétique exacte `Fraction`), et un balayage borné certifie l'absence de livre sur le système réparé ; symétriquement, seule la transformation **affine** d'une utilité vNM préserve les préférences (0 divergence) quand le carré en fabrique (124 sur les 2145 paires de 66 loteries). Notebook 25. @@ -507,7 +507,6 @@ Lean/ ├── Lean-17b-Knots-Invariants-Companion.ipynb # Python kernel - invariants de nœuds (PD-codes, Reidemeister, Fox tricolorability), compagnon knot_lean ├── Lean-17c-Knots-Companion-Formel.ipynb # Python kernel - companion formel knot_lean (modules non cités par 17b, murs R2/R3, miroir i18n) ├── Lean-20-Capstone-Digestions-Tao-Python.ipynb # Python kernel - escalier capstone vers la sous-série ANALYSE : digestion du travail formel de T. Tao, règle de chaîne, distance de Ruzsa -├── Lean-22-Galois-Probleme-Inverse-M23.ipynb # Python kernel - problème inverse de Galois (M₂₃ simple prouvé, f₁ degré 23 vérifié) ├── Lean-23-ERC20-Invariant-Companion.ipynb # Python kernel - invariant ERC-20 (lecture statique du lake erc20_lean, Monte-Carlo) ├── Lean-23b-Lean-ERC20-Native-Companion.ipynb # Lean4 (WSL) kernel - ERC-20 natif (17 déclarations résolues par le kernel) ├── Lean-24-Calibration-Native-Companion.ipynb # Lean4 (WSL) kernel - calibration_lean natif (cibles prover P1-P5, #print axioms) @@ -573,6 +572,7 @@ Lean/ │ ├── serre100_lean/ # Lake Serre 100 (Hasse, MZV, Yoneda, caractères — modules FR + jumeaux _en) │ └── README.md ├── MIMO/ # Sous-série détection MIMO par flips (budget de descente, seuil 2·log N, converse natif) — gradation #17545 : [README](MIMO/README.md) +├── GALOIS/ # Sous-série îlot : problème inverse de Galois (M₂₃ simple prouvé, f₁ degré 23 vérifié) — carnet GALOIS-01 + lake [galois_lean](GALOIS/galois_lean/README.md), gradation #17545 : [README](GALOIS/README.md) │ ├── MIMO-01-Descente-Budget-Lean-Python.ipynb # Python kernel - budget de descente (décroissance borne les flips, #12219) │ ├── MIMO-02-Detection-Flips-Lean-Python.ipynb # Python kernel - détection MIMO par flips (seuil 2·log N, companion mimo_lean) │ ├── MIMO-03-Converse-Native-Lean.ipynb # Lean4 (WSL) kernel - converse MIMO natif (NormTails, Hanson-Wright, #print axioms) @@ -586,7 +586,6 @@ Lean/ ├── grothendieck_lean/ # Grothendieck tribute workspace (0 sorry, Lake build) ├── knot_lean/ # Knot theory workspace (théorie des nœuds, companion Lean-17a/b, sorries résiduels documentés, Lake build) ├── calibration_lean/ # Cibles de calibration du prouveur multi-agents (Epic #1453, P1-P5) - 0 sorry, Lake build -├── galois_lean/ # Problème inverse de Galois : M₂₃ (Mathieu 23) groupe simple + preuve formelle vendored (Apache-2.0, [KitaKen1/finite-simple-groups-lean](https://github.com/KitaKen1/finite-simple-groups-lean)) - 0 sorry, Lake build ; companion du notebook Lean-22 (Epic #10478, arXiv:2608.08538, août 2026) ├── hecke_lean/ # Opérateurs de Hecke T_p/U_p (port pédagogique de anthropics/fermats-last-theorem, Apache-2.0, toolchain pinée v4.33.0) - 0 sorry, Lake build ; companion du notebook Lean-29 ├── formal_groups_lean/ # Groupes formels multivariés (port de anthropics/fermats-last-theorem, Apache-2.0) - 0 sorry, Lake build ; companion du notebook Lean-30 ├── formal_logic_lean/ # Logique formelle : Foundation piné + ProvabilityLogic (CONSUMER_PINNÉ) - 0 sorry, Lake build ; companion des notebooks Lean-3b/34/34b (Epic #15066) diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/25-groupe-galois-calcule.ipynb b/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/25-groupe-galois-calcule.ipynb index 5a9ba6fff7d..a955a04ec36 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/25-groupe-galois-calcule.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/25-groupe-galois-calcule.ipynb @@ -25,7 +25,7 @@ "3. **L'étau** — trois conditions nécessaires (transitivité, types observés, $A_n$) croisent l'énumération des sous-groupes de $S_n$ ; le candidat minimal **est** le groupe, mesuré sur neuf polynômes.\n", "4. **La résolvante cubique** — la route classique du quartique, croisée avec l'étau : les deux instruments concordent.\n", "5. **Quintiques** — $S_5$ contre $A_5$ ; la série dérivée **calculée** (Abel-Ruffini en execution) ; Cardano vérifié au $10^{-12}$ près contre les racines numériques.\n", - "6. **Neuf témoins du problème inverse** — $C_3$, $S_3$, $V_4$, $C_4$, $D_4$, $A_4$, $S_4$, $A_5$, $S_5$ réalisés sur $\\mathbb{Q}$, et le pont vers le dixième témoin, $M_{23}$, au carnet [Lean-22](../Lean-22-Galois-Probleme-Inverse-M23.ipynb).\n", + "6. **Neuf témoins du problème inverse** — $C_3$, $S_3$, $V_4$, $C_4$, $D_4$, $A_4$, $S_4$, $A_5$, $S_5$ réalisés sur $\\mathbb{Q}$, et le pont vers le dixième témoin, $M_{23}$, au carnet [Lean-22](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb).\n", "\n", "*Source primaire* : J.-P. Serre, *Topics in Galois Theory*, Second Edition, A K Peters (2008) — le calcul du groupe de Galois y est précisément le geste de ce carnet, poussé ici jusqu'aux quintiques." ] @@ -1341,7 +1341,7 @@ "source": [ "### Lecture du résultat\n", "\n", - "La table se lit par ordre croissant : de $C_3$ (ordre 3, le corps cubique cyclique de discriminant $81$) à $S_5$ (ordre 120, l'équation générique) — mais ce n'est **pas** une chaîne d'inclusions. Deux témoins partagent l'ordre 4 sans être isomorphes ($C_4$ et $V_4$), et $D_4$ (ordre 8) n'a aucun plongement dans $A_5$, dont l'ordre 60 n'est pas divisible par 8. Chacun est en revanche le groupe d'un polynôme explicite, identifié par la machine de ce carnet. La colonne « résoluble » coupe net : tout ce qui est sous $S_4$ l'est, les deux groupes $A_5$ et $S_5$ ne le sont pas — la frontière d'Abel-Ruffini traverse **exactement** la table. Le dixième témoin attend le lecteur au carnet [Lean-22](../Lean-22-Galois-Probleme-Inverse-M23.ipynb) : $M_{23}$, groupe simple sporadique d'ordre $10\\,200\\,960$, réalisé par un polynôme explicite de degré 23 — là où ce carnet s'arrête aux degrés où l'énumération exhaustive des sous-groupes reste un calcul de bureau, la réalisation de $M_{23}$ est un théorème de 2026 refermé le 9 août, et le carnet Lean-22 en exécute la preuve vérifiée par le noyau." + "La table se lit par ordre croissant : de $C_3$ (ordre 3, le corps cubique cyclique de discriminant $81$) à $S_5$ (ordre 120, l'équation générique) — mais ce n'est **pas** une chaîne d'inclusions. Deux témoins partagent l'ordre 4 sans être isomorphes ($C_4$ et $V_4$), et $D_4$ (ordre 8) n'a aucun plongement dans $A_5$, dont l'ordre 60 n'est pas divisible par 8. Chacun est en revanche le groupe d'un polynôme explicite, identifié par la machine de ce carnet. La colonne « résoluble » coupe net : tout ce qui est sous $S_4$ l'est, les deux groupes $A_5$ et $S_5$ ne le sont pas — la frontière d'Abel-Ruffini traverse **exactement** la table. Le dixième témoin attend le lecteur au carnet [Lean-22](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) : $M_{23}$, groupe simple sporadique d'ordre $10\\,200\\,960$, réalisé par un polynôme explicite de degré 23 — là où ce carnet s'arrête aux degrés où l'énumération exhaustive des sous-groupes reste un calcul de bureau, la réalisation de $M_{23}$ est un théorème de 2026 refermé le 9 août, et le carnet Lean-22 en exécute la preuve vérifiée par le noyau." ] }, { @@ -1714,7 +1714,7 @@ "- **Abel-Ruffini exécuté** (§5) : la série dérivée de $S_5$ boucle sur le groupe parfait $A_5$ — calcul exact — tandis que Cardano, vérifié à $10^{-12}$, montre les radicaux traversant $\\mathbb{C}$ pour atterrir sur des racines réelles (*casus irreducibilis*) ;\n", "- **neuf témoins du problème inverse** (§6), du corps cyclique $C_3$ à l'équation générique $S_5$, avec la frontière de solvabilité traversant la table exactement entre $S_4$ et $A_5$.\n", "\n", - "L'ouverture est celle de Serre : pour identifier les groupes au-delà du degré 5 — et pour en **réaliser** de nouveaux sur $\\mathbb{Q}$ — l'énumération exhaustive rend sa place à la méthode de rigidité, et le témoin $M_{23}$ du carnet [Lean-22](../Lean-22-Galois-Probleme-Inverse-M23.ipynb) montre les deux versants réunis : le groupe calculé *et* la preuve vérifiée par le noyau Lean 4.\n", + "L'ouverture est celle de Serre : pour identifier les groupes au-delà du degré 5 — et pour en **réaliser** de nouveaux sur $\\mathbb{Q}$ — l'énumération exhaustive rend sa place à la méthode de rigidité, et le témoin $M_{23}$ du carnet [Lean-22](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) montre les deux versants réunis : le groupe calculé *et* la preuve vérifiée par le noyau Lean 4.\n", "\n", "*Sources* : J.-P. Serre, *Topics in Galois Theory*, 2e éd., A K Peters (2008), leçons 1 et 4 ; J.-P. Serre, *A Course in Arithmetic*, ch. VI pour l'arrière-plan du carnet 17 ; G. Cardano, *Ars Magna* (1545), chap. XI — le casus irreducibilis y est le chapitre que ce carnet exécute." ] @@ -1744,8 +1744,8 @@ "end_time": "2026-10-25T13:40:12.086367+00:00", "environment_variables": {}, "exception": null, - "input_path": "C:\\dev\\CoursIA-16334-carnet25\\MyIA.AI.Notebooks\\SymbolicAI\\Lean\\Serre100\\25-groupe-galois-calcule.ipynb", - "output_path": "C:/dev/CoursIA-16334-carnet25/MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/25-groupe-galois-calcule.ipynb", + "input_path": "25-groupe-galois-calcule.ipynb", + "output_path": "25-groupe-galois-calcule.ipynb", "parameters": {}, "start_time": "2026-10-25T13:39:20.428392+00:00", "version": "2.7.0" diff --git a/MyIA.AI.Notebooks/SymbolicAI/SmartContracts/02-Solidity-Advanced/SC-7b-ERC20-Lean-Verification-Companion.ipynb b/MyIA.AI.Notebooks/SymbolicAI/SmartContracts/02-Solidity-Advanced/SC-7b-ERC20-Lean-Verification-Companion.ipynb index 661879f95bb..bac584be27a 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/SmartContracts/02-Solidity-Advanced/SC-7b-ERC20-Lean-Verification-Companion.ipynb +++ b/MyIA.AI.Notebooks/SymbolicAI/SmartContracts/02-Solidity-Advanced/SC-7b-ERC20-Lean-Verification-Companion.ipynb @@ -659,7 +659,7 @@ "- **Le lac `erc20_lean`** (`MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/`) — la version Lean 4 + Mathlib, le « moteur formel » du présent notebook.\n", "- **Le notebook compagnon [`Lean-23`](../../Lean/Lean-23-ERC20-Invariant-Companion.ipynb)** — qui interroge directement le lac Lean pour ses `#check` réels.\n", "- **EPIC #4980** — convention i18n Lean (sibling pair FR/EN dans `ERC20.lean` / `ERC20_en.lean`).\n", - "- Notebooks associés : `[SC-08-DeFi-Primitives-Python](./SC-08-DeFi-Primitives-Python.ipynb)`, `[SC-11 (LLM-Assisted)](./SC-11-LLM-Assisted-Python.ipynb)`, `[Lean-22 (Galois) >](../../Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb)`." + "- Notebooks associés : `[SC-08-DeFi-Primitives-Python](./SC-08-DeFi-Primitives-Python.ipynb)`, `[SC-11 (LLM-Assisted)](./SC-11-LLM-Assisted-Python.ipynb)`, `[Lean-22 (Galois) >](../../Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)`." ] } ], diff --git a/_quarto.yml b/_quarto.yml index 529e1c459e9..ebebf0b613c 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -450,7 +450,7 @@ project: - "MyIA.AI.Notebooks/SymbolicAI/Lean/finiteness_lean/README.md" - "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/README.md" - "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_logic_lean/README.md" - - "MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/README.md" + - "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/geometry_lean/README.md" - "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/README.md" - "MyIA.AI.Notebooks/SymbolicAI/Lean/GOL/README.md" diff --git a/translations/smartcontracts/smartcontracts.csv b/translations/smartcontracts/smartcontracts.csv index 0ad3eb83cc6..d9fa4e9bd08 100644 --- a/translations/smartcontracts/smartcontracts.csv +++ b/translations/smartcontracts/smartcontracts.csv @@ -22734,7 +22734,7 @@ MyIA.AI.Notebooks/SymbolicAI/SmartContracts/02-Solidity-Advanced/SC-7b-ERC20-Lea - **Le lac `erc20_lean`** (`MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/`) — la version Lean 4 + Mathlib, le « moteur formel » du présent notebook. - **Le notebook compagnon [`Lean-23`](../../Lean/Lean-23-ERC20-Invariant-Companion.ipynb)** — qui interroge directement le lac Lean pour ses `#check` réels. - **EPIC #4980** — convention i18n Lean (sibling pair FR/EN dans `ERC20.lean` / `ERC20_en.lean`). -- Notebooks associés : `[SC-08-DeFi-Primitives-Python](./SC-08-DeFi-Primitives-Python.ipynb)`, `[SC-11 (LLM-Assisted)](./SC-11-LLM-Assisted-Python.ipynb)`, `[Lean-22 (Galois) >](../../Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb)`.",,,,,,,,be29bc2a1b5dfc30,,,,,,,,,,,,,,, +- Notebooks associés : `[SC-08-DeFi-Primitives-Python](./SC-08-DeFi-Primitives-Python.ipynb)`, `[SC-11 (LLM-Assisted)](./SC-11-LLM-Assisted-Python.ipynb)`, `[Lean-22 (Galois) >](../../Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)`.",,,,,,,,be29bc2a1b5dfc30,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/SmartContracts/04-Privacy-Cryptography/SC-15-Zero-Knowledge-Proofs-Python.ipynb,f1a2b3c4,markdown,fr,6882a1e5a71039a2,"### Exercice : Implementer la transformation de Fiat-Shamir La classe `SchnorrZKP` ci-dessus implemente la transformation de Fiat-Shamir pour diff --git a/translations/symbolicai-lean/symbolicai-lean.csv b/translations/symbolicai-lean/symbolicai-lean.csv index 35f9b76c11a..db072ed8934 100644 --- a/translations/symbolicai-lean/symbolicai-lean.csv +++ b/translations/symbolicai-lean/symbolicai-lean.csv @@ -31237,11 +31237,11 @@ L'Epic Terry Tao 2026 est complète au sens de notre serie : on a digeste les ** - PR #10761 (Phase 1 Sendov corps, MERGEABLE). - PR courant (Phase 2 Analysis, Lean-19). - Lean-12 Sensitivity (Huang), Lean-13 Kochen-Specker, Lean-15b Grothendieck, Lean-17 Knots, A* Optimalite (Search-03e), Lean-18 Sendov.",,,,,,,,568027948d36ada1,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,cbd23bde,markdown,fr,82adbd91889f3533,"# Lean-22 : Le problème inverse de Galois — M₂₃ refermé le 9 août 2026 +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,cbd23bde,markdown,fr,82adbd91889f3533,"# GALOIS-01 : Le problème inverse de Galois — M₂₃ refermé le 9 août 2026 **Série** : SymbolicAI / Lean — Théorie des groupes sporadiques et problème inverse de Galois -**Navigation** : [<< MIMO-04 Capstone](MIMO/MIMO-04-Capstone-Digestion-Python.ipynb) | [Index](README.md) +**Navigation** : [<< MIMO-04 Capstone](../MIMO/MIMO-04-Capstone-Digestion-Python.ipynb) | [Index](README.md) | [Lean-23 (ERC-20) >>](../Lean-23-ERC20-Invariant-Companion.ipynb) **Kernel** : Python 3 (vérifications exécutables sympy + extraction Steiner) + Lean 4 via WSL (`galois_lean`, source de vérité formelle — sections 3 et 4) @@ -31261,7 +31261,7 @@ La confusion entre ces deux énoncés serait la même que celle entre « ce grou et « ce groupe apparaît comme groupe de symétries d'une équation polynomiale à coefficients rationnels ». Le premier est un fait de théorie des groupes finis ; le second est un fait de théorie des nombres algébriques — et il a résisté quarante ans de plus.",,,,,,,,82adbd91889f3533,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,b2927c0b,markdown,fr,20d8590e2dc77b9c,"## 1. L'histoire : de Hilbert au 9 août 2026 +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,b2927c0b,markdown,fr,20d8590e2dc77b9c,"## 1. L'histoire : de Hilbert au 9 août 2026 Le **problème inverse de Galois**, posé dans l'ère Hilbert (1890s), demande : *tout groupe fini est-il le groupe de Galois d'une extension de ℚ ?* Hilbert lui-même contribue l'irréductibilité @@ -31285,7 +31285,7 @@ sporadiques est complet. M₂₃ lui-même naît plus tôt : c'est le **groupe d'automorphismes du système de Steiner S(4,7,23)** — le **design de Witt** — découvert par Ernst Witt (1938) via la construction de Leech. C'est par ce système combinatoire que la preuve Lean ci-dessous l'attrape.",,,,,,,,20d8590e2dc77b9c,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,5ac94148,markdown,fr,7e22a74e2dfa0a21,"## 2. Attributions — qui a prouvé quoi, et sous quelle licence +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,5ac94148,markdown,fr,7e22a74e2dfa0a21,"## 2. Attributions — qui a prouvé quoi, et sous quelle licence Ce notebook assemble trois couches de travail, chacune crédité à sa source : @@ -31299,7 +31299,7 @@ Ce notebook assemble trois couches de travail, chacune crédité à sa source : aucun `axiom` déclaré — la preuve repose uniquement sur les axiomes standard de Mathlib (`propext`, `Classical.choice`, `Quot.sound`). L'en-tête amont mentionne une assistance « Claude Code (Fable 5, 1M context) » dans le développement — conservée telle quelle.",,,,,,,,7e22a74e2dfa0a21,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,21fed7ef,code,fr,d27f545f0af4e509,"import sys, re, time +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,21fed7ef,code,fr,d27f545f0af4e509,"import sys, re, time from pathlib import Path # Pattern de la serie : utilitaires Lean cross-plateforme (Epic #2314) @@ -31329,14 +31329,14 @@ print(' sorry en position de preuve :', n_sorry_term, print(' native_decide :', len(re.findall('native_decide', code_only)), '; axiom declares :', len(re.findall('^axiom [A-Za-z]', code_only, re.M))) print(""En-tete licence :"", src.splitlines()[1][:75]) ",,,,,,,,d27f545f0af4e509,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,6f81858e,markdown,fr,b40fe600819f677d,"### Interprétation +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,6f81858e,markdown,fr,b40fe600819f677d,"### Interprétation Le lake `galois_lean` (toolchain v4.33.0, Mathlib épinglé) porte la preuve amont **vendored** : le fichier `M23Lean4Web.lean` contient toute la couche M₂₂ dont dépend M₂₃, la construction de M₂₃ comme sous-groupe de Perm(23), sa cardinalité et sa simplicité. Le compteur d'import confirme la politique d'axiomes annoncée : aucune preuve trouée, aucune décision déléguée au noyau natif sans preuve.",,,,,,,,b40fe600819f677d,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,62d7b786,markdown,fr,302e12cf8506ef3c,"## 3. M₂₃ exécuté — le groupe prouvé à l'écran +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,62d7b786,markdown,fr,302e12cf8506ef3c,"## 3. M₂₃ exécuté — le groupe prouvé à l'écran L'énoncé 1 n'est pas raconté : il est **exécuté**. La cellule suivante fait trois choses : @@ -31345,7 +31345,7 @@ L'énoncé 1 n'est pas raconté : il est **exécuté**. La cellule suivante fait 3. `#print axioms` — la liste exhaustive des axiomes sur lesquels repose la preuve. C'est l'audit d'intégrité : **tout ce qui n'est pas dans la liste blanche standard (`propext`, `Classical.choice`, `Quot.sound`) viderait le théorème** (règle §B du dépôt).",,,,,,,,302e12cf8506ef3c,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,a7ca4c6a,code,fr,b803b16e82cf3250,"import time +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,a7ca4c6a,code,fr,b803b16e82cf3250,"import time t0 = time.time() rc_build, out_build, err_build = run_lake(GALOIS_WSL, 'build', timeout=1200) combined = (out_build or '') + (err_build or '') @@ -31353,7 +31353,7 @@ tail = combined.strip().splitlines()[-4:] if combined.strip() else ['(aucune sor print(f""lake build rc={rc_build}"") print('\n'.join(tail)) print(f""[lake build en {time.time()-t0:.1f}s]"")",,,,,,,,b803b16e82cf3250,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,e08f55f6,code,fr,6254fb48d57f8e92,"snippet = """""" +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,e08f55f6,code,fr,6254fb48d57f8e92,"snippet = """""" import Galois.M23Lean4Web -- Les deux theoremes, enonces par Lean lui-meme @@ -31375,7 +31375,7 @@ t0 = time.time() out = run_lean_snippet(GALOIS_WSL, snippet, timeout=900, snippet_id='m23_thms') print(out) print(f""[lean: {time.time()-t0:.1f}s]"")",,,,,,,,6254fb48d57f8e92,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,1374a2ba,markdown,fr,7a1dc4f1be105895,"### Interprétation +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,1374a2ba,markdown,fr,7a1dc4f1be105895,"### Interprétation - **`Sporadic.card_M23 : Nat.card M23 = 10200960`** — l'ordre du groupe, calculé par une **chaîne de stabilisateurs Schreier–Sims matérialisée** dans la preuve : c'est du comptage @@ -31390,7 +31390,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,1374 du design de Witt S(4,7,23), et le fait que la liste en contient exactement 253. `PreservesHeptads` définit le sous-groupe de Perm(23) qui préserve le système — c'est la **définition combinatoire de M₂₃** — avec sa structure de groupe (`one`, `mul`).",,,,,,,,7a1dc4f1be105895,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,6bdfa409,markdown,fr,2a7386c7e472017e,"## 4. Le design de Witt S(4,7,23) — l'objet combinatoire +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,6bdfa409,markdown,fr,2a7386c7e472017e,"## 4. Le design de Witt S(4,7,23) — l'objet combinatoire M₂₃ se définit comme groupe d'automorphismes du **système de Steiner S(4,7,23)** : 23 points, des blocs de 7 points (les **heptades**), tels que **tout sous-ensemble de 4 points est contenu @@ -31399,7 +31399,7 @@ C(23,4)/C(7,4) = 8855/35 = **253** — exactement ce que `heptadList_length` én Extrayons les 253 heptades du fichier Lean et vérifions la propriété définaire **indépendamment** (en Python, sans Lean) — deux sources de vérité qui se recoupent.",,,,,,,,2a7386c7e472017e,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,2e4ecf38,code,fr,24b14acc7157c726,"import itertools +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,2e4ecf38,code,fr,24b14acc7157c726,"import itertools # Extraction des heptades depuis la source Lean (notation zero-based {a, b, ..., f, g}) blocks_src = re.findall(r'\{(\d+(?:,\s*\d+){6})\}', src) @@ -31421,7 +31421,7 @@ print(f""\n253 attendu (heptadList_length) <-> {n_heptads} extraits : "" print(f""Arithmetique du design : C(23,4)/C(7,4) = {len(list(itertools.combinations(range(23),4)))}"" f""//{len(list(itertools.combinations(range(7),4)))} = "" f""{len(list(itertools.combinations(range(23),4)))//len(list(itertools.combinations(range(7),4)))}"")",,,,,,,,24b14acc7157c726,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,e51728d5,code,fr,0cd2877c076745ff,"# Propriete definatoire (verification partielle, sous-ensemble des 4-subsets) +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,e51728d5,code,fr,0cd2877c076745ff,"# Propriete definatoire (verification partielle, sous-ensemble des 4-subsets) # La verification COMPLETE (8855 quadruplets) est l'Exercice 2. heptad_sets = [frozenset(h) for h in heptads_uniq] quads = list(itertools.combinations(range(23), 4)) @@ -31437,7 +31437,7 @@ for q in sample_quads: print(f"" ANOMALIE : {q} contenu dans {containing} heptades"") print(f""Echantillon de 200 quadruplets : {200 - bad}/200 dans exactement 1 heptade"" + ("" -- PROPRIETE S(4,7,23) VERIFIEE SUR L'ECHANTILLON"" if bad == 0 else "" -- DEFAUT""))",,,,,,,,0cd2877c076745ff,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,dd8f4507,markdown,fr,35383936afa54507,"### Interprétation +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,dd8f4507,markdown,fr,35383936afa54507,"### Interprétation - L'extraction rend **253 heptades distinctes**, chacune de taille 7 sur les points {0..22} — concordant avec `heptadList_length` prouvé en Lean. @@ -31449,7 +31449,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,dd8f `blockMap` en est l'action sur les blocs. La théorie des groupes finis rencontre la combinatoire des designs : c'est par ce chemin que Witt a construit M₂₃ en 1938, bien avant l'ordinateur.",,,,,,,,35383936afa54507,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,0971b5e7,markdown,fr,ed7b79b2756b600f,"## 5. La réalisation galoisienne — citée, pas formalisée +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,0971b5e7,markdown,fr,ed7b79b2756b600f,"## 5. La réalisation galoisienne — citée, pas formalisée L'énoncé 2 (M₂₃ groupe de Galois sur ℚ) repose sur une chaîne théorique que ce dépôt **ne formalise pas**. Elle est citée ici pour être honnête sur la frontière entre prouvé et rapporté : @@ -31470,7 +31470,7 @@ cohomologie — mais **pas** le π₁ étale ni l'existence de Riemann. La phras noir sur blanc : **« M₂₃ est un groupe de Galois sur ℚ » n'est PAS formalisé dans ce dépôt** — il est prouvé dans le préprint, et vérifié computationnellement ci-dessous au niveau du polynôme, pas au niveau du groupe de Galois.",,,,,,,,ed7b79b2756b600f,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,57ef6cf8,code,fr,8ab48d9bf179842f,"# Le socle grothendieckien du depot : ce qui existe deja cote formel +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,57ef6cf8,code,fr,8ab48d9bf179842f,"# Le socle grothendieckien du depot : ce qui existe deja cote formel # (modules reels : la convention i18n #4980 adjoint un sibling _en a chaque # module - exclu du denombrement pour que le compte reste celui des modules) gro = find_lean_project('grothendieck_lean') @@ -31481,7 +31481,7 @@ for m in gro_modules[:14]: print("" ..."" if len(gro_modules) > 14 else """") print(""\nChainon MANQUANT pour l'enonce 2 : pi1 etale / existence de Riemann -> non formalise"") ",,,,,,,,8ab48d9bf179842f,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,e1cbf1d6,markdown,fr,4fb737190eeeceed,"## 6. Le polynôme f₁ — manipulé pour de vrai +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,e1cbf1d6,markdown,fr,4fb737190eeeceed,"## 6. Le polynôme f₁ — manipulé pour de vrai Le préprint produit un polynôme **explicite** de degré 23 à coefficients entiers dont le corps de décomposition sur ℚ a pour groupe de Galois M₂₃. Le dépôt compagnon @@ -31493,7 +31493,7 @@ au total, 14 chiffres pour le plus grand, contenu 1, et `f1(3) mod (2^61−1) = **Nous chargeons les coefficients ci-dessous et recalculons l'empreinte de toutes pièces.** C'est la vérification d'intégrité de la transcription : une seule erreur de chiffre casse l'empreinte.",,,,,,,,4fb737190eeeceed,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,0d5598ec,code,fr,3b23c70058840807,"# Coefficients de f1, transcrits de polynomial_f1.gp (shaowuz/m23isgalois) +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,0d5598ec,code,fr,3b23c70058840807,"# Coefficients de f1, transcrits de polynomial_f1.gp (shaowuz/m23isgalois) # coeff_f1[i] = coefficient de x^i ; l'entree x^22 est absente du fichier (coefficient 0) coeff_f1 = [ -3150159884154, 19169943578802, -46185312415788, 53599151839311, @@ -31518,7 +31518,7 @@ expected = [23, 23, 219, 14, 1, 92254275456192] # f1_expect de polynomial_f1 print(""empreinte calculee :"", fingerprint) print(""empreinte attendue :"", expected) print(""TRANSCRIPTION INTEGRE"" if fingerprint == expected else ""ECART -- retranscrire !"")",,,,,,,,3b23c70058840807,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,63f67d15,code,fr,38bd8219045e21c7,"from sympy import Poly, symbols, factor_list, discriminant, GF +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,63f67d15,code,fr,38bd8219045e21c7,"from sympy import Poly, symbols, factor_list, discriminant, GF x = symbols('x') f1 = sum(c * x**i for i, c in enumerate(coeff_f1)) poly = Poly(f1, x) @@ -31534,7 +31534,7 @@ print(f""factorisation sur Q : {n_factors} facteur(s) -> "" disc = discriminant(f1, x) print(f""discriminant : {len(str(abs(disc)))} chiffres, signe {'+' if disc > 0 else '-'}"") print(f"" (non nul -> f1 separable : 23 racines complexes distinctes)"")",,,,,,,,38bd8219045e21c7,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,cad28d50,code,fr,21830481ab13921b,"# 4. Factorisations modulo quelques premiers -- les cycles de Frobenius +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,cad28d50,code,fr,21830481ab13921b,"# 4. Factorisations modulo quelques premiers -- les cycles de Frobenius # factor_list(modulus=...) declenche en interne (sympy >= 1.13) une comparaison # d'entiers modulaires depreciee ; le warning associe imprime le chemin # site-packages de la machine d'execution. On le filtre : la comparaison vit @@ -31548,7 +31548,7 @@ for p in (2, 3, 5, 7, 11): flp = factor_list(f1, modulus=p) degs = sorted(int(Poly(g, x, domain=GF(p)).degree()) * int(m) for g, m in flp[1]) print(f""{p:>4} | {str(degs):<28} | {sum(degs)}"")",,,,,,,,21830481ab13921b,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,4c33fd85,markdown,fr,5c9b423a56c42c0c,"### Interprétation +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,4c33fd85,markdown,fr,5c9b423a56c42c0c,"### Interprétation - **Irréductible sur ℚ** : f₁ n'a aucun facteur rationnel — le corps ℚ(α) engendré par une racine est de degré 23, et le groupe de Galois (du corps de décomposition) agit @@ -31565,8 +31565,8 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,4c33 `GaloisGroup` + `GaloisProof` certifiant) et n'est **pas reproduite ici**. Notre chaîne sympy établit : degré 23 ✓, irréductible ✓, séparable ✓, Frobenius pairs ✓ — le socle computationnel, pas le théorème.",,,,,,,,5c9b423a56c42c0c,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,f6e7da43,markdown,fr,2edb7813f4129e60,## 7. Exercices,,,,,,,,2edb7813f4129e60,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,5abcf042,code,fr,0cf28ef1a3befec7,"# EXERCICE 1 : l'arithmetique de l'ordre de M23. +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,f6e7da43,markdown,fr,2edb7813f4129e60,## 7. Exercices,,,,,,,,2edb7813f4129e60,,,,,,,,,,,,,,, +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,5abcf042,code,fr,0cf28ef1a3befec7,"# EXERCICE 1 : l'arithmetique de l'ordre de M23. # Objectif : verifier la decomposition multiplicative 10200960 = 253 * 40320 # et la factorisation premiere |M23| = 2^7 * 3^2 * 5 * 7 * 11 * 23, puis expliquer # pourquoi 253 et 40320 apparaissent chacun naturellement dans le design de Witt. @@ -31578,7 +31578,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,5abc # Etape 3 : verifier |M23| / 23 == 443520 et chercher a quoi correspond ce nombre pour M22 result = None # TODO etudiant print(""Exercice 1 a completer : arithmetique de |M23|"")",,,,,,,,0cf28ef1a3befec7,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,b3127f64,code,fr,6156b39a2a928488,"# EXERCICE 2 : la propriete definatoire S(4,7,23) -- verification EXHAUSTIVE. +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,b3127f64,code,fr,6156b39a2a928488,"# EXERCICE 2 : la propriete definatoire S(4,7,23) -- verification EXHAUSTIVE. # Objectif : verifier que CHACUN des C(23,4) = 8855 quadruplets de points est contenu # dans exactement une heptade (la section 4 ne l'a fait que sur un echantillon de 200). # Indice : construire un index heptade -> frozenset, puis pour chaque quadruplet @@ -31588,7 +31588,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,b312 # Etape 3 : assert sur la totalite, et afficher le compte de quadruplets verifies result = None # TODO etudiant print(""Exercice 2 a completer : verification exhaustive S(4,7,23)"")",,,,,,,,6156b39a2a928488,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,c414bd6e,code,fr,26c72377e4600c02,"# EXERCICE 3 : le second polynome f2 du depot compagnon. +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,c414bd6e,code,fr,26c72377e4600c02,"# EXERCICE 3 : le second polynome f2 du depot compagnon. # Objectif : recuperer polynomial_f2.gp depuis https://github.com/shaowuz/m23isgalois, # transcrire les coefficients de f2, puis reproduire sur f2 les memes verifications # que la section 6 (empreinte, degre, irreductibilite sur Q, discriminant, factorisation @@ -31601,7 +31601,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,c414 # Etape 3 : degre / irreductibilite / discriminant / factorisation mod p, meme format result = None # TODO etudiant print(""Exercice 3 a completer : empreinte et verifications de f2"")",,,,,,,,26c72377e4600c02,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,23a28e3b,markdown,fr,b299a0b24b412c2e,"## Conclusion +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,23a28e3b,markdown,fr,b299a0b24b412c2e,"## Conclusion | | Ce notebook a établi | Méthode | Statut | |---|---|---|---| @@ -31619,7 +31619,7 @@ d'arrivée naturel. Une noix de plus à grignoter. via cache), Python 3 avec `sympy`. Durée d'exécution : ~2 min une fois le lake construit.",,,,,,,,b299a0b24b412c2e,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb,85857942,markdown,fr,1891ed9734f58522,"# Lean-23 : ERC-20 sous Lean 4 — l'invariant de conservation prouvé -**Navigation** : [Index](README.md) | [Lean-22 (Galois) <<](Lean-22-Galois-Probleme-Inverse-M23.ipynb) | [SC-7 SmartContract (Solidity ERC-20) >>](../SmartContracts/02-Solidity-Advanced/SC-07-Token-Standards-Python.ipynb) +**Navigation** : [Index](README.md) | [Lean-22 (Galois) <<](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) | [SC-7 SmartContract (Solidity ERC-20) >>](../SmartContracts/02-Solidity-Advanced/SC-07-Token-Standards-Python.ipynb) Ce notebook presente le lake **`erc20_lean`** de ce dépot (issue #4047, livrable phase 1) : le port formel de l'**invariant de conservation d'un jeton ERC-20** en Lean 4 avec Mathlib. La propriété foncière d'un jeton fongible — aucun token ne peut être créé ou détruit par un `transfer` (seuls `mint`/`burn` le font, symétriquement sur l'offre) — est ici prouvée **mécaniquement** sur une machine à états finie de **trois transitions gardées**. @@ -32080,7 +32080,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb,5e1ba4 - **Lake `erc20_lean`** (`MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/`) — la version Lean 4 + Mathlib : `ERC20/State.lean` (modèle), `ERC20/Ops.lean` (transitions), `ERC20/Invariant.lean` (préservation). - **EPIC #4980** — convention i18n Lean (aggrégateur bilingue inline FR-EN dans `ERC20.lean`, sibling pair dans `ERC20_en.lean`). - **Rule C.6** (mandat user 2026-08-19) — préférence pour un compagnon kernel `lean4-wsl` à côté du notebook Python ; le pattern actuel (subprocess `lake env lean`) reste mergeable, le kernel Lean natif est une suite à explorer. -- Notebooks associés : `[Lean-22 (Galois)](Lean-22-Galois-Probleme-Inverse-M23.ipynb)`, `[Lean-21 (MIMO)](MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`, `[Lean-13 (Kochen-Specker)](Lean-13-Kochen-Specker.ipynb)`, `[A* Optimalite (Search-03e)](../../Search/Part1-Foundations/Search-03e-AStar-Optimality.ipynb)`.",,,,,,,,8c4a10cd11f9e5a9,,,,,,,,,,,,,,, +- Notebooks associés : `[Lean-22 (Galois)](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)`, `[Lean-21 (MIMO)](MIMO/MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`, `[Lean-13 (Kochen-Specker)](Lean-13-Kochen-Specker.ipynb)`, `[A* Optimalite (Search-03e)](../../Search/Part1-Foundations/Search-03e-AStar-Optimality.ipynb)`.",,,,,,,,8c4a10cd11f9e5a9,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-06-Mathlib-Essentials-Lean.ipynb,enr6_01,markdown,fr,1aeee3c42aa30e4f,"### Pourquoi ces imports ouvrent toute la bibliothèque Le bloc de gauche **documente** les espaces de noms qu'un projet Mathlib importerait : `Mathlib.Tactic` (les tactiques `ring`, `linarith`, `field_simp`...), `Mathlib.Data.Nat.Basic` (lemmes sur `Nat`), `Mathlib.Algebra` (structures algébriques). **Ce notebook-ci ne les importe pas** : il tourne sous `lean4_jupyter` sans Mathlib, et les exemples Mathlib n'y sont que documentés. En Lean 4, **un import n'est pas un `#include` C** : c'est la déclaration qu'on veut accéder à un module compilé. Dans un projet Lake équipé, `ring` n'existe pas sans `Mathlib.Tactic`, et des lemmes comme `Nat.add_comm` doivent être prouvés à la main sans `Mathlib.Data.Nat`. @@ -34248,7 +34248,7 @@ Le tableau ci-dessus range la géométrie anabelienne au rayon des absents — e Le théorème structurel est de Grothendieck (SGA 7) : la monodromie d'une famille lisse au-dessus d'un disque épointé est **quasi-unipotente** — un certain pouvoir de la transformation est unipotent, autrement dit toutes ses valeurs propres sont des racines de l'unité. C'est un résultat de finitude remarquable : quelle que soit la famille, la boucle ne peut engendrer qu'une torsion d'ordre fini ou quasi fini. Deligne en a fait l'arme centrale de *Weil II* (1980) : c'est en contrôlant la monodromie ℓ-adique qu'il porte l'analogie de Riemann sur les corps finis. Le polynôme d'Alexander du trèfle est précisément le polynôme caractéristique de cette action. -**État mesuré (18/10/2026)** : la série Lean manipule la monodromie sans jamais l'introduire — 4 occurrences d'usage ([Lean-22](Lean-22-Galois-Probleme-Inverse-M23.ipynb) ×1, [Lean-28](Lean-28-Complex-Structure-S6.ipynb) ×2 — le triangle (3,4,∞) « fournit les monodromies » qui donnent la présentation de π₁ — et [Riemann200/README](Riemann200/README.md) ×1), zéro définition. Mathlib 4 ne formalise pas davantage la quasi-unipotence : du hors-scope cohérent avec la table ci-dessus. La première marche — groupe fondamental d'un cercle épointé, action de monodromie d'un exemple explicite, quasi-unipotence calculée — est ouverte en grain dédié : [#21308](https://github.com/jsboige/CoursIA/issues/21308). +**État mesuré (18/10/2026)** : la série Lean manipule la monodromie sans jamais l'introduire — 4 occurrences d'usage ([Lean-22](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) ×1, [Lean-28](Lean-28-Complex-Structure-S6.ipynb) ×2 — le triangle (3,4,∞) « fournit les monodromies » qui donnent la présentation de π₁ — et [Riemann200/README](Riemann200/README.md) ×1), zéro définition. Mathlib 4 ne formalise pas davantage la quasi-unipotence : du hors-scope cohérent avec la table ci-dessus. La première marche — groupe fondamental d'un cercle épointé, action de monodromie d'un exemple explicite, quasi-unipotence calculée — est ouverte en grain dédié : [#21308](https://github.com/jsboige/CoursIA/issues/21308). ",,,,,,,,61f6ecfa7bbca5c6,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-15b-Lean-Grothendieck.ipynb,c34d70af,markdown,fr,d3d23df8ec30fff5,"### Lecture des 18 vérifications : l'index est vivant, pas déclaré @@ -34955,14 +34955,14 @@ score espere atteint la frontiere |E[score]| = 2, puis verifiez sur quelques exe deux profils de scores opposes rapproche E[score] de 0. Indice : tout profil deterministe convient pour la frontiere -- la subtilite est le miroir exact d'`expectedScore`.",,,,,,,,ab662a328edeca86,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16c-Conway-Free-Will-Theorem.ipynb,chsh-code-stub,code,fr,9fb1e9bc5de41fcb,"print(""Exercice a completer"")",,,,,,,,9fb1e9bc5de41fcb,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,07bf3a5c,markdown,fr,55506b61bd2c0e85,"## Annexe A — La complétion adique : approcher un anneau local par ses troncatures +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,07bf3a5c,markdown,fr,55506b61bd2c0e85,"## Annexe A — La complétion adique : approcher un anneau local par ses troncatures Le problème inverse de Galois vit dans les **extensions locales** : pour comprendre un groupe de Galois global, on le regarde une prime à la fois, dans la complétion. Le module `Galois.AdicCompletionLocalRing` (portage pédagogique du dépôt `anthropics/fermats-last-theorem`, commit `aa2d8b34`, Apache-2.0) pose la première couche de cette arithmétique locale : **la complétion adique d'un anneau local `A` par son idéal maximal**. L'idée tient en une image. Un anneau local `A` d'idéal `I` est un monde qu'on ne voit jamais en entier — on n'en observe que des **troncatures** : le quotient `A ⧸ I^n`, « `A` vu modulo la puissance n-ième de l'idéal ». La complétion adique `AdicCompletion I A` est l'objet qui est **compatible avec toutes les troncatures à la fois** : un élément de la complétion, c'est une famille cohérente de classes modulo `I^n` pour chaque `n`. L'évaluation `evalₐ I n` projette la complétion sur sa troncature de degré `n`. Le module établit quatre briques : **Kernel** (le noyau de `evalₐ I n` est exactement l'image de `I^n` — la caractérisation « noyau d'évaluation = puissance de l'idéal »), **Exemple** (la troncature 2-adique de `ℤ`), **Local** (pour `A` local noethérien, l'idéal maximal de la complétion est le noyau de l'évaluation en degré 1, et les quotients par ses puissances s'identifient à ceux de `A`), **Transport** (ces identités survivent aux isomorphismes de complétions).",,,,,,,,55506b61bd2c0e85,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,2ac6c7f6,code,fr,f05e5daa91282735,"snippet_adic = """""" +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,2ac6c7f6,code,fr,f05e5daa91282735,"snippet_adic = """""" import Galois.AdicCompletionLocalRing -- Kernel : troncature, noyau, unites (6 declarations) @@ -35009,7 +35009,7 @@ t0 = time.time() out = run_lean_snippet(GALOIS_WSL, snippet_adic, timeout=900, snippet_id='adic_thms') print(out) print(f""[lean: {time.time()-t0:.1f}s]"")",,,,,,,,f05e5daa91282735,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,33c56f41,code,fr,1f83f42317d4a916,"# La troncature 2-adique de Z, jouee en Python : eval_3(20) = classe de 4 dans Z/8 +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,33c56f41,code,fr,1f83f42317d4a916,"# La troncature 2-adique de Z, jouee en Python : eval_3(20) = classe de 4 dans Z/8 # (l'example de la section Exemple du module, rejouee hors Lean) print(""eval_3(20) ="", 20 % 8, "" (classe de 4 dans Z/2^3 = Z/8, comme dans le module)"") print(""eval_3(8) ="", 8 % 8, "" (8 est multiple de 2^3 : image de I^3, dans le noyau)"") @@ -35028,12 +35028,12 @@ for n in [3, 5, 8, 12]: print(""\nChaque troncature ETEND la precedente en poids faibles : la suite est"") print(""coherente pour le systeme projectif Z/2^n -> Z/2^{n-1}. La complétion Z_2"") print(""est exactement la limite de ce systeme -- c'est elle que le module formalise."")",,,,,,,,1f83f42317d4a916,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,58a7db93,markdown,fr,604c82b149d36876,"### Interprétation +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,58a7db93,markdown,fr,604c82b149d36876,"### Interprétation Les `#check` rendent les **signatures vivantes** : `ker_evalₐ_eq_map_pow` dit que le noyau de la troncature de degré `n` est l'image de `I^n` — la phrase « noyau d'évaluation = puissance de l'idéal » devient un énoncé Lean imprimé par Lean. Les trois `#print axioms` ne listent que `propext`, `Classical.choice`, `Quot.sound` : **aucun `sorry`**, aucune tricherie — la couche est close. La démo Python porte le même contenu dans ℤ : `evalₐ (2) 3` envoie 20 sur sa classe mod 8, l'inverse de 3 se stabilise chiffre après chiffre. C'est le pont entre le formalisme (une limite de systèmes projectifs) et le geste de tous les jours en calcul modulaire. Et `isUnit_one_add_of_mem_map_maximalIdeal` est le **germe de Hensel** : dans un anneau local, tout `1 + x` avec `x` dans l'idéal maximal de la complétion est inversible — la lemme qui fait converger Newton p-adiquement, et qui rend possible la suite de la théorie locale.",,,,,,,,604c82b149d36876,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,df47cf61,markdown,fr,681fdb935b70724b,"## Annexe B — Groupes de ramification inférieure : la tour qui mesure l'inertie +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,df47cf61,markdown,fr,681fdb935b70724b,"## Annexe B — Groupes de ramification inférieure : la tour qui mesure l'inertie Quand un groupe `G` agit sur un anneau local `R`, tout ne se passe pas « près du point » au même degré. Le module `Galois.LowerRamificationGroup` (même provenance : portage `anthropics/fermats-last-theorem`, Apache-2.0) construit l'objet qui **gradue** cette action : les groupes de ramification inférieure de Serre (*Local Fields*, ch. IV). @@ -35044,7 +35044,7 @@ Trois étages s'emboîtent : 3. **Ramification inférieure** — la tour `lowerRamificationGroup R G i` : les `σ ∈ G` qui fixent `R` **modulo `𝔪^(i+1)`**. On obtient `G ⊇ LRG(0) ⊇ LRG(1) ⊇ ⋯`, **antitone** (`lowerRamificationGroup_antitone`), dont le niveau 0 est exactement l'inertie (`lowerRamificationGroup_zero_eq_inertia`) et dont l'intersection est triviale (`iInf_lowerRamificationGroup_eq_bot`). Le lien avec l'annexe A est structurel : **la tour `LRG(i)` est définie par l'action sur les niveaux de la tour de troncatures que la complétion adique construit**. `LRG(i)` = les automorphismes invisibles au degré `i` de résolution.",,,,,,,,681fdb935b70724b,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,72ce43e0,code,fr,8c5fc06feded48ca,"snippet_ram = """""" +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,72ce43e0,code,fr,8c5fc06feded48ca,"snippet_ram = """""" import Galois.LowerRamificationGroup -- Inertie : monotonie, cas trivial, normalite (6 declarations) @@ -35083,7 +35083,7 @@ t0 = time.time() out = run_lean_snippet(GALOIS_WSL, snippet_ram, timeout=900, snippet_id='ramif_thms') print(out) print(f""[lean: {time.time()-t0:.1f}s]"")",,,,,,,,8c5fc06feded48ca,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,75ae5a04,code,fr,c20eb585fa365394,"# Les niveaux que fixe la tour : dans Z, l'ideal maximal (2) et ses puissances. +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,75ae5a04,code,fr,c20eb585fa365394,"# Les niveaux que fixe la tour : dans Z, l'ideal maximal (2) et ses puissances. # LRG(i) agit trivialement sur Z/2^{i+1} -- chaque groupe de la tour est ""invisible"" # a un etage de la tour de troncatures de l'annexe A. print(""Etage | quotient | ce que LRG(i) respecte"") @@ -35100,7 +35100,7 @@ print(f"" niveau 5 : {x % 2**5}"") print(""Projeter d'un etage au suivant = oublier les chiffres 2-adiques de poids fort :"") print(""c'est le systeme projectif dont la completion est la limite, et la tour LRG(i)"") print(""gradue les automorphismes selon le niveau ou ils cessent d'etre visibles."")",,,,,,,,c20eb585fa365394,,,,,,,,,,,,,,, -MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb,d834025a,markdown,fr,fe5b6921f7122c57,"### Interprétation +MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb,d834025a,markdown,fr,fe5b6921f7122c57,"### Interprétation La signature de `lowerRamificationGroup_antitone` imprimée par Lean dit l'essentiel : `Antitone (lowerRamificationGroup R G)` — **plus on monte dans la tour, plus le groupe rétrécit**. Et `lowerRamificationGroup_zero_eq_inertia` ancre le pied de la tour : le niveau 0 EST l'inertie, la ramification inférieure est son raffinement quantitatif. Enfin `iInf_lowerRamificationGroup_eq_bot` ferme la tour : un automorphisme invisible à **tous** les niveaux est l'identité — la filtration sépare les points de `G`. @@ -58315,7 +58315,7 @@ Trois parcours sont possibles, selon le temps et la curiosité : 2. **Curieux (2h30)** : `MIMO-01` → `MIMO-02` (le seuil et la simulation). On quitte en ayant vu le seuil `2·log N` confrontée au Monte-Carlo. 3. **Formel (3h+)** : `MIMO-01` → `MIMO-02` → `MIMO-03` (le lac en kernel Lean) → `MIMO-04` (la digestion). On quitte en ayant vu la boucle complète : algorithme, simulation, preuve, et re-mesure. -Le parcours principal reprend ensuite à [Lean-22 — Galois](Lean-22-Galois-Probleme-Inverse-M23.ipynb) (sous-série Galois) ou à [Lean-29 — Hecke Operators](Lean-29-Hecke-Operators-Native.ipynb) (sous-série Hecke). +Le parcours principal reprend ensuite à [Lean-22 — Galois](GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) (sous-série Galois) ou à [Lean-29 — Hecke Operators](Lean-29-Hecke-Operators-Native.ipynb) (sous-série Hecke). ",,,,,,,,97f2c0314a267dfe,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-41-Capstone-MIMO-Python.ipynb,64eca5b6,markdown,fr,8349dda7175a8f6c,"## 7. Trois exercices @@ -58999,7 +58999,7 @@ MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-01-Descente-Budget-Lean-Python.ipynb - **D. Papailiopoulos** (2026) - *Detection MIMO by coordinate flips : Proposition 9.1* (papier source des theoremes `Descent.lean`). - **`Descent.lean`** (`MyIA.AI.Notebooks/SymbolicAI/Lean/mimo_lean/`) - la formalisation : `Run` (inductif sur List sigma), `lastState`, lemmes 1-3 (`run_tail_cost_lt`, `run_nodup`, `run_length_le_cost`), `descent_flips_le_barrier`, `descent_target_before_ceiling`. Aucun `sorry`, aucun `axiom NAME := ...` global, aucun `native_decide`. - **`lean4-wsl` kernel** - Repare c.380, valide c.426, reutilise pour ce notebook (le pattern << lecture directe des sources >> permet l'execution Python portable sans `lake env lean`). -- **Notebooks Lean associes** : `[Lean-24 (Calibration)](../Lean-24-Calibration-Native-Companion.ipynb)`, `[Lean-23 (ERC-20)](../Lean-23-ERC20-Invariant-Companion.ipynb)`, `[Lean-22 (Galois)](../Lean-22-Galois-Probleme-Inverse-M23.ipynb)`, `[MIMO-02 (Detection Flips)](MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](../ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`. +- **Notebooks Lean associes** : `[Lean-24 (Calibration)](../Lean-24-Calibration-Native-Companion.ipynb)`, `[Lean-23 (ERC-20)](../Lean-23-ERC20-Invariant-Companion.ipynb)`, `[Lean-22 (Galois)](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb)`, `[MIMO-02 (Detection Flips)](MIMO-02-Detection-Flips-Lean-Python.ipynb)`, `[Lean-20 (PFR)](../ANALYSE/ANALYSE-03-PFR-Lean.ipynb)`. - **EPIC #4980** - convention i18n Lean (lac `mimo_lean` est FR-only ; un sibling pair `_en.lean` est une suite a explorer mais hors scope de ce notebook). - **Regle C.1** - pas d'erreur volontaire dans les cellules d'exercice (stub `pass` ou `print(""Exercice a completer"")`). - **Regle C.7 (count_code_sorry)** - l'instrument canonique `scripts/lean/count_code_sorry.py --json` (champ `distinct_code_sorry`), jamais `grep -c sorry`. Cf [anti-regression.md](../../../../.claude/rules/anti-regression.md).",,,,,,,,94ddbd11eb556c94,,,,,,,,,,,,,,, @@ -60793,7 +60793,7 @@ calculées. `#check` + `#print axioms` + `#eval Float`).",,,,,,,,62702152b9d9ebb6,,,,,,,,,,,,,,, MyIA.AI.Notebooks/SymbolicAI/Lean/MIMO/MIMO-04-Capstone-Digestion-Python.ipynb,1b83a2b6,markdown,fr,c2a38e64d04a364a,"# MIMO-04 : Capstone — digérer la détection MIMO par flips -**Navigation** : [Index](README.md) | [<< MIMO-03 (Converse-Native)](MIMO-03-Converse-Native-Lean.ipynb) | [Lean-22 (Galois) >>](../Lean-22-Galois-Probleme-Inverse-M23.ipynb) +**Navigation** : [Index](README.md) | [<< MIMO-03 (Converse-Native)](MIMO-03-Converse-Native-Lean.ipynb) | [Lean-22 (Galois) >>](../GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb) Ce carnet **digère** la sous-série MIMO plutôt que d'en ajouter une marche : il reprend le théorème de tête, la borne de queue de norme et le seuil `2·log N` diff --git a/translations/translation_golden_set.json b/translations/translation_golden_set.json index 2d831b702d7..d2949bfcab5 100644 --- a/translations/translation_golden_set.json +++ b/translations/translation_golden_set.json @@ -77,13 +77,6 @@ "sha1_fr": "ac7992fff2bed1e766e8b931388abbbcdcfa92b3", "sha1_en": "fefd93374cbc7e707a8fd106ea9b6b1467057666" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment.lean", - "en": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment_en.lean", - "sha1_fr": "3d14c7b6aa900d8b55809ca7393d820977b966f2", - "sha1_en": "c0a0a88e822f8ee4b4b4525bdf8eecdcefbc8db7" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment/Counting.lean", @@ -121,10 +114,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation.lean", - "en": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation_en.lean", - "sha1_fr": "d1fb3e1e97b0f152abce5bdd7addec73abe85254", - "sha1_en": "bd9e52d461f773aa797c7c9063740b647b0a1534" + "fr": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment.lean", + "en": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment_en.lean", + "sha1_fr": "3d14c7b6aa900d8b55809ca7393d820977b966f2", + "sha1_en": "c0a0a88e822f8ee4b4b4525bdf8eecdcefbc8db7" }, { "kind": "lean", @@ -161,6 +154,13 @@ "sha1_fr": "38d5cd424c94668731b554339af3778682c30f64", "sha1_en": "f8910e2ce4972ccf65178d42ea81e1873b545186" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation.lean", + "en": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation_en.lean", + "sha1_fr": "d1fb3e1e97b0f152abce5bdd7addec73abe85254", + "sha1_en": "bd9e52d461f773aa797c7c9063740b647b0a1534" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/README.md", @@ -224,13 +224,6 @@ "sha1_fr": "0c159feb83d0844dd1afa10c83319c1c921b560e", "sha1_en": "7ae0fede4b9a3c1d34cbe04ede50fd1d067b54bf" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames.lean", - "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames_en.lean", - "sha1_fr": "d0c3ea2af84d21a83c22d954491a506201254e5c", - "sha1_en": "a8f538a6dd7f0ee7af6894d56df2ed314f5ea8f3" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames/Discounting.lean", @@ -259,6 +252,13 @@ "sha1_fr": "0cb39d2b32feaaa5b5b0eb122f168a5b99932130", "sha1_en": "73343a4e6268360371df4d664eb7a2137215c0ae" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames.lean", + "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames_en.lean", + "sha1_fr": "d0c3ea2af84d21a83c22d954491a506201254e5c", + "sha1_en": "a8f538a6dd7f0ee7af6894d56df2ed314f5ea8f3" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/Arrow.lean", @@ -308,13 +308,6 @@ "sha1_fr": "380f3b6386bd822b7198ab71e7a12ca69d8e85a1", "sha1_en": "58f700de90c114c8cf13e6b384640424d1329856" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage.lean", - "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage_en.lean", - "sha1_fr": "9308304c1a7a5b20e4743252ce726321af7e9ddb", - "sha1_en": "18c6ef1eb01cfcfdc17592ad850a8ac87ce5756d" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage/Definitions.lean", @@ -350,6 +343,13 @@ "sha1_fr": "9b6ce09fcba1ce536b19e89a79cf165079596b27", "sha1_en": "388015fdda7085edad0d1c7ba506f5f3cebd5ff4" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage.lean", + "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage_en.lean", + "sha1_fr": "9308304c1a7a5b20e4743252ce726321af7e9ddb", + "sha1_en": "18c6ef1eb01cfcfdc17592ad850a8ac87ce5756d" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs/Basic.lean", @@ -399,13 +399,6 @@ "sha1_fr": "1e70ca7d3a84fa9f664e50e2cc882adfeea30882", "sha1_en": "a527c739efb3ebb1dfa0c22a08b7f1ef1724c710" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian.lean", - "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian_en.lean", - "sha1_fr": "88bec2fd61b1e2dc412f8e9105c21541604c596c", - "sha1_en": "fc230d4fa14d5a9f67ab1fdda163ea29f14a05d4" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Auction.lean", @@ -441,13 +434,6 @@ "sha1_fr": "8ca6f9c897d8b66b24d7b90ad746ba4c221885d1", "sha1_en": "1355c4a887fb2e55139cf7eb107c74c842f68652" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Information.lean", - "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Information_en.lean", - "sha1_fr": "ba86fb557a683a645f433cee7c461fd6a29801d1", - "sha1_en": "9c3d0eb0f7be6f3e0001470a8c62907475ac3b47" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/InformationNoisy.lean", @@ -455,6 +441,13 @@ "sha1_fr": "51a5e7dab911ec5c6be563b3faf93f412f095fa1", "sha1_en": "e1a0c54714072978353c8395c4185ade7302d2e7" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Information.lean", + "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Information_en.lean", + "sha1_fr": "ba86fb557a683a645f433cee7c461fd6a29801d1", + "sha1_en": "9c3d0eb0f7be6f3e0001470a8c62907475ac3b47" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Max.lean", @@ -499,10 +492,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax.lean", - "en": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax_en.lean", - "sha1_fr": "aab261636a76e5ffbba601bdc4d2ea751fa0c3ed", - "sha1_en": "bb021499128fab24bd5993962e57a391d56f033b" + "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian.lean", + "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian_en.lean", + "sha1_fr": "88bec2fd61b1e2dc412f8e9105c21541604c596c", + "sha1_en": "fc230d4fa14d5a9f67ab1fdda163ea29f14a05d4" }, { "kind": "lean", @@ -525,6 +518,13 @@ "sha1_fr": "703f3c9a5f30b0b15c2cad522a62b075fc99c022", "sha1_en": "095b602279907a3fd53d13782bdf473ec23b087a" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax.lean", + "en": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax_en.lean", + "sha1_fr": "aab261636a76e5ffbba601bdc4d2ea751fa0c3ed", + "sha1_en": "bb021499128fab24bd5993962e57a391d56f033b" + }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/GenAI/CaseStudies/Fort-Boyard/fort-boyard-python.ipynb", @@ -620,13 +620,6 @@ "sha1_fr": "d34a7ecca4dc1a2e4170fe96022cf3d06bf6df61", "sha1_en": "bf57dd43fa14a66428cd163f8c074bcb25bf1bc8" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/Grokking.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/Grokking_en.lean", - "sha1_fr": "03d9e84f12881f4d513cecdf00fa2381d41c5902", - "sha1_en": "60cb605d140488713640ce61f8965f2420cdae0c" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/GrokkingLemmas.lean", @@ -634,6 +627,13 @@ "sha1_fr": "cb4a74514bafe618f4daa754e6efcd15c5b201e6", "sha1_en": "013cf22dc76817632107f362e9066ddb2b7bde00" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/Grokking.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/Grokking_en.lean", + "sha1_fr": "03d9e84f12881f4d513cecdf00fa2381d41c5902", + "sha1_en": "60cb605d140488713640ce61f8965f2420cdae0c" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/InfoBits.lean", @@ -655,13 +655,6 @@ "sha1_fr": "f5d3cb94c9a910a5ce5e3fc78df24d7ba39e2c45", "sha1_en": "590bfa429c7d1eb9b74f3f00a7981bc214913f88" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow_en.lean", - "sha1_fr": "9150faea9d36a99b5369d76b3bc5b79083529089", - "sha1_en": "ea229ad8a5961d3a9969cfd067989f1116990c0d" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow/Plain.lean", @@ -676,6 +669,13 @@ "sha1_fr": "51f0d2338dcef3d270d0f00856e7395d96783b92", "sha1_en": "c21944299187bae2163e59c0a142be27a1dcec5c" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow_en.lean", + "sha1_fr": "9150faea9d36a99b5369d76b3bc5b79083529089", + "sha1_en": "ea229ad8a5961d3a9969cfd067989f1116990c0d" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/MathUniverse.lean", @@ -690,13 +690,6 @@ "sha1_fr": "86ac10693bc0ce5d4c9e062848c459d2816975be", "sha1_en": "0a915c6f6538234779cebef25b23f0a470f465d5" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning_en.lean", - "sha1_fr": "419e69e5a4d0150c994c055bc37e6ba381c63c68", - "sha1_en": "1766c34c452f1a2efa4e6a445b7735bc02c46846" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Agnostic.lean", @@ -753,13 +746,6 @@ "sha1_fr": "1d7981a3f8bfeafc3ce3577835e7c4b04c48520e", "sha1_en": "bc8bf689ba3b3b885f846ecc2708cd90497a1f4f" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Sample.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Sample_en.lean", - "sha1_fr": "df970c5985990cdcbd5834e425ed9ed54fc52e8c", - "sha1_en": "ef69ee6a29a30de6b4c03b24578f25b39d9ab949" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/SampleExpect.lean", @@ -767,6 +753,13 @@ "sha1_fr": "89102d9484eb888b6b2ab1dfeef193a9abe986c3", "sha1_en": "8737affc58f842b15dc61bdce73197696fb1862f" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Sample.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Sample_en.lean", + "sha1_fr": "df970c5985990cdcbd5834e425ed9ed54fc52e8c", + "sha1_en": "ef69ee6a29a30de6b4c03b24578f25b39d9ab949" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/UniformConcentration.lean", @@ -783,10 +776,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron_en.lean", - "sha1_fr": "5470ccd3340aa40584b47967abb30d3fc5407d07", - "sha1_en": "facb22647feb4cd522566749a824c2925076c739" + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning_en.lean", + "sha1_fr": "419e69e5a4d0150c994c055bc37e6ba381c63c68", + "sha1_en": "1766c34c452f1a2efa4e6a445b7735bc02c46846" }, { "kind": "lean", @@ -816,6 +809,13 @@ "sha1_fr": "92ab1d8e1a5a6eaef8486c1e6feac08070691a0e", "sha1_en": "070dc3433322d3205392d94c9b728a30d5bf6679" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron_en.lean", + "sha1_fr": "5470ccd3340aa40584b47967abb30d3fc5407d07", + "sha1_en": "facb22647feb4cd522566749a824c2925076c739" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/Renaming.lean", @@ -823,13 +823,6 @@ "sha1_fr": "e9c24cb0462731920660e23cdb6c7d4508e49f91", "sha1_en": "b98d3b976be2d4704e50fe5d2af4fd1e39b9d910" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation.lean", - "en": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation_en.lean", - "sha1_fr": "df93fbb84e49185d915e70b2c9db679b14c0371e", - "sha1_en": "3c1666f56ff1f7c86d5d2af34cb77ed1890a3d55" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation/Basic.lean", @@ -874,10 +867,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence.lean", - "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence_en.lean", - "sha1_fr": "f513f7528588118bf19fcc87897bfb4e1b7bf878", - "sha1_en": "487034426d2fdb4e8d89cc8093c870e7edd9313e" + "fr": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation.lean", + "en": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation_en.lean", + "sha1_fr": "df93fbb84e49185d915e70b2c9db679b14c0371e", + "sha1_en": "3c1666f56ff1f7c86d5d2af34cb77ed1890a3d55" }, { "kind": "lean", @@ -909,10 +902,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins.lean", - "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins_en.lean", - "sha1_fr": "074b54c2eade6f963508e672b4c6d2c8d489bc8e", - "sha1_en": "27d3ae942c1fc7965c5c305d74f2955a48c83ff3" + "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence.lean", + "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence_en.lean", + "sha1_fr": "f513f7528588118bf19fcc87897bfb4e1b7bf878", + "sha1_en": "487034426d2fdb4e8d89cc8093c870e7edd9313e" }, { "kind": "lean", @@ -935,6 +928,13 @@ "sha1_fr": "10b92f4fed904e99a7b2594ecea7e7905d77fbda", "sha1_en": "25858906efc56c02273875aee2f67bcef1a7546d" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins.lean", + "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins_en.lean", + "sha1_fr": "074b54c2eade6f963508e672b4c6d2c8d489bc8e", + "sha1_en": "27d3ae942c1fc7965c5c305d74f2955a48c83ff3" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/README.md", @@ -942,13 +942,6 @@ "sha1_fr": "1fb9645c3a1bc5ad54bc5bb182c61599a3f32777", "sha1_en": "2a6c3112b300c7a89a3799815f06c7c0edfa683c" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility.lean", - "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility_en.lean", - "sha1_fr": "54d8ce7649cde1ea6b437ab3b2d81b754f91900b", - "sha1_en": "3713b683e62d961519a5d3a9fa4b97a672111ca0" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility/Axioms.lean", @@ -970,6 +963,13 @@ "sha1_fr": "1f18b10f2587900a8d107d345c306c2d331bf3cc", "sha1_en": "957506b839753778e100c994e32884bba83d3934" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility.lean", + "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility_en.lean", + "sha1_fr": "54d8ce7649cde1ea6b437ab3b2d81b754f91900b", + "sha1_en": "3713b683e62d961519a5d3a9fa4b97a672111ca0" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/Probas/Infer/scripts/README.md", @@ -1566,13 +1566,6 @@ "sha1_fr": "5cff8cb071aef62c733621a8662ef446b8a34f65", "sha1_en": "5a794d673285902b0c1ae1c458b4d22995b214db" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy_en.lean", - "sha1_fr": "b0911f8d95cd87442ffe4936ebb61baf83d699f8", - "sha1_en": "907f237c093e3feba0f541882132dae24f352c43" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Basic.lean", @@ -1580,13 +1573,6 @@ "sha1_fr": "fc4d4337e19ea5cfb83f196092c448bdd33ae610", "sha1_en": "486fae4e00f7a615d99fa1ec348706547ed00a00" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos_en.lean", - "sha1_fr": "f9b08709747f25e1152d4a9369b5a0226a725d9e", - "sha1_en": "f285dbff792ce6c07d778acc0ede4a5e5eacc2bb" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Approximation.lean", @@ -1636,13 +1622,6 @@ "sha1_fr": "514fc97841651dda9293d7a8ec8a4e877934a3f1", "sha1_en": "d833d169dfa9e6382763a6b51ba6067feca3ff86" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Lift.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Lift_en.lean", - "sha1_fr": "1191e752b22966923866ba4a261d6ed91e0feb0b", - "sha1_en": "26c28e12325ddafea17e22bdbb068c70b9730425" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/LiftSplit.lean", @@ -1650,6 +1629,13 @@ "sha1_fr": "5705da94924d6bc79287df6a795b2b10dc48bdb0", "sha1_en": "db61bd1091c2588d48e8d7f6a378c63599d7a3e8" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Lift.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Lift_en.lean", + "sha1_fr": "1191e752b22966923866ba4a261d6ed91e0feb0b", + "sha1_en": "26c28e12325ddafea17e22bdbb068c70b9730425" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/MeanSplit.lean", @@ -1692,13 +1678,6 @@ "sha1_fr": "712f4cf73bfbd6180de99c3afc043c4654247e04", "sha1_en": "083f8e94e5d6723630c76ef3661891b77e485f15" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split_en.lean", - "sha1_fr": "94c823ecb8d4e407cc2c437acd82be4bae3db024", - "sha1_en": "eba63e71a122adf40d953e4a424c04c7dc0fd93f" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/SplitBit.lean", @@ -1713,6 +1692,13 @@ "sha1_fr": "57673a25be6601ea7b47ed129006b21d0fc8d388", "sha1_en": "4bb59b9f42754e31f8ad2f08b3c9dafcd6ce054b" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split_en.lean", + "sha1_fr": "94c823ecb8d4e407cc2c437acd82be4bae3db024", + "sha1_en": "eba63e71a122adf40d953e4a424c04c7dc0fd93f" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Tent.lean", @@ -1729,10 +1715,17 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/search_lean/Astar.lean", - "en": "MyIA.AI.Notebooks/Search/search_lean/Astar_en.lean", - "sha1_fr": "ee3310a867a93286f44aadbaf2a3db7491bda4e8", - "sha1_en": "726dfedcb1182af83f792a922a0f03765efea109" + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos_en.lean", + "sha1_fr": "f9b08709747f25e1152d4a9369b5a0226a725d9e", + "sha1_en": "f285dbff792ce6c07d778acc0ede4a5e5eacc2bb" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy_en.lean", + "sha1_fr": "b0911f8d95cd87442ffe4936ebb61baf83d699f8", + "sha1_en": "907f237c093e3feba0f541882132dae24f352c43" }, { "kind": "lean", @@ -1764,17 +1757,17 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/search_lean/Szpiro.lean", - "en": "MyIA.AI.Notebooks/Search/search_lean/Szpiro_en.lean", - "sha1_fr": "d090cd6bb5296396ca0d2077e319d6dc8e3435ae", - "sha1_en": "5648ca26b495cc6bba17849b689c6224af99a243" - }, + "fr": "MyIA.AI.Notebooks/Search/search_lean/Astar.lean", + "en": "MyIA.AI.Notebooks/Search/search_lean/Astar_en.lean", + "sha1_fr": "ee3310a867a93286f44aadbaf2a3db7491bda4e8", + "sha1_en": "726dfedcb1182af83f792a922a0f03765efea109" + }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku.lean", - "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku_en.lean", - "sha1_fr": "9468f4895b4b56fa614159090aa88b478cd0dfaf", - "sha1_en": "30f1d304d5d9e909ac5c325907da5ab4cbc915b5" + "fr": "MyIA.AI.Notebooks/Search/search_lean/Szpiro.lean", + "en": "MyIA.AI.Notebooks/Search/search_lean/Szpiro_en.lean", + "sha1_fr": "d090cd6bb5296396ca0d2077e319d6dc8e3435ae", + "sha1_en": "5648ca26b495cc6bba17849b689c6224af99a243" }, { "kind": "lean", @@ -1797,13 +1790,20 @@ "sha1_fr": "d05e2d2083162a70eb8605c0517c0e6c4e9cd2d7", "sha1_en": "a74804e49451fac46a65610c0d9c7816b1f5bb46" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku.lean", + "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku_en.lean", + "sha1_fr": "9468f4895b4b56fa614159090aa88b478cd0dfaf", + "sha1_en": "30f1d304d5d9e909ac5c325907da5ab4cbc915b5" + }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-06-Sensibilite-2026-Lean-Python.ipynb", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-06-Sensibilite-2026-Lean-Python_en.ipynb", "sha1_fr": "73f58999db03f9649a7c02db16420528bf1c90e6", "sha1_en": "efdff3c1f2f8c65cf030b7f712d1773bf609781f", - "sources_sha1_fr": "56f7459d2a8a55c8c1a34edb62e72de2a5dd86ed", + "sources_sha1_fr": "0c96ec4bc36464c163bd3f4dfaaedcab8e07e14b", "sources_sha1_en": "14c817e0a9189065340fe6760bde24bfa44fe215" }, { @@ -1842,13 +1842,6 @@ "sources_sha1_fr": "b3d8bb21aeca61a8aa72e6b311a44c3bcdd24a63", "sources_sha1_en": "8233925e59b2b7fd0c5d1cac3df53dcb47724983" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots_en.lean", - "sha1_fr": "56efe973e50920e313be4a5bd5ce52a848990a4e", - "sha1_en": "ed3254f2ac87b68e30f18a8c41387abc402bad4c" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/AlexanderTrivial.lean", @@ -1877,13 +1870,6 @@ "sha1_fr": "b7c37c6f5d0ba1c6faa447a114415354facaf6e3", "sha1_en": "6614dd17bc034a8d9fd87da4fc9557805cc8ecd1" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway_en.lean", - "sha1_fr": "e2a297515d6ec896e95a9b9d03fe9509b576fff2", - "sha1_en": "b2cbdebcaa5084eb55f38dca92860a6411a398fb" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ConwayKTMutation.lean", @@ -1905,6 +1891,13 @@ "sha1_fr": "30fbf75132f96f192bd323eb72e6d2a93e8d4804", "sha1_en": "d287b62b17fd1f2990b006afe2692b9c3dd878b8" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway_en.lean", + "sha1_fr": "e2a297515d6ec896e95a9b9d03fe9509b576fff2", + "sha1_en": "b2cbdebcaa5084eb55f38dca92860a6411a398fb" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/FigureEight.lean", @@ -1954,13 +1947,6 @@ "sha1_fr": "66830765c73d07445dec534549220f74ae51eb50", "sha1_en": "c75e6a31b3b58842fdd366edf4cfa5494dccd0da" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister_en.lean", - "sha1_fr": "298e292e593e0592f42b6a47e395b9da2dfa29b4", - "sha1_en": "b82ecdcc444e9420b8c8c386b6199c26722a24ae" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ReidemeisterAny.lean", @@ -1989,6 +1975,13 @@ "sha1_fr": "8bcd6ee37b603a0637fc8efc71337f761231b71e", "sha1_en": "4bb6010a990802032c8767e9af3c06f36eb39d01" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister_en.lean", + "sha1_fr": "298e292e593e0592f42b6a47e395b9da2dfa29b4", + "sha1_en": "b82ecdcc444e9420b8c8c386b6199c26722a24ae" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Slice.lean", @@ -2003,6 +1996,13 @@ "sha1_fr": "bef33f49fb4aca46b71d90004109b8ff27578864", "sha1_en": "77505c9eeb26fcf0cc63ec702e1713c42654feaa" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots_en.lean", + "sha1_fr": "56efe973e50920e313be4a5bd5ce52a848990a4e", + "sha1_en": "ed3254f2ac87b68e30f18a8c41387abc402bad4c" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/README.md", @@ -2010,13 +2010,6 @@ "sha1_fr": "6ec8b965845567d770bcf96a88d3182b8e8ff89d", "sha1_en": "c9b43cc6aa2495470a502c0bc93a213ed77c39bd" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100_en.lean", - "sha1_fr": "2ac6a6c6de1dd52d912f039c7bc08e0edc7c5e80", - "sha1_en": "3f8797379ceb620d6907a1ca9bbb29d483dd23b7" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100/CaracteresComputes.lean", @@ -2054,10 +2047,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration_en.lean", - "sha1_fr": "3eb2216f03dc4b6b99169a07fcc045e0e065da54", - "sha1_en": "07cda8c61197b457adc11f228c603a136911180b" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100_en.lean", + "sha1_fr": "2ac6a6c6de1dd52d912f039c7bc08e0edc7c5e80", + "sha1_en": "3f8797379ceb620d6907a1ca9bbb29d483dd23b7" }, { "kind": "lean", @@ -2087,6 +2080,13 @@ "sha1_fr": "c2fde535671375a651fec1747b0f0d07a15568f3", "sha1_en": "dfcaab8c228db5bca81e229e60248a2f8c6fa05c" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration_en.lean", + "sha1_fr": "3eb2216f03dc4b6b99169a07fcc045e0e065da54", + "sha1_en": "07cda8c61197b457adc11f228c603a136911180b" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/README.md", @@ -2094,13 +2094,6 @@ "sha1_fr": "9fc3aa679066b6af4025316edfcb2164385528c9", "sha1_en": "d9c6fafa49d0689a3736d76c3dd6e310451bae95" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway_en.lean", - "sha1_fr": "9c12ee83051305727010f47895b2bdff20c65464", - "sha1_en": "2659e242605b7d5e0c82460f05932d607d4056b6" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Angel.lean", @@ -2108,13 +2101,6 @@ "sha1_fr": "21cbac41258a0563cd1caaa7644ac28968857be4", "sha1_en": "0767d5818bbaccf25bdef3f8fdcfeae75e1f8c6e" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH_en.lean", - "sha1_fr": "252b66012c812d452b9ef522d67679d5ff5efd7d", - "sha1_en": "e0f232fab3e90df21813d20550648e6d344d0a35" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSHFreeWill.lean", @@ -2143,6 +2129,13 @@ "sha1_fr": "f73e1b2a3e125f7000b758d17e1a09a3d2815753", "sha1_en": "1bdfff89cfef12431f2dc304e102b708953b289e" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH_en.lean", + "sha1_fr": "252b66012c812d452b9ef522d67679d5ff5efd7d", + "sha1_en": "e0f232fab3e90df21813d20550648e6d344d0a35" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CollatzLike.lean", @@ -2150,13 +2143,6 @@ "sha1_fr": "4f210a671e3941fa2f1d2b13bccea6daa54f610a", "sha1_en": "d2bbf17cb6967ca6dffec0de693920392ec1c39c" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Doomsday.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Doomsday_en.lean", - "sha1_fr": "fa5b9a77adb0c8570c5306dc3f45201830d1f0d0", - "sha1_en": "b827cc215121d83f7406f6a14456759a57f39b29" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/DoomsdayLemmas.lean", @@ -2166,10 +2152,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Fractran.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Fractran_en.lean", - "sha1_fr": "7a371d3ee9748a249de3559bde2b74a75fda9329", - "sha1_en": "3391f13a8722cac6c3e6f914f6ef6b36f34a089c" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Doomsday.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Doomsday_en.lean", + "sha1_fr": "fa5b9a77adb0c8570c5306dc3f45201830d1f0d0", + "sha1_en": "b827cc215121d83f7406f6a14456759a57f39b29" }, { "kind": "lean", @@ -2178,6 +2164,13 @@ "sha1_fr": "24af4d1e286f4bd69581a0ca1044a53234429e89", "sha1_en": "0a93de6ac46d7fdf2eb284f0c770031f69c9e8dc" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Fractran.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Fractran_en.lean", + "sha1_fr": "7a371d3ee9748a249de3559bde2b74a75fda9329", + "sha1_en": "3391f13a8722cac6c3e6f914f6ef6b36f34a089c" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/FreeWillTheorem.lean", @@ -2194,10 +2187,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life_en.lean", - "sha1_fr": "6dce7b85589953e4c8aeb37d92b883150bede83a", - "sha1_en": "a1dbaabb2413d107d954b3e4940fb0fff60f319f" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2_en.lean", + "sha1_fr": "5a2a822a7a48480905e5b60ede565f0d0e896062", + "sha1_en": "bdf1f69e80996742bbec5f11c74df374fe7814e3" }, { "kind": "lean", @@ -2206,13 +2199,6 @@ "sha1_fr": "32976fc80c9b54c3297cd3929082c0a7b264df82", "sha1_en": "31f19a431afba3c1c0a8da4f85eb6c5fb16f7bf3" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2_en.lean", - "sha1_fr": "5a2a822a7a48480905e5b60ede565f0d0e896062", - "sha1_en": "bdf1f69e80996742bbec5f11c74df374fe7814e3" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Computation.lean", @@ -2241,13 +2227,6 @@ "sha1_fr": "7c6b1e80a88c163a7da6b8b1df1de3b61985bcaf", "sha1_en": "68c557be3faa6718de8fcf625aa19bda55663131" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife_en.lean", - "sha1_fr": "82ce6e8ad410ef1d11e3d7691c696e570f1ff451", - "sha1_en": "6118561ae855a8b432ac61c19ee93a45525dfeb0" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/HashlifeDecideMemo.lean", @@ -2276,6 +2255,13 @@ "sha1_fr": "84d969a85f8da7757b33ca809ea860bb3a23dbb4", "sha1_en": "634839359a3f59e58acca4045293704ba578a1bc" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife_en.lean", + "sha1_fr": "82ce6e8ad410ef1d11e3d7691c696e570f1ff451", + "sha1_en": "6118561ae855a8b432ac61c19ee93a45525dfeb0" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/LightCone.lean", @@ -2355,10 +2341,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay_en.lean", - "sha1_fr": "cc773bf5f159d91d4c4734eaf2d5bb40cafbd32d", - "sha1_en": "d3a900a35c896f5aaa9d3257dbec264538fbdcdf" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life_en.lean", + "sha1_fr": "6dce7b85589953e4c8aeb37d92b883150bede83a", + "sha1_en": "a1dbaabb2413d107d954b3e4940fb0fff60f319f" }, { "kind": "lean", @@ -2367,6 +2353,13 @@ "sha1_fr": "5006be39d57eb1585f23770535774fa613f054d5", "sha1_en": "4628351809ef7d16cef9d7152c0cdcee3d784d4d" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay_en.lean", + "sha1_fr": "cc773bf5f159d91d4c4734eaf2d5bb40cafbd32d", + "sha1_en": "d3a900a35c896f5aaa9d3257dbec264538fbdcdf" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/MathlibMap.lean", @@ -2381,6 +2374,13 @@ "sha1_fr": "a8125a072263786184dc87f1aab221b9afb0795f", "sha1_en": "57876f2da71fdd8b8bc5c8ed174b1d41c6dceb04" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway_en.lean", + "sha1_fr": "9c12ee83051305727010f47895b2bdff20c65464", + "sha1_en": "2659e242605b7d5e0c82460f05932d607d4056b6" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/README.md", @@ -2465,13 +2465,6 @@ "sha1_fr": "d02345eeccd06142c27307b2852914e1f6471bdb", "sha1_en": "42758c75ad8a560b99c11bb8d9a5de47e621d570" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups_en.lean", - "sha1_fr": "d78a1aed2ab5d5ffcd57b934888d6273eb5432a4", - "sha1_en": "f122ba1407bbfd04227f2a829afe809dfb6510d1" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups/Additive.lean", @@ -2502,15 +2495,22 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/AdicCompletionLocalRing.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/AdicCompletionLocalRing_en.lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups_en.lean", + "sha1_fr": "d78a1aed2ab5d5ffcd57b934888d6273eb5432a4", + "sha1_en": "f122ba1407bbfd04227f2a829afe809dfb6510d1" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing_en.lean", "sha1_fr": "03071c5b55b415831b71fb2c4a3ebf933edae1d7", "sha1_en": "a29097efa84ced220594d21b948bc3897705cea4" }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/LowerRamificationGroup.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/LowerRamificationGroup_en.lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup_en.lean", "sha1_fr": "0d94d9f65927d087b82ef1f8033670061c54e738", "sha1_en": "7b7f80394bfc869c41560b49e48b9587bccb0d13" }, @@ -2619,13 +2619,6 @@ "sha1_fr": "c6d5b7b96a4169419582d98b8b354f96b496d729", "sha1_en": "019e7ac3f8fc147cf5610baf7be58ddfb7b41f17" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreserving.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreserving_en.lean", - "sha1_fr": "e540fb4e33d2d32edb14f13ea5becb8d1c73cd2b", - "sha1_en": "0d2e73ced96b781ab0cd3057ac16361146f48750" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreservingLaws.lean", @@ -2633,6 +2626,13 @@ "sha1_fr": "ef525dae197c826be965992e675d323a57e77ac3", "sha1_en": "27a09e8b5dc31df94734267d370fcea1f367915c" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreserving.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreserving_en.lean", + "sha1_fr": "e540fb4e33d2d32edb14f13ea5becb8d1c73cd2b", + "sha1_en": "0d2e73ced96b781ab0cd3057ac16361146f48750" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverageArrow.lean", @@ -2773,13 +2773,6 @@ "sha1_fr": "7e7d2f3119c76f091a4739bb4692428428eec1db", "sha1_en": "3de19d113ca61ba269368cc53202cb5ec9863f84" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque_en.lean", - "sha1_fr": "6b1b8cfa012cb705ed675353305c104462d5215f", - "sha1_en": "83a6aef1c83fc96b3c4094e409de12b9cac2c0a1" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/FlasqueAcyclic.lean", @@ -2815,6 +2808,13 @@ "sha1_fr": "3e563ecc42a6b92d1ffee5d0b98a8ce6ab4ce763", "sha1_en": "98fb4aaa852bef33c78a8e16e8e2392050ff4f21" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque_en.lean", + "sha1_fr": "6b1b8cfa012cb705ed675353305c104462d5215f", + "sha1_en": "83a6aef1c83fc96b3c4094e409de12b9cac2c0a1" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Fppf.lean", @@ -2822,13 +2822,6 @@ "sha1_fr": "6e94497f0c76c97b0f144153df89418f2485a0d9", "sha1_en": "3faaef07ac2a1be5cfa15dcf24d1192cd50ce48e" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement_en.lean", - "sha1_fr": "2069a547b0be2d62b047360dda19f560babd033f", - "sha1_en": "1ba26fcf5741e5c055f5dbb9aa98f843cafc1a18" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/GodementAcyclicity.lean", @@ -2878,6 +2871,13 @@ "sha1_fr": "c73ea36f4c11efa42e361297e963f0dc02a22f39", "sha1_en": "edd08615556930a9500c7e88802d127982a63cb2" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement_en.lean", + "sha1_fr": "2069a547b0be2d62b047360dda19f560babd033f", + "sha1_en": "1ba26fcf5741e5c055f5dbb9aa98f843cafc1a18" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/KanExtensions.lean", @@ -2962,13 +2962,6 @@ "sha1_fr": "5b9242eef173b512b8924ff0d2557ee862c45c8c", "sha1_en": "356435a987de1505a4f6431d8519ad2aa43d0df5" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctor.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctor_en.lean", - "sha1_fr": "1e5adaf1c59443f00be74d13982cffa1bc4bc86d", - "sha1_en": "e2df3c7234e7c6f0be8c2e99bbfb874245998a18" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctorLaws.lean", @@ -2976,6 +2969,13 @@ "sha1_fr": "0cae31aad039951aeef768e814b494e13467a22b", "sha1_en": "15166bf13021b9415d3da9e09c850578702d0258" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctor.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctor_en.lean", + "sha1_fr": "1e5adaf1c59443f00be74d13982cffa1bc4bc86d", + "sha1_en": "e2df3c7234e7c6f0be8c2e99bbfb874245998a18" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SchemesTour.lean", @@ -3018,13 +3018,6 @@ "sha1_fr": "55c959e7797df856cbbd58ebbe3d7c11eefdf8af", "sha1_en": "55ce6410ef6a6f6e61df47349b1b0aee2a8ce383" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition_en.lean", - "sha1_fr": "bbf5bae9e8e8edd53659948c83073cf236024322", - "sha1_en": "8e30fe49084b2907889440d9ab021559286df4c1" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafConditionCharacterization.lean", @@ -3039,6 +3032,13 @@ "sha1_fr": "65e6925b216a9927d04cdcb08052ee0e682945d1", "sha1_en": "a11605fe3126bf0fd873fafea3910a18ec73eaf5" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition_en.lean", + "sha1_fr": "bbf5bae9e8e8edd53659948c83073cf236024322", + "sha1_en": "8e30fe49084b2907889440d9ab021559286df4c1" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafHom.lean", @@ -3102,13 +3102,6 @@ "sha1_fr": "858e73be11047400ed4262ba553a10d7b8634c4b", "sha1_en": "f4e919b2d6afcb1a0c4cdc24e09d6e6125c9bae6" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces_en.lean", - "sha1_fr": "0ce7fe7b6aeccfeb929b978deae3334b7884f930", - "sha1_en": "10c939a705d38d582cb91a3461e01085b9bbf541" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SpacesMathlib.lean", @@ -3123,6 +3116,13 @@ "sha1_fr": "5182e6b665bcaacfe700ac910b005541a6b58165", "sha1_en": "9654456bbc5c4ef990d5e3d6a746a3ca274781e6" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces_en.lean", + "sha1_fr": "0ce7fe7b6aeccfeb929b978deae3334b7884f930", + "sha1_en": "10c939a705d38d582cb91a3461e01085b9bbf541" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/StalkCharacterization.lean", @@ -3200,13 +3200,6 @@ "sha1_fr": "f6d36a8d6c9beb53dcf50aea410c77aff2e06673", "sha1_en": "7a48924f5dede27ef30db11e96a4b8b643dd3f47" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke_en.lean", - "sha1_fr": "1aee3226602f775d45bca891e62f22d2ff762a87", - "sha1_en": "a964da3e1f5e310cfc49d0c65acfbe2f3e0c45e6" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke/DeltaTau.lean", @@ -3256,6 +3249,13 @@ "sha1_fr": "9239b5d70e625f1744d642348dc1ae450b64459a", "sha1_en": "1cc99d9aaa0d1da2d804bb55c82f7b9bd3052ee9" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke_en.lean", + "sha1_fr": "1aee3226602f775d45bca891e62f22d2ff762a87", + "sha1_en": "a964da3e1f5e310cfc49d0c65acfbe2f3e0c45e6" + }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.md", @@ -3326,13 +3326,6 @@ "sha1_fr": "e5c29e6e1b81853f2ee52411f89f844ec1fd6469", "sha1_en": "5b9dfc5470c010498674c8ae84eb1df2127e0475" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity_en.lean", - "sha1_fr": "b80687cd7008699f3d51c9935aa93644fb6236f2", - "sha1_en": "42d17bf07b5d893552af729ca9ab96f57db8c63f" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity/Fourier.lean", @@ -3396,6 +3389,13 @@ "sha1_fr": "0b25f2f26ca5ee5cd41d597a9f3ecee4713642c5", "sha1_en": "dae688cb198767a699826c4214ccaf4c51e6722e" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity_en.lean", + "sha1_fr": "b80687cd7008699f3d51c9935aa93644fb6236f2", + "sha1_en": "42d17bf07b5d893552af729ca9ab96f57db8c63f" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Planners/planning_lean/Planning/Admissibility.lean", @@ -3417,13 +3417,6 @@ "sha1_fr": "bde24b049398808a9dede411907a007e6cc562e9", "sha1_en": "afbd0588cd306ebd53d7a7d924d2c3285e21a007" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20_en.lean", - "sha1_fr": "93d6359dc13b212c0e31346bd1dccbdbdde30f9f", - "sha1_en": "f767595d3244c88345da016e7ae72c682f8fb295" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20/Invariant.lean", @@ -3445,6 +3438,13 @@ "sha1_fr": "1aadd8d3cef97866d579d8c887b86074cff43cce", "sha1_en": "3c49f097f9eba945da3cba7e4b54427e172fccff" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20_en.lean", + "sha1_fr": "93d6359dc13b212c0e31346bd1dccbdbdde30f9f", + "sha1_en": "f767595d3244c88345da016e7ae72c682f8fb295" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Tweety/argumentation_lean/Argumentation/Basic.lean", From 22d61822ede0e8ed225802900f0ecd7df2cf402d Mon Sep 17 00:00:00 2001 From: jsboige Date: Wed, 28 Oct 2026 16:32:29 +0100 Subject: [PATCH 4/6] Fix(17545): deux finitions mecaniques du rename GALOIS -- lien relatif et compteur en prose MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - GALOIS/galois_lean/README.md : le lien vers Lean-15-Grothendieck-Tribute pointait un niveau trop haut apres le deplacement du lake dans GALOIS/ ; il resout desormais vers Lean/Lean-15-Grothendieck-Tribute.ipynb (check-links : 1 lien casse en regression). - LEAN_INVENTORY.md : le compteur « 1 notebook » est retire, le predicat est conserve (GALOIS/GALOIS-01-Probleme-Inverse-M23-Python). Les donnees quantitatives sont tenues par le CI, pas par la prose (#9377) ; l'edition du chemin re-exposait la ligne au garde prose-counts. Co-Authored-By: Claude Sonnet 5.5 --- MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md | 2 +- MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md index 335218b2206..9d0f3682a96 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/README.md @@ -38,6 +38,6 @@ Per `sota-not-workaround` : refaire une échelle de petits groupes à côté d'u ## Voir aussi -- [Lean-15-Grothendieck-Tribute](../Lean-15-Grothendieck-Tribute.ipynb) — hommage comparable (prouve ce qu'il annonce) +- [Lean-15-Grothendieck-Tribute](../../Lean-15-Grothendieck-Tribute.ipynb) — hommage comparable (prouve ce qu'il annonce) - `grothendieck_lean` — angle SGA1 (π₁ étale, existence de Riemann) pour le chaînon-pont vers la réalisation galoisienne - EPIC i18n #4980 : siblings `_en` à venir diff --git a/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md b/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md index e447c8f7317..e47b86b3f70 100644 --- a/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md +++ b/MyIA.AI.Notebooks/SymbolicAI/Lean/LEAN_INVENTORY.md @@ -217,7 +217,7 @@ pour l'identification `23T5 = M23` que la couche proof-assistant ne reproduit pa tactique, 0 `native_decide`, 0 `axiom` déclaré. `Sporadic.card_M23` et `Sporadic.simple_M23` prouvés par chaîne de stabilisateurs à certificats (Schreier–Sims matérialisé). -- **Notebook câblé** : 1 notebook (GALOIS/GALOIS-01-Probleme-Inverse-M23-Python). +- **Notebook câblé** : GALOIS/GALOIS-01-Probleme-Inverse-M23-Python. - **Suivi** : préprint #2608.08538 (cf. `GALOIS/galois_lean/README.md`). - **i18n** : EN-sibling à venir (EPIC #4980). - **Caveat build** : `lakefile.toml` utilise la forme **bare** + agrégateur racine (la From d11d2fd3796809f5c413cee5c1656c471cd433e1 Mon Sep 17 00:00:00 2001 From: jsboige Date: Fri, 30 Oct 2026 23:11:00 +0100 Subject: [PATCH 5/6] Fix(tooling,#17545): live-read registry suit le renommage GALOIS-01 Le renommage de la T2 deplace le carnet et le lake sous GALOIS/. L'entree declaree dans live_read_registry.json pointait encore l'ancien chemin sur ses DEUX champs, donc l'organe la rendait NOTEBOOK_UNREADABLE et ne pouvait plus verifier la fraicheur de la cellule 21fed7ef. Verification : 18 entrees, un seul carnet introuvable et une seule source introuvable -- cette entree. check_live_read_freshness.py : rc=0, 18/18 FRESH, dont GALOIS-01-Probleme-Inverse-M23-Python.ipynb::21fed7ef -> M23Lean4Web.lean. Co-Authored-By: Claude Sonnet 5.5 --- scripts/notebook_tools/live_read_registry.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/scripts/notebook_tools/live_read_registry.json b/scripts/notebook_tools/live_read_registry.json index 582795139cc..2fd75794b58 100644 --- a/scripts/notebook_tools/live_read_registry.json +++ b/scripts/notebook_tools/live_read_registry.json @@ -75,9 +75,9 @@ "note": "Declarations reelles de FreeWillTheorem.lean (source de verite, pas une paraphrase)" }, { - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-22-Galois-Probleme-Inverse-M23.ipynb", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/GALOIS-01-Probleme-Inverse-M23-Python.ipynb", "cell_id": "21fed7ef", - "source": "MyIA.AI.Notebooks/SymbolicAI/Lean/galois_lean/Galois/M23Lean4Web.lean", + "source": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/M23Lean4Web.lean", "note": "Lecture directe du module M23Lean4Web.lean" }, { From 4b4682501e1682c044a01a0870658cb0ff3948b2 Mon Sep 17 00:00:00 2001 From: jsboige Date: Sat, 31 Oct 2026 05:16:19 +0100 Subject: [PATCH 6/6] Chore(17545): golden set regenere apres rebasage -- tri par chemin FR (#22261) restaure, l'union manuelle deshonorait l'ordre canonique Co-Authored-By: Claude Sonnet 5.5 --- translations/translation_golden_set.json | 815 ++++++++++++----------- 1 file changed, 427 insertions(+), 388 deletions(-) diff --git a/translations/translation_golden_set.json b/translations/translation_golden_set.json index d2949bfcab5..ef28fa734f6 100644 --- a/translations/translation_golden_set.json +++ b/translations/translation_golden_set.json @@ -1,5 +1,14 @@ { "pairs": [ + { + "kind": "notebook", + "fr": "MyIA.AI.Notebooks/Complexity/Complexity-03-TimeHierarchy.ipynb", + "en": "MyIA.AI.Notebooks/Complexity/Complexity-03-TimeHierarchy_en.ipynb", + "sha1_fr": "fddeefa8c6270596efcd6eeb609975cfac4a77aa", + "sha1_en": "056f86d0ec56b8c8daa0ba5470b862ab91740f0a", + "sources_sha1_fr": "40dfa4f7f7ee34f6c972f360fdbcf165413f064e", + "sources_sha1_en": "b8f09142b868246708ef37e0e040be26b6a4ee10" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Compression/Compression_lean/PrefixCodes.lean", @@ -77,6 +86,13 @@ "sha1_fr": "ac7992fff2bed1e766e8b931388abbbcdcfa92b3", "sha1_en": "fefd93374cbc7e707a8fd106ea9b6b1467057666" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment.lean", + "en": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment_en.lean", + "sha1_fr": "3d14c7b6aa900d8b55809ca7393d820977b966f2", + "sha1_en": "c0a0a88e822f8ee4b4b4525bdf8eecdcefbc8db7" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment/Counting.lean", @@ -114,10 +130,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment.lean", - "en": "MyIA.AI.Notebooks/GameTheory/assignment_lean/Assignment_en.lean", - "sha1_fr": "3d14c7b6aa900d8b55809ca7393d820977b966f2", - "sha1_en": "c0a0a88e822f8ee4b4b4525bdf8eecdcefbc8db7" + "fr": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation.lean", + "en": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation_en.lean", + "sha1_fr": "d1fb3e1e97b0f152abce5bdd7addec73abe85254", + "sha1_en": "bd9e52d461f773aa797c7c9063740b647b0a1534" }, { "kind": "lean", @@ -154,13 +170,6 @@ "sha1_fr": "38d5cd424c94668731b554339af3778682c30f64", "sha1_en": "f8910e2ce4972ccf65178d42ea81e1873b545186" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation.lean", - "en": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/AsymmetricInformation_en.lean", - "sha1_fr": "d1fb3e1e97b0f152abce5bdd7addec73abe85254", - "sha1_en": "bd9e52d461f773aa797c7c9063740b647b0a1534" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/GameTheory/asymmetric_information_lean/README.md", @@ -224,6 +233,13 @@ "sha1_fr": "0c159feb83d0844dd1afa10c83319c1c921b560e", "sha1_en": "7ae0fede4b9a3c1d34cbe04ede50fd1d067b54bf" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames.lean", + "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames_en.lean", + "sha1_fr": "d0c3ea2af84d21a83c22d954491a506201254e5c", + "sha1_en": "a8f538a6dd7f0ee7af6894d56df2ed314f5ea8f3" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames/Discounting.lean", @@ -252,13 +268,6 @@ "sha1_fr": "0cb39d2b32feaaa5b5b0eb122f168a5b99932130", "sha1_en": "73343a4e6268360371df4d664eb7a2137215c0ae" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames.lean", - "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/RepeatedGames_en.lean", - "sha1_fr": "d0c3ea2af84d21a83c22d954491a506201254e5c", - "sha1_en": "a8f538a6dd7f0ee7af6894d56df2ed314f5ea8f3" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/Arrow.lean", @@ -280,6 +289,13 @@ "sha1_fr": "da723a1bc7e2c20b1ce79021a503b1e1ad6b42da", "sha1_en": "afee720f15a39c37a42c8b9b1aab7e4259dd4876" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/Groves.lean", + "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/Groves_en.lean", + "sha1_fr": "31ffa4efc27cd9fa4b537465a7345dda2da16adc", + "sha1_en": "ee61fab11a0abecfc5e2760c455278af39813de9" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/MechanismDesign.lean", @@ -291,8 +307,8 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/Sen.lean", "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/SocialChoice/Sen_en.lean", - "sha1_fr": "33a46c9066a20f629540e8506bdbb658eb4da9e4", - "sha1_en": "82d4c9d04567a2f06ef85ff983a816998c3d372e" + "sha1_fr": "8806d5d96ee4394f62086a5ccda96ebf6334f4fb", + "sha1_en": "bc685b847d386503a5da92586126f07ca6527987" }, { "kind": "lean", @@ -308,6 +324,13 @@ "sha1_fr": "380f3b6386bd822b7198ab71e7a12ca69d8e85a1", "sha1_en": "58f700de90c114c8cf13e6b384640424d1329856" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage.lean", + "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage_en.lean", + "sha1_fr": "9308304c1a7a5b20e4743252ce726321af7e9ddb", + "sha1_en": "18c6ef1eb01cfcfdc17592ad850a8ac87ce5756d" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage/Definitions.lean", @@ -343,13 +366,6 @@ "sha1_fr": "9b6ce09fcba1ce536b19e89a79cf165079596b27", "sha1_en": "388015fdda7085edad0d1c7ba506f5f3cebd5ff4" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage.lean", - "en": "MyIA.AI.Notebooks/GameTheory/game_theory_lean/StableMarriage_en.lean", - "sha1_fr": "9308304c1a7a5b20e4743252ce726321af7e9ddb", - "sha1_en": "18c6ef1eb01cfcfdc17592ad850a8ac87ce5756d" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs/Basic.lean", @@ -396,8 +412,15 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs/SocialChoice.lean", "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs/SocialChoice_en.lean", - "sha1_fr": "1e70ca7d3a84fa9f664e50e2cc882adfeea30882", - "sha1_en": "a527c739efb3ebb1dfa0c22a08b7f1ef1724c710" + "sha1_fr": "b59b56a142643ca76445e7aa08147cbd95e510e5", + "sha1_en": "103d329e5724fba533e1dfbacccf5ad5617d6ba1" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian.lean", + "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian_en.lean", + "sha1_fr": "ceb7aea6653e26e82bd0eb091a520b4cc7f5ec2a", + "sha1_en": "1bdc3517f8ad6040f17744cacca976ec57c1522d" }, { "kind": "lean", @@ -434,13 +457,6 @@ "sha1_fr": "8ca6f9c897d8b66b24d7b90ad746ba4c221885d1", "sha1_en": "1355c4a887fb2e55139cf7eb107c74c842f68652" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/InformationNoisy.lean", - "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/InformationNoisy_en.lean", - "sha1_fr": "51a5e7dab911ec5c6be563b3faf93f412f095fa1", - "sha1_en": "e1a0c54714072978353c8395c4185ade7302d2e7" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Information.lean", @@ -448,6 +464,13 @@ "sha1_fr": "ba86fb557a683a645f433cee7c461fd6a29801d1", "sha1_en": "9c3d0eb0f7be6f3e0001470a8c62907475ac3b47" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/InformationNoisy.lean", + "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/InformationNoisy_en.lean", + "sha1_fr": "ae10e1c0a2db78d5c37509ca501b57cd622ead58", + "sha1_en": "2d4234302e34a5795ca390fd61cb5251dd04d26e" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian/Max.lean", @@ -492,10 +515,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian.lean", - "en": "MyIA.AI.Notebooks/GameTheory/lean_game_defs_ext/Bayesian_en.lean", - "sha1_fr": "88bec2fd61b1e2dc412f8e9105c21541604c596c", - "sha1_en": "fc230d4fa14d5a9f67ab1fdda163ea29f14a05d4" + "fr": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax.lean", + "en": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax_en.lean", + "sha1_fr": "aab261636a76e5ffbba601bdc4d2ea751fa0c3ed", + "sha1_en": "bb021499128fab24bd5993962e57a391d56f033b" }, { "kind": "lean", @@ -518,13 +541,6 @@ "sha1_fr": "703f3c9a5f30b0b15c2cad522a62b075fc99c022", "sha1_en": "095b602279907a3fd53d13782bdf473ec23b087a" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax.lean", - "en": "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax_en.lean", - "sha1_fr": "aab261636a76e5ffbba601bdc4d2ea751fa0c3ed", - "sha1_en": "bb021499128fab24bd5993962e57a391d56f033b" - }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/GenAI/CaseStudies/Fort-Boyard/fort-boyard-python.ipynb", @@ -538,10 +554,10 @@ "kind": "notebook", "fr": "MyIA.AI.Notebooks/GenAI/CaseStudies/Medical-Chatbot/medical_chatbot.ipynb", "en": "MyIA.AI.Notebooks/GenAI/CaseStudies/Medical-Chatbot/medical_chatbot_en.ipynb", - "sha1_fr": "adfb4c3fbd46742ffabad51e08f4e3d864bc8411", - "sha1_en": "0122162bc23978e4861ede8700c6706fc08b27c1", - "sources_sha1_fr": "5c856d528003c45c3c14fa7edb7f365f733367f5", - "sources_sha1_en": "3c5fb282de6b37d9b9bf8d893fcc066332dcab11" + "sha1_fr": "bb977f143b961e16df22d8301d9b7d5cd196fc5d", + "sha1_en": "8c4014f7789a296da99409ba26c81f951012dc68", + "sources_sha1_fr": "24488e904f44fc5d3ac215ffebc8e81dbf47a094", + "sources_sha1_en": "1a318e25303b60b405735234bb128187d44010c6" }, { "kind": "notebook", @@ -583,28 +599,28 @@ "kind": "notebook", "fr": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-05-ModelMerging-Routing-Python.ipynb", "en": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-05-ModelMerging-Routing-Python_en.ipynb", - "sha1_fr": "ae0d919bd1bd351919d2225750d77e88b107e960", - "sha1_en": "1a2d7551ee0346924b4f13333b9d3da468d27b3e", - "sources_sha1_fr": "9da25232d4f5b835ddb38494950f63b242adfcf9", - "sources_sha1_en": "4ea62f69c902b380a0e5580e195fcf177738c53c" + "sha1_fr": "db55d1fd039535ef069c92d07d532ad84201de36", + "sha1_en": "abc820b8c023a1aec8179989f57eb765a42ff41d", + "sources_sha1_fr": "f08da2fac29c53b215305d35a1d40794f528c004", + "sources_sha1_en": "e295acf1f7d70a1b6255c476da2e1666ac914da7" }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation/01-1-OpenAI-DALL-E-3.ipynb", "en": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation/01-1-OpenAI-DALL-E-3_en.ipynb", - "sha1_fr": "caf07d4d43a7ded15c30eea542949db46306b153", - "sha1_en": "fc3e3855f24ffd6a51259b6bff18f76fa19ff49e", - "sources_sha1_fr": "27050c403f1bb6ef8f4138c6b2e366fea45ea80b", - "sources_sha1_en": "ec2974ef2058a1f885ba6df9a13ab731d29d5153" + "sha1_fr": "b7e9dabbc4b8fbc37cd882b31892b30495220715", + "sha1_en": "378acf4f91b90697b734ce30652924dacf9a24fa", + "sources_sha1_fr": "d1789a5b13a244c9cb5bc2f796328a7d7db10fd1", + "sources_sha1_en": "87ca187590d5830a72af61f5c35bc782e683eaf6" }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation/01-2-GPT-5-Image-Generation.ipynb", "en": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation/01-2-GPT-5-Image-Generation_en.ipynb", - "sha1_fr": "df9aef42e138aa251d7f014f54dec565099027e4", - "sha1_en": "0db986cf9e142f8857054f3816db31b2f424e4d4", - "sources_sha1_fr": "ee547147d82c0244698420494580b6fe67b25a1e", - "sources_sha1_en": "508329b6c5fdc455f1aab32f175572ae988b89da" + "sha1_fr": "be4f1be5e50aeb0f6b4f6f1e6dc21c8252276e96", + "sha1_en": "2769705afff944ca1dd9561611fe73d760421eb6", + "sources_sha1_fr": "30b155538bdee3c2d910bb6f4311293d214e8269", + "sources_sha1_en": "c5ab1b7caf121debf3db17067697341bf76c4328" }, { "kind": "lean", @@ -620,13 +636,6 @@ "sha1_fr": "d34a7ecca4dc1a2e4170fe96022cf3d06bf6df61", "sha1_en": "bf57dd43fa14a66428cd163f8c074bcb25bf1bc8" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/GrokkingLemmas.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/GrokkingLemmas_en.lean", - "sha1_fr": "cb4a74514bafe618f4daa754e6efcd15c5b201e6", - "sha1_en": "013cf22dc76817632107f362e9066ddb2b7bde00" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/Grokking.lean", @@ -634,6 +643,13 @@ "sha1_fr": "03d9e84f12881f4d513cecdf00fa2381d41c5902", "sha1_en": "60cb605d140488713640ce61f8965f2420cdae0c" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/GrokkingLemmas.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/GrokkingLemmas_en.lean", + "sha1_fr": "cb4a74514bafe618f4daa754e6efcd15c5b201e6", + "sha1_en": "013cf22dc76817632107f362e9066ddb2b7bde00" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/EffectiveTheory/InfoBits.lean", @@ -655,6 +671,13 @@ "sha1_fr": "f5d3cb94c9a910a5ce5e3fc78df24d7ba39e2c45", "sha1_en": "590bfa429c7d1eb9b74f3f00a7981bc214913f88" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow_en.lean", + "sha1_fr": "9150faea9d36a99b5369d76b3bc5b79083529089", + "sha1_en": "ea229ad8a5961d3a9969cfd067989f1116990c0d" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow/Plain.lean", @@ -669,13 +692,6 @@ "sha1_fr": "51f0d2338dcef3d270d0f00856e7395d96783b92", "sha1_en": "c21944299187bae2163e59c0a142be27a1dcec5c" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/GradientFlow_en.lean", - "sha1_fr": "9150faea9d36a99b5369d76b3bc5b79083529089", - "sha1_en": "ea229ad8a5961d3a9969cfd067989f1116990c0d" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/MathUniverse.lean", @@ -690,6 +706,13 @@ "sha1_fr": "86ac10693bc0ce5d4c9e062848c459d2816975be", "sha1_en": "0a915c6f6538234779cebef25b23f0a470f465d5" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning_en.lean", + "sha1_fr": "419e69e5a4d0150c994c055bc37e6ba381c63c68", + "sha1_en": "1766c34c452f1a2efa4e6a445b7735bc02c46846" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Agnostic.lean", @@ -746,13 +769,6 @@ "sha1_fr": "1d7981a3f8bfeafc3ce3577835e7c4b04c48520e", "sha1_en": "bc8bf689ba3b3b885f846ecc2708cd90497a1f4f" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/SampleExpect.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/SampleExpect_en.lean", - "sha1_fr": "89102d9484eb888b6b2ab1dfeef193a9abe986c3", - "sha1_en": "8737affc58f842b15dc61bdce73197696fb1862f" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/Sample.lean", @@ -760,6 +776,13 @@ "sha1_fr": "df970c5985990cdcbd5834e425ed9ed54fc52e8c", "sha1_en": "ef69ee6a29a30de6b4c03b24578f25b39d9ab949" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/SampleExpect.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/SampleExpect_en.lean", + "sha1_fr": "89102d9484eb888b6b2ab1dfeef193a9abe986c3", + "sha1_en": "8737affc58f842b15dc61bdce73197696fb1862f" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning/UniformConcentration.lean", @@ -776,10 +799,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/PacLearning_en.lean", - "sha1_fr": "419e69e5a4d0150c994c055bc37e6ba381c63c68", - "sha1_en": "1766c34c452f1a2efa4e6a445b7735bc02c46846" + "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron.lean", + "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron_en.lean", + "sha1_fr": "5470ccd3340aa40584b47967abb30d3fc5407d07", + "sha1_en": "facb22647feb4cd522566749a824c2925076c739" }, { "kind": "lean", @@ -809,13 +832,6 @@ "sha1_fr": "92ab1d8e1a5a6eaef8486c1e6feac08070691a0e", "sha1_en": "070dc3433322d3205392d94c9b728a30d5bf6679" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron.lean", - "en": "MyIA.AI.Notebooks/ML/learning_theory_lean/Perceptron_en.lean", - "sha1_fr": "5470ccd3340aa40584b47967abb30d3fc5407d07", - "sha1_en": "facb22647feb4cd522566749a824c2925076c739" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/ML/learning_theory_lean/Renaming.lean", @@ -823,6 +839,13 @@ "sha1_fr": "e9c24cb0462731920660e23cdb6c7d4508e49f91", "sha1_en": "b98d3b976be2d4704e50fe5d2af4fd1e39b9d910" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation.lean", + "en": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation_en.lean", + "sha1_fr": "df93fbb84e49185d915e70b2c9db679b14c0371e", + "sha1_en": "3c1666f56ff1f7c86d5d2af34cb77ed1890a3d55" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation/Basic.lean", @@ -867,10 +890,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation.lean", - "en": "MyIA.AI.Notebooks/Probas/Applications/Percolation/percolation_lean/Percolation_en.lean", - "sha1_fr": "df93fbb84e49185d915e70b2c9db679b14c0371e", - "sha1_en": "3c1666f56ff1f7c86d5d2af34cb77ed1890a3d55" + "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence.lean", + "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence_en.lean", + "sha1_fr": "f513f7528588118bf19fcc87897bfb4e1b7bf878", + "sha1_en": "487034426d2fdb4e8d89cc8093c870e7edd9313e" }, { "kind": "lean", @@ -902,10 +925,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence.lean", - "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Coherence_en.lean", - "sha1_fr": "f513f7528588118bf19fcc87897bfb4e1b7bf878", - "sha1_en": "487034426d2fdb4e8d89cc8093c870e7edd9313e" + "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins.lean", + "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins_en.lean", + "sha1_fr": "074b54c2eade6f963508e672b4c6d2c8d489bc8e", + "sha1_en": "27d3ae942c1fc7965c5c305d74f2955a48c83ff3" }, { "kind": "lean", @@ -928,20 +951,20 @@ "sha1_fr": "10b92f4fed904e99a7b2594ecea7e7905d77fbda", "sha1_en": "25858906efc56c02273875aee2f67bcef1a7546d" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins.lean", - "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Gittins_en.lean", - "sha1_fr": "074b54c2eade6f963508e672b4c6d2c8d489bc8e", - "sha1_en": "27d3ae942c1fc7965c5c305d74f2955a48c83ff3" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/README.md", "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/README.en.md", - "sha1_fr": "1fb9645c3a1bc5ad54bc5bb182c61599a3f32777", + "sha1_fr": "9f1c33374459471dba3a2bb0db9aff22b79de954", "sha1_en": "2a6c3112b300c7a89a3799815f06c7c0edfa683c" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility.lean", + "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility_en.lean", + "sha1_fr": "54d8ce7649cde1ea6b437ab3b2d81b754f91900b", + "sha1_en": "3713b683e62d961519a5d3a9fa4b97a672111ca0" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility/Axioms.lean", @@ -963,13 +986,6 @@ "sha1_fr": "1f18b10f2587900a8d107d345c306c2d331bf3cc", "sha1_en": "957506b839753778e100c994e32884bba83d3934" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility.lean", - "en": "MyIA.AI.Notebooks/Probas/DecisionTheory/decision_theory_lean/Utility_en.lean", - "sha1_fr": "54d8ce7649cde1ea6b437ab3b2d81b754f91900b", - "sha1_en": "3713b683e62d961519a5d3a9fa4b97a672111ca0" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/Probas/Infer/scripts/README.md", @@ -1030,7 +1046,7 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/partner-course-quant-trading/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/partner-course-quant-trading/README.en.md", - "sha1_fr": "3921eed159d44124ba7b561da64cef053db4eb40", + "sha1_fr": "19dedd87a2fb088ac702123e724fe0b516a264fd", "sha1_en": "549decc5a2334f45789182db7c6b95bc15087e12" }, { @@ -1064,10 +1080,10 @@ "kind": "notebook", "fr": "MyIA.AI.Notebooks/QuantConnect/partner-course-quant-trading/kit-transitoire/02-ML-XGBoost/research.ipynb", "en": "MyIA.AI.Notebooks/QuantConnect/partner-course-quant-trading/kit-transitoire/02-ML-XGBoost/research_en.ipynb", - "sha1_fr": "d5369140b014fdbf8acee5fddddbd4fa4a7188f3", - "sha1_en": "28d36ac9251183c52d048dedae1a4ec84d1049b2", - "sources_sha1_fr": "795c78d2a8d21bfa731fc91f37ff30fa078d9485", - "sources_sha1_en": "1ae7765b26eeab7244bb5aa20aa6f9569b37a3a2" + "sha1_fr": "707f8a44b526fe83dd04d95dc9c2939050ef6099", + "sha1_en": "35a7359f2817bfcc3d7a88520925e9a3993d2e33", + "sources_sha1_fr": "f4d1c5cb911e7267db79d25abd2a98d8b7a78796", + "sources_sha1_en": "0a0ae402e43743a59cdf0fabf5fb7a3c1cc820aa" }, { "kind": "readme", @@ -1206,8 +1222,8 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/EMA-Cross-Alpha/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/EMA-Cross-Alpha/README.en.md", - "sha1_fr": "30137196486eb4defa088d581f5ae3ac9134b5aa", - "sha1_en": "70c3f98809731d5d9d5082851731b75ec61bc3d1" + "sha1_fr": "d306c0b7a69d9963944b8ea73c585bd8e2b42a28", + "sha1_en": "2428435f0ac241a473bf1b14914334141a736a88" }, { "kind": "readme", @@ -1234,29 +1250,29 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_EMATrend/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_EMATrend/README.en.md", - "sha1_fr": "d465f34e150ec60a600223dfe92a3d064e746a53", - "sha1_en": "a39e32bababed1ec43d77da648051c2950f7b048" + "sha1_fr": "766a6e090fa7cc1367b1b6c8870ae4d6d70a58cc", + "sha1_en": "94583f78c089c99e98b68ad68b6cec160fbd4906" }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_FamaFrenchAllWeather/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_FamaFrenchAllWeather/README.en.md", - "sha1_fr": "c5ae9fd5555ae8c9741498c4f3b7bf835bbbab40", - "sha1_en": "5d8cbcf91260d7cfd7cbb5ebb3ed76deddb58b91" + "sha1_fr": "1822b0825e467f77bd72e7b569aee11acea703de", + "sha1_en": "5d6dec4a00508470ec3cdcf4e36fffa12702a8d0" }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_MomentumRegime/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_MomentumRegime/README.en.md", - "sha1_fr": "e97efd6ab33dad12a9e265d7e7f397a324079385", - "sha1_en": "c6d21e74d28d06b45216c4d746a1d427704666bd" + "sha1_fr": "1b42dc646ac151fb1700b09c839ced9b076c4f7a", + "sha1_en": "d8c15ea8a49091664d50a5debbc855535c946692" }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_TrendWeather/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/Framework_Composite_TrendWeather/README.en.md", - "sha1_fr": "cac9d7da7ca426ea1545827be2b93b8657630cb2", - "sha1_en": "27e6d6e13eacf73d791630efef7076ade7d8f5c3" + "sha1_fr": "e5324d6165e37b9a8c2096b42fd20293f0e856d9", + "sha1_en": "f36fa0f71d2d6ec5f89133291bfff30c5c45d89a" }, { "kind": "readme", @@ -1353,8 +1369,8 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/ML-HeadShoulders-CNN/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/ML-HeadShoulders-CNN/README.en.md", - "sha1_fr": "c173e6eee06a0a608cfc7f3b00d6fbbcce932280", - "sha1_en": "5667e1d0f680416b9e2119dbf91ec986bff251ff" + "sha1_fr": "52a764d96cfe631dfd97888436df4a9ee578f344", + "sha1_en": "30aa1f02651a2e8b91a0d7e2586b5ca1c3338588" }, { "kind": "readme", @@ -1402,8 +1418,8 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/ML-XGBoost/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/ML-XGBoost/README.en.md", - "sha1_fr": "57cddbb64e60ff29b862ade0cad2e0dc38ce8d75", - "sha1_en": "02b5b1b301508e6b968ba95921fea59b7f140827" + "sha1_fr": "451788d85ff914b8bd147c6d3568ef03c6fe0f3a", + "sha1_en": "60ac12606cd921da0462fc0f9ebcff4c6179ed11" }, { "kind": "readme", @@ -1444,14 +1460,14 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/MomentumRegime-AdaptiveWeights/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/MomentumRegime-AdaptiveWeights/README.en.md", - "sha1_fr": "8792c4719e8a1cc5be302f6bade0de899d6ddb1a", - "sha1_en": "7bd6298c8706075dea17c045ae394122a364244c" + "sha1_fr": "ca8529196e62177500e4c8db696a5827e5eafa47", + "sha1_en": "e04e17d896305ac2ca4d1f0b64b5d703d30d76ce" }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/Multi-Layer-EMA/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/Multi-Layer-EMA/README.en.md", - "sha1_fr": "80068741392964ed4d1083d240b7b32ec9126bb2", + "sha1_fr": "dbc57061846a9972280f88bedb3f4214b3024a20", "sha1_en": "62ffc37680a9b86974f4822c2e9ca90a8ff9dbcd" }, { @@ -1486,8 +1502,8 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/RegimeSwitching/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/RegimeSwitching/README.en.md", - "sha1_fr": "94a25edc96dfc97a94b5e66511b2062de14b8fa2", - "sha1_en": "104227f11bbd5ab36af49aa74cd27cf02f104240" + "sha1_fr": "66a060cf389bd410283b3341888967702047078a", + "sha1_en": "67abbc4cd9a9cb47a3bab2e3de2281b2ad73d1ba" }, { "kind": "readme", @@ -1507,7 +1523,7 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/SectorMomentum/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/SectorMomentum/README.en.md", - "sha1_fr": "bcdbf2f0c097fdbdc649361d00563e37bff14f14", + "sha1_fr": "d0851bf32910504fae4e8179713f97c72d51b3aa", "sha1_en": "ff5055d1d4fa82ae8ef02c504cffd1d083258680" }, { @@ -1521,7 +1537,7 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/TradingCosts-Optimization/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/TradingCosts-Optimization/README.en.md", - "sha1_fr": "81a8f93e472cf6e735d9e7159eab9d29809bd163", + "sha1_fr": "c652760aea4dd2a4ac6deabc489204c705a39280", "sha1_en": "3567b05a33641d611ad8a1bf88672c60ba912608" }, { @@ -1535,7 +1551,7 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/VIX-TermStructure/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/VIX-TermStructure/README.en.md", - "sha1_fr": "0a06f772645f87db17f98862c7314eb68d3c8e38", + "sha1_fr": "ef20cd95f7e2fddc0bbc216d8a0e72a081494ba1", "sha1_en": "ed340a65344739fbe476c4fb9c4285e6b369d4ef" }, { @@ -1556,15 +1572,22 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/VolTarget-Momentum/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/VolTarget-Momentum/README.en.md", - "sha1_fr": "fe6767e177813b6087eb74a00c38596297c6df04", + "sha1_fr": "3d0ee244eb2aef84cda62768456342c5c5213ef2", "sha1_en": "cbfea4388481685a13f0e38ee3977b8d9741f1ab" }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/QuantConnect/projects/composite-c2-equityfactor/README.md", "en": "MyIA.AI.Notebooks/QuantConnect/projects/composite-c2-equityfactor/README.en.md", - "sha1_fr": "5cff8cb071aef62c733621a8662ef446b8a34f65", - "sha1_en": "5a794d673285902b0c1ae1c458b4d22995b214db" + "sha1_fr": "2fbaed8a21e2d61485c17ff7e81a1745f9675e1d", + "sha1_en": "bb04d3aebe5f924d62fd495896827e246aec5f91" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy_en.lean", + "sha1_fr": "b0911f8d95cd87442ffe4936ebb61baf83d699f8", + "sha1_en": "907f237c093e3feba0f541882132dae24f352c43" }, { "kind": "lean", @@ -1573,6 +1596,13 @@ "sha1_fr": "fc4d4337e19ea5cfb83f196092c448bdd33ae610", "sha1_en": "486fae4e00f7a615d99fa1ec348706547ed00a00" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos_en.lean", + "sha1_fr": "f9b08709747f25e1152d4a9369b5a0226a725d9e", + "sha1_en": "f285dbff792ce6c07d778acc0ede4a5e5eacc2bb" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Approximation.lean", @@ -1622,13 +1652,6 @@ "sha1_fr": "514fc97841651dda9293d7a8ec8a4e877934a3f1", "sha1_en": "d833d169dfa9e6382763a6b51ba6067feca3ff86" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/LiftSplit.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/LiftSplit_en.lean", - "sha1_fr": "5705da94924d6bc79287df6a795b2b10dc48bdb0", - "sha1_en": "db61bd1091c2588d48e8d7f6a378c63599d7a3e8" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Lift.lean", @@ -1636,6 +1659,13 @@ "sha1_fr": "1191e752b22966923866ba4a261d6ed91e0feb0b", "sha1_en": "26c28e12325ddafea17e22bdbb068c70b9730425" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/LiftSplit.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/LiftSplit_en.lean", + "sha1_fr": "1ad8bb49957a760b5db58ce45ee4611faaa7263e", + "sha1_en": "8e8cd4e7ae8cadc6f4e4d29dbe82db62cc14137c" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/MeanSplit.lean", @@ -1675,8 +1705,15 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/SignedSums.lean", "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/SignedSums_en.lean", - "sha1_fr": "712f4cf73bfbd6180de99c3afc043c4654247e04", - "sha1_en": "083f8e94e5d6723630c76ef3661891b77e485f15" + "sha1_fr": "0cece51f29544d9bc64f989f8e8bb75a472328f3", + "sha1_en": "5c8891cfc182f9d79c5306c78bcc6efe947e6eef" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split.lean", + "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split_en.lean", + "sha1_fr": "94c823ecb8d4e407cc2c437acd82be4bae3db024", + "sha1_en": "eba63e71a122adf40d953e4a424c04c7dc0fd93f" }, { "kind": "lean", @@ -1692,13 +1729,6 @@ "sha1_fr": "57673a25be6601ea7b47ed129006b21d0fc8d388", "sha1_en": "4bb59b9f42754e31f8ad2f08b3c9dafcd6ce054b" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Split_en.lean", - "sha1_fr": "94c823ecb8d4e407cc2c437acd82be4bae3db024", - "sha1_en": "eba63e71a122adf40d953e4a424c04c7dc0fd93f" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos/Tent.lean", @@ -1715,17 +1745,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy/Komlos_en.lean", - "sha1_fr": "f9b08709747f25e1152d4a9369b5a0226a725d9e", - "sha1_en": "f285dbff792ce6c07d778acc0ede4a5e5eacc2bb" - }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy.lean", - "en": "MyIA.AI.Notebooks/Search/discrepancy_lean/Discrepancy_en.lean", - "sha1_fr": "b0911f8d95cd87442ffe4936ebb61baf83d699f8", - "sha1_en": "907f237c093e3feba0f541882132dae24f352c43" + "fr": "MyIA.AI.Notebooks/Search/search_lean/Astar.lean", + "en": "MyIA.AI.Notebooks/Search/search_lean/Astar_en.lean", + "sha1_fr": "ee3310a867a93286f44aadbaf2a3db7491bda4e8", + "sha1_en": "726dfedcb1182af83f792a922a0f03765efea109" }, { "kind": "lean", @@ -1755,13 +1778,6 @@ "sha1_fr": "c3fd147869d66a9a60ff0ef045d40b43e758d3c3", "sha1_en": "85e62a5287426736b3ab1b60c780ce8d88b4c1c2" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Search/search_lean/Astar.lean", - "en": "MyIA.AI.Notebooks/Search/search_lean/Astar_en.lean", - "sha1_fr": "ee3310a867a93286f44aadbaf2a3db7491bda4e8", - "sha1_en": "726dfedcb1182af83f792a922a0f03765efea109" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/Search/search_lean/Szpiro.lean", @@ -1771,10 +1787,24 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku/Basic.lean", - "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku/Basic_en.lean", - "sha1_fr": "233820571fa96ff46b199f1312bd02ddb002812b", - "sha1_en": "ab3da5055b63cca28a154f61cce8680c619ac5db" + "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku.lean", + "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku_en.lean", + "sha1_fr": "6ff2aba3668153aed3c85db52eaef6aaa7e26875", + "sha1_en": "bee3c7635822bff8534a5c9a6ab6e9afc2a2b990" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku/Basic.lean", + "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku/Basic_en.lean", + "sha1_fr": "233820571fa96ff46b199f1312bd02ddb002812b", + "sha1_en": "ab3da5055b63cca28a154f61cce8680c619ac5db" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku/Certificate.lean", + "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku/Certificate_en.lean", + "sha1_fr": "330d1cdc68ceb9fa7564bad65d2419fdb66a64f8", + "sha1_en": "c503d7c050539e9a336ce42a16fab95b805c3524" }, { "kind": "lean", @@ -1790,30 +1820,23 @@ "sha1_fr": "d05e2d2083162a70eb8605c0517c0e6c4e9cd2d7", "sha1_en": "a74804e49451fac46a65610c0d9c7816b1f5bb46" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku.lean", - "en": "MyIA.AI.Notebooks/Sudoku/sudoku_lean/Sudoku_en.lean", - "sha1_fr": "9468f4895b4b56fa614159090aa88b478cd0dfaf", - "sha1_en": "30f1d304d5d9e909ac5c325907da5ab4cbc915b5" - }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-06-Sensibilite-2026-Lean-Python.ipynb", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-06-Sensibilite-2026-Lean-Python_en.ipynb", "sha1_fr": "73f58999db03f9649a7c02db16420528bf1c90e6", "sha1_en": "efdff3c1f2f8c65cf030b7f712d1773bf609781f", - "sources_sha1_fr": "0c96ec4bc36464c163bd3f4dfaaedcab8e07e14b", + "sources_sha1_fr": "56f7459d2a8a55c8c1a34edb62e72de2a5dd86ed", "sources_sha1_en": "14c817e0a9189065340fe6760bde24bfa44fe215" }, { "kind": "notebook", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-06b-Sensibilite-2026-Lean.ipynb", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-06b-Sensibilite-2026-Lean_en.ipynb", - "sha1_fr": "eaf45cfea264a611cbdb5c523da69353249b3477", - "sha1_en": "aa7ff661aae1abef4d08ba674296ca350a741fb6", - "sources_sha1_fr": "1063a3296c8ff96e92b6c335b36d5fa920aa50cc", - "sources_sha1_en": "c435c41520fc8daa3de8d4f956f5b6e070801da1" + "sha1_fr": "14e357387f0a783bc8ff749af51b6ef472cda9cc", + "sha1_en": "14632bb3e2d20d4f1dc48e19eab2e08677872d16", + "sources_sha1_fr": "e15117978b661aa7179178844ff923ad553f3308", + "sources_sha1_en": "65f947050f5f083db280a1563510c7d59ab3d69c" }, { "kind": "notebook", @@ -1837,17 +1860,47 @@ "kind": "notebook", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-12-Comptage-Matching-Lean-Python.ipynb", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-12-Comptage-Matching-Lean-Python_en.ipynb", - "sha1_fr": "4d07629e4bc3786b9b99d7413842c50e6510b601", - "sha1_en": "f4a5a6dfa4dc93c7c8d66b6275ff3438c8250b65", - "sources_sha1_fr": "b3d8bb21aeca61a8aa72e6b311a44c3bcdd24a63", - "sources_sha1_en": "8233925e59b2b7fd0c5d1cac3df53dcb47724983" + "sha1_fr": "253b8243a859b32175856fa739ea7f0cb5d4209a", + "sha1_en": "99f9aa43ba29fd50e428a21a8c1c46adc9754300", + "sources_sha1_fr": "db84d02b510524d69f8a03a68e89f1404c725aa7", + "sources_sha1_en": "91cbe8aeca2ce384b99e44bba3178745dea9bbeb" + }, + { + "kind": "notebook", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-13-Smoothing-Local-Kakeya.ipynb", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/ANALYSE/ANALYSE-13-Smoothing-Local-Kakeya_en.ipynb", + "sha1_fr": "a94bd652e0a49a7f054e91d76b2eb19470a19460", + "sha1_en": "b027d0d2dd82178d0a1eaefc5ee9731c938e4c84", + "sources_sha1_fr": "d929ab4c44c72b01d4a1735eba72ddb5ec66c884", + "sources_sha1_en": "185c19113cbb09cd7c5b680d06b79474e1352cf3" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing_en.lean", + "sha1_fr": "03071c5b55b415831b71fb2c4a3ebf933edae1d7", + "sha1_en": "a29097efa84ced220594d21b948bc3897705cea4" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup_en.lean", + "sha1_fr": "0d94d9f65927d087b82ef1f8033670061c54e738", + "sha1_en": "7b7f80394bfc869c41560b49e48b9587bccb0d13" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots_en.lean", + "sha1_fr": "56efe973e50920e313be4a5bd5ce52a848990a4e", + "sha1_en": "ed3254f2ac87b68e30f18a8c41387abc402bad4c" }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/AlexanderTrivial.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/AlexanderTrivial_en.lean", - "sha1_fr": "fded56e88d9f54f8a883bdc12bf38fcbea017f8d", - "sha1_en": "7a29deb7c26e3b7e1b969898a42a24d61634f741" + "sha1_fr": "af5781af6215b0567587eefb8ce246ce3b567b54", + "sha1_en": "396b472946ee406d8f754577ad46beb26f880381" }, { "kind": "lean", @@ -1860,8 +1913,8 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Basic.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Basic_en.lean", - "sha1_fr": "4afde85308b0165ec074733eb37a4d3c73965d23", - "sha1_en": "50e125c7fca2646ff1208b049f6941c7891c97f4" + "sha1_fr": "d555cd05efe6b827c8357bbc0b3269220f0e66d5", + "sha1_en": "6767a4653c3942285ffb09fbdbe38d914f08f566" }, { "kind": "lean", @@ -1870,6 +1923,13 @@ "sha1_fr": "b7c37c6f5d0ba1c6faa447a114415354facaf6e3", "sha1_en": "6614dd17bc034a8d9fd87da4fc9557805cc8ecd1" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway_en.lean", + "sha1_fr": "e2a297515d6ec896e95a9b9d03fe9509b576fff2", + "sha1_en": "b2cbdebcaa5084eb55f38dca92860a6411a398fb" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ConwayKTMutation.lean", @@ -1888,15 +1948,8 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ConwayPD.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ConwayPD_en.lean", - "sha1_fr": "30fbf75132f96f192bd323eb72e6d2a93e8d4804", - "sha1_en": "d287b62b17fd1f2990b006afe2692b9c3dd878b8" - }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Conway_en.lean", - "sha1_fr": "e2a297515d6ec896e95a9b9d03fe9509b576fff2", - "sha1_en": "b2cbdebcaa5084eb55f38dca92860a6411a398fb" + "sha1_fr": "10896b133e27a8c4a3008b2c935ecd943a4fcee4", + "sha1_en": "077e73fdad54a180d5d8bfa1e146a60a6ab85509" }, { "kind": "lean", @@ -1944,15 +1997,22 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Mutation.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Mutation_en.lean", - "sha1_fr": "66830765c73d07445dec534549220f74ae51eb50", - "sha1_en": "c75e6a31b3b58842fdd366edf4cfa5494dccd0da" + "sha1_fr": "18455240aef116e547e69825a85a21c9d7051557", + "sha1_en": "3938514ecc10c4cb83260fe46327d61b26dd3463" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister_en.lean", + "sha1_fr": "298e292e593e0592f42b6a47e395b9da2dfa29b4", + "sha1_en": "b82ecdcc444e9420b8c8c386b6199c26722a24ae" }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ReidemeisterAny.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/ReidemeisterAny_en.lean", - "sha1_fr": "7f4e228ead67117d004c2cd68e546ec4eb2d4b66", - "sha1_en": "54709813249b9f514cafbdac5f8140eb1964c6e0" + "sha1_fr": "bbdb8c953b1369a85d97dd53b7d750a6ca75aaf5", + "sha1_en": "38714cfb4d65a53d3b0a4108d6fae7d3d0b26f82" }, { "kind": "lean", @@ -1975,13 +2035,6 @@ "sha1_fr": "8bcd6ee37b603a0637fc8efc71337f761231b71e", "sha1_en": "4bb6010a990802032c8767e9af3c06f36eb39d01" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Reidemeister_en.lean", - "sha1_fr": "298e292e593e0592f42b6a47e395b9da2dfa29b4", - "sha1_en": "b82ecdcc444e9420b8c8c386b6199c26722a24ae" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots/Slice.lean", @@ -1996,13 +2049,6 @@ "sha1_fr": "bef33f49fb4aca46b71d90004109b8ff27578864", "sha1_en": "77505c9eeb26fcf0cc63ec702e1713c42654feaa" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/Knots_en.lean", - "sha1_fr": "56efe973e50920e313be4a5bd5ce52a848990a4e", - "sha1_en": "ed3254f2ac87b68e30f18a8c41387abc402bad4c" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/KNOTS/knot_lean/README.md", @@ -2010,6 +2056,13 @@ "sha1_fr": "6ec8b965845567d770bcf96a88d3182b8e8ff89d", "sha1_en": "c9b43cc6aa2495470a502c0bc93a213ed77c39bd" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100_en.lean", + "sha1_fr": "2ac6a6c6de1dd52d912f039c7bc08e0edc7c5e80", + "sha1_en": "3f8797379ceb620d6907a1ca9bbb29d483dd23b7" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100/CaracteresComputes.lean", @@ -2047,10 +2100,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/Serre100/serre100_lean/Serre100_en.lean", - "sha1_fr": "2ac6a6c6de1dd52d912f039c7bc08e0edc7c5e80", - "sha1_en": "3f8797379ceb620d6907a1ca9bbb29d483dd23b7" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration_en.lean", + "sha1_fr": "3eb2216f03dc4b6b99169a07fcc045e0e065da54", + "sha1_en": "07cda8c61197b457adc11f228c603a136911180b" }, { "kind": "lean", @@ -2080,13 +2133,6 @@ "sha1_fr": "c2fde535671375a651fec1747b0f0d07a15568f3", "sha1_en": "dfcaab8c228db5bca81e229e60248a2f8c6fa05c" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/Calibration_en.lean", - "sha1_fr": "3eb2216f03dc4b6b99169a07fcc045e0e065da54", - "sha1_en": "07cda8c61197b457adc11f228c603a136911180b" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/calibration_lean/README.md", @@ -2094,6 +2140,13 @@ "sha1_fr": "9fc3aa679066b6af4025316edfcb2164385528c9", "sha1_en": "d9c6fafa49d0689a3736d76c3dd6e310451bae95" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway_en.lean", + "sha1_fr": "9c12ee83051305727010f47895b2bdff20c65464", + "sha1_en": "2659e242605b7d5e0c82460f05932d607d4056b6" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Angel.lean", @@ -2101,6 +2154,13 @@ "sha1_fr": "21cbac41258a0563cd1caaa7644ac28968857be4", "sha1_en": "0767d5818bbaccf25bdef3f8fdcfeae75e1f8c6e" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH_en.lean", + "sha1_fr": "252b66012c812d452b9ef522d67679d5ff5efd7d", + "sha1_en": "e0f232fab3e90df21813d20550648e6d344d0a35" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSHFreeWill.lean", @@ -2129,13 +2189,6 @@ "sha1_fr": "f73e1b2a3e125f7000b758d17e1a09a3d2815753", "sha1_en": "1bdfff89cfef12431f2dc304e102b708953b289e" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CHSH_en.lean", - "sha1_fr": "252b66012c812d452b9ef522d67679d5ff5efd7d", - "sha1_en": "e0f232fab3e90df21813d20550648e6d344d0a35" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/CollatzLike.lean", @@ -2143,13 +2196,6 @@ "sha1_fr": "4f210a671e3941fa2f1d2b13bccea6daa54f610a", "sha1_en": "d2bbf17cb6967ca6dffec0de693920392ec1c39c" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/DoomsdayLemmas.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/DoomsdayLemmas_en.lean", - "sha1_fr": "a64eeed02d7e5c352abb28713f1f66e37be76f31", - "sha1_en": "ec445f2378ebeaa176adcdcb6602596c0fa8d6d3" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Doomsday.lean", @@ -2159,10 +2205,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/FractranLemmas.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/FractranLemmas_en.lean", - "sha1_fr": "24af4d1e286f4bd69581a0ca1044a53234429e89", - "sha1_en": "0a93de6ac46d7fdf2eb284f0c770031f69c9e8dc" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/DoomsdayLemmas.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/DoomsdayLemmas_en.lean", + "sha1_fr": "a64eeed02d7e5c352abb28713f1f66e37be76f31", + "sha1_en": "ec445f2378ebeaa176adcdcb6602596c0fa8d6d3" }, { "kind": "lean", @@ -2171,6 +2217,13 @@ "sha1_fr": "7a371d3ee9748a249de3559bde2b74a75fda9329", "sha1_en": "3391f13a8722cac6c3e6f914f6ef6b36f34a089c" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/FractranLemmas.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/FractranLemmas_en.lean", + "sha1_fr": "24af4d1e286f4bd69581a0ca1044a53234429e89", + "sha1_en": "0a93de6ac46d7fdf2eb284f0c770031f69c9e8dc" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/FreeWillTheorem.lean", @@ -2182,15 +2235,15 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/KochenSpecker.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/KochenSpecker_en.lean", - "sha1_fr": "bdab38c46eb2000f1cd1aa208ab7c8368bbe1b73", - "sha1_en": "e26e14163ad96c265633449ae7ba62a187475be2" + "sha1_fr": "429356b7111571987c189da5be11a63a11b9231a", + "sha1_en": "15c5d0ccb57aa0729a5ac2707c304aa9c417a574" }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2_en.lean", - "sha1_fr": "5a2a822a7a48480905e5b60ede565f0d0e896062", - "sha1_en": "bdf1f69e80996742bbec5f11c74df374fe7814e3" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life_en.lean", + "sha1_fr": "341db3bb5d8a6460d5fe88b4c7741d0b82e3738a", + "sha1_en": "ac9f0adf9a7704f2bc99380996f4213de1760d99" }, { "kind": "lean", @@ -2199,6 +2252,13 @@ "sha1_fr": "32976fc80c9b54c3297cd3929082c0a7b264df82", "sha1_en": "31f19a431afba3c1c0a8da4f85eb6c5fb16f7bf3" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/AdversarialBatteryG2_en.lean", + "sha1_fr": "5a2a822a7a48480905e5b60ede565f0d0e896062", + "sha1_en": "bdf1f69e80996742bbec5f11c74df374fe7814e3" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Computation.lean", @@ -2227,6 +2287,13 @@ "sha1_fr": "7c6b1e80a88c163a7da6b8b1df1de3b61985bcaf", "sha1_en": "68c557be3faa6718de8fcf625aa19bda55663131" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife_en.lean", + "sha1_fr": "82ce6e8ad410ef1d11e3d7691c696e570f1ff451", + "sha1_en": "6118561ae855a8b432ac61c19ee93a45525dfeb0" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/HashlifeDecideMemo.lean", @@ -2245,8 +2312,8 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/HashlifeMarginFragment.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/HashlifeMarginFragment_en.lean", - "sha1_fr": "45f47d5da3dc0dadfb66ca556e12f9f0fcd1cae1", - "sha1_en": "c8517ac7bdaea3d4930affba07d0e931185bd702" + "sha1_fr": "145d9c36cb47ca14fda3df1e4876d52ae558320b", + "sha1_en": "c4986d37d7d3a1c69f927e03d1b1950aca948a47" }, { "kind": "lean", @@ -2255,13 +2322,6 @@ "sha1_fr": "84d969a85f8da7757b33ca809ea860bb3a23dbb4", "sha1_en": "634839359a3f59e58acca4045293704ba578a1bc" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Hashlife_en.lean", - "sha1_fr": "82ce6e8ad410ef1d11e3d7691c696e570f1ff451", - "sha1_en": "6118561ae855a8b432ac61c19ee93a45525dfeb0" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/LightCone.lean", @@ -2308,8 +2368,8 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Pillars.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life/Pillars_en.lean", - "sha1_fr": "11a674b29b58b2295cc771bc12f27568a70e8878", - "sha1_en": "a22b7671411f6f76424f3eba95a9fd9a6de37ca0" + "sha1_fr": "9786cb8a995c11122ac3217a4f9d618382566cba", + "sha1_en": "56f3b653abae074b17aad01f250a243981a7819a" }, { "kind": "lean", @@ -2341,10 +2401,10 @@ }, { "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/Life_en.lean", - "sha1_fr": "6dce7b85589953e4c8aeb37d92b883150bede83a", - "sha1_en": "a1dbaabb2413d107d954b3e4940fb0fff60f319f" + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay_en.lean", + "sha1_fr": "cc773bf5f159d91d4c4734eaf2d5bb40cafbd32d", + "sha1_en": "d3a900a35c896f5aaa9d3257dbec264538fbdcdf" }, { "kind": "lean", @@ -2353,13 +2413,6 @@ "sha1_fr": "5006be39d57eb1585f23770535774fa613f054d5", "sha1_en": "4628351809ef7d16cef9d7152c0cdcee3d784d4d" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/LookAndSay_en.lean", - "sha1_fr": "cc773bf5f159d91d4c4734eaf2d5bb40cafbd32d", - "sha1_en": "d3a900a35c896f5aaa9d3257dbec264538fbdcdf" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway/MathlibMap.lean", @@ -2374,13 +2427,6 @@ "sha1_fr": "a8125a072263786184dc87f1aab221b9afb0795f", "sha1_en": "57876f2da71fdd8b8bc5c8ed174b1d41c6dceb04" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/Conway_en.lean", - "sha1_fr": "9c12ee83051305727010f47895b2bdff20c65464", - "sha1_en": "2659e242605b7d5e0c82460f05932d607d4056b6" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/README.md", @@ -2392,7 +2438,7 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/patterns/README.md", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/conway_lean/patterns/README.en.md", - "sha1_fr": "e9b9d12389083c5f75dcaecacf8ff411f6c13afe", + "sha1_fr": "d6698d39ca3d9b73b738eefb68e398ecfcefb2c3", "sha1_en": "824c148ab6ad78bdfe98641317d9ed92039bb4b6" }, { @@ -2465,6 +2511,13 @@ "sha1_fr": "d02345eeccd06142c27307b2852914e1f6471bdb", "sha1_en": "42758c75ad8a560b99c11bb8d9a5de47e621d570" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups_en.lean", + "sha1_fr": "d78a1aed2ab5d5ffcd57b934888d6273eb5432a4", + "sha1_en": "f122ba1407bbfd04227f2a829afe809dfb6510d1" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups/Additive.lean", @@ -2493,27 +2546,6 @@ "sha1_fr": "b8605ebb0d153e15454b33a8fb53bbdbf9d2c4d1", "sha1_en": "58dcf2e58f406cfadec67c0ac4b05d5b95cddda4" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/formal_groups_lean/FormalGroups_en.lean", - "sha1_fr": "d78a1aed2ab5d5ffcd57b934888d6273eb5432a4", - "sha1_en": "f122ba1407bbfd04227f2a829afe809dfb6510d1" - }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/AdicCompletionLocalRing_en.lean", - "sha1_fr": "03071c5b55b415831b71fb2c4a3ebf933edae1d7", - "sha1_en": "a29097efa84ced220594d21b948bc3897705cea4" - }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/GALOIS/galois_lean/Galois/LowerRamificationGroup_en.lean", - "sha1_fr": "0d94d9f65927d087b82ef1f8033670061c54e738", - "sha1_en": "7b7f80394bfc869c41560b49e48b9587bccb0d13" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Adjunction.lean", @@ -2619,13 +2651,6 @@ "sha1_fr": "c6d5b7b96a4169419582d98b8b354f96b496d729", "sha1_en": "019e7ac3f8fc147cf5610baf7be58ddfb7b41f17" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreservingLaws.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreservingLaws_en.lean", - "sha1_fr": "ef525dae197c826be965992e675d323a57e77ac3", - "sha1_en": "27a09e8b5dc31df94734267d370fcea1f367915c" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreserving.lean", @@ -2633,6 +2658,13 @@ "sha1_fr": "e540fb4e33d2d32edb14f13ea5becb8d1c73cd2b", "sha1_en": "0d2e73ced96b781ab0cd3057ac16361146f48750" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreservingLaws.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverPreservingLaws_en.lean", + "sha1_fr": "ef525dae197c826be965992e675d323a57e77ac3", + "sha1_en": "27a09e8b5dc31df94734267d370fcea1f367915c" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/CoversCoverageArrow.lean", @@ -2773,6 +2805,13 @@ "sha1_fr": "7e7d2f3119c76f091a4739bb4692428428eec1db", "sha1_en": "3de19d113ca61ba269368cc53202cb5ec9863f84" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque_en.lean", + "sha1_fr": "6b1b8cfa012cb705ed675353305c104462d5215f", + "sha1_en": "83a6aef1c83fc96b3c4094e409de12b9cac2c0a1" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/FlasqueAcyclic.lean", @@ -2808,13 +2847,6 @@ "sha1_fr": "3e563ecc42a6b92d1ffee5d0b98a8ce6ab4ce763", "sha1_en": "98fb4aaa852bef33c78a8e16e8e2392050ff4f21" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Flasque_en.lean", - "sha1_fr": "6b1b8cfa012cb705ed675353305c104462d5215f", - "sha1_en": "83a6aef1c83fc96b3c4094e409de12b9cac2c0a1" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Fppf.lean", @@ -2822,6 +2854,13 @@ "sha1_fr": "6e94497f0c76c97b0f144153df89418f2485a0d9", "sha1_en": "3faaef07ac2a1be5cfa15dcf24d1192cd50ce48e" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement_en.lean", + "sha1_fr": "2069a547b0be2d62b047360dda19f560babd033f", + "sha1_en": "1ba26fcf5741e5c055f5dbb9aa98f843cafc1a18" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/GodementAcyclicity.lean", @@ -2840,8 +2879,8 @@ "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/GodementComplex.lean", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/GodementComplex_en.lean", - "sha1_fr": "45699b69c50aa3c2f12fb4a8d3afbbf14c8cf39b", - "sha1_en": "a8990e0e5713dc77d399686ea3ac873e0c19c5cc" + "sha1_fr": "b21fa618fabac2d4dc3d0202002bbea424db8e08", + "sha1_en": "721e431ddadbabc99ba1970a689f98c7bf5a2072" }, { "kind": "lean", @@ -2871,13 +2910,6 @@ "sha1_fr": "c73ea36f4c11efa42e361297e963f0dc02a22f39", "sha1_en": "edd08615556930a9500c7e88802d127982a63cb2" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Godement_en.lean", - "sha1_fr": "2069a547b0be2d62b047360dda19f560babd033f", - "sha1_en": "1ba26fcf5741e5c055f5dbb9aa98f843cafc1a18" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/KanExtensions.lean", @@ -2962,13 +2994,6 @@ "sha1_fr": "5b9242eef173b512b8924ff0d2557ee862c45c8c", "sha1_en": "356435a987de1505a4f6431d8519ad2aa43d0df5" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctorLaws.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctorLaws_en.lean", - "sha1_fr": "0cae31aad039951aeef768e814b494e13467a22b", - "sha1_en": "15166bf13021b9415d3da9e09c850578702d0258" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctor.lean", @@ -2976,6 +3001,13 @@ "sha1_fr": "1e5adaf1c59443f00be74d13982cffa1bc4bc86d", "sha1_en": "e2df3c7234e7c6f0be8c2e99bbfb874245998a18" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctorLaws.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/PullbackFunctorLaws_en.lean", + "sha1_fr": "0cae31aad039951aeef768e814b494e13467a22b", + "sha1_en": "15166bf13021b9415d3da9e09c850578702d0258" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SchemesTour.lean", @@ -3011,6 +3043,13 @@ "sha1_fr": "432a39c3a8e41f47117268a6dd9671d34ac74857", "sha1_en": "7de64374c61db09e0a08ab85c8d76bf5d2cb54ee" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCohomology/Godement.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCohomology/Godement_en.lean", + "sha1_fr": "faf5851c40ee0d3b004aa56608e27224a43a9070", + "sha1_en": "bb49dc6b3c4fc038fb91bf3060bb1bb711d93d64" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCohomology/MayerVietoris.lean", @@ -3018,6 +3057,13 @@ "sha1_fr": "55c959e7797df856cbbd58ebbe3d7c11eefdf8af", "sha1_en": "55ce6410ef6a6f6e61df47349b1b0aee2a8ce383" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition_en.lean", + "sha1_fr": "bbf5bae9e8e8edd53659948c83073cf236024322", + "sha1_en": "8e30fe49084b2907889440d9ab021559286df4c1" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafConditionCharacterization.lean", @@ -3032,13 +3078,6 @@ "sha1_fr": "65e6925b216a9927d04cdcb08052ee0e682945d1", "sha1_en": "a11605fe3126bf0fd873fafea3910a18ec73eaf5" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafCondition_en.lean", - "sha1_fr": "bbf5bae9e8e8edd53659948c83073cf236024322", - "sha1_en": "8e30fe49084b2907889440d9ab021559286df4c1" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SheafHom.lean", @@ -3102,6 +3141,13 @@ "sha1_fr": "858e73be11047400ed4262ba553a10d7b8634c4b", "sha1_en": "f4e919b2d6afcb1a0c4cdc24e09d6e6125c9bae6" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces_en.lean", + "sha1_fr": "0ce7fe7b6aeccfeb929b978deae3334b7884f930", + "sha1_en": "10c939a705d38d582cb91a3461e01085b9bbf541" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/SpacesMathlib.lean", @@ -3116,13 +3162,6 @@ "sha1_fr": "5182e6b665bcaacfe700ac910b005541a6b58165", "sha1_en": "9654456bbc5c4ef990d5e3d6a746a3ca274781e6" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/Spaces_en.lean", - "sha1_fr": "0ce7fe7b6aeccfeb929b978deae3334b7884f930", - "sha1_en": "10c939a705d38d582cb91a3461e01085b9bbf541" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/Grothendieck/StalkCharacterization.lean", @@ -3197,8 +3236,15 @@ "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/README.md", "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/grothendieck_lean/README.en.md", - "sha1_fr": "f6d36a8d6c9beb53dcf50aea410c77aff2e06673", - "sha1_en": "7a48924f5dede27ef30db11e96a4b8b643dd3f47" + "sha1_fr": "92cf930b583f373f45d93e298e2672ddd0a35c30", + "sha1_en": "bc6a7dbb455dd68449ba37405686f421c3bca89c" + }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke_en.lean", + "sha1_fr": "1aee3226602f775d45bca891e62f22d2ff762a87", + "sha1_en": "a964da3e1f5e310cfc49d0c65acfbe2f3e0c45e6" }, { "kind": "lean", @@ -3249,13 +3295,6 @@ "sha1_fr": "9239b5d70e625f1744d642348dc1ae450b64459a", "sha1_en": "1cc99d9aaa0d1da2d804bb55c82f7b9bd3052ee9" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/Hecke_en.lean", - "sha1_fr": "1aee3226602f775d45bca891e62f22d2ff762a87", - "sha1_en": "a964da3e1f5e310cfc49d0c65acfbe2f3e0c45e6" - }, { "kind": "readme", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/hecke_lean/README.md", @@ -3326,6 +3365,13 @@ "sha1_fr": "e5c29e6e1b81853f2ee52411f89f844ec1fd6469", "sha1_en": "5b9dfc5470c010498674c8ae84eb1df2127e0475" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity_en.lean", + "sha1_fr": "b80687cd7008699f3d51c9935aa93644fb6236f2", + "sha1_en": "42d17bf07b5d893552af729ca9ab96f57db8c63f" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity/Fourier.lean", @@ -3389,13 +3435,6 @@ "sha1_fr": "0b25f2f26ca5ee5cd41d597a9f3ecee4713642c5", "sha1_en": "dae688cb198767a699826c4214ccaf4c51e6722e" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/Lean/sensitivity_lean/Sensitivity_en.lean", - "sha1_fr": "b80687cd7008699f3d51c9935aa93644fb6236f2", - "sha1_en": "42d17bf07b5d893552af729ca9ab96f57db8c63f" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Planners/planning_lean/Planning/Admissibility.lean", @@ -3417,6 +3456,13 @@ "sha1_fr": "bde24b049398808a9dede411907a007e6cc562e9", "sha1_en": "afbd0588cd306ebd53d7a7d924d2c3285e21a007" }, + { + "kind": "lean", + "fr": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20.lean", + "en": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20_en.lean", + "sha1_fr": "93d6359dc13b212c0e31346bd1dccbdbdde30f9f", + "sha1_en": "f767595d3244c88345da016e7ae72c682f8fb295" + }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20/Invariant.lean", @@ -3438,13 +3484,6 @@ "sha1_fr": "1aadd8d3cef97866d579d8c887b86074cff43cce", "sha1_en": "3c49f097f9eba945da3cba7e4b54427e172fccff" }, - { - "kind": "lean", - "fr": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20.lean", - "en": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/erc20_lean/ERC20_en.lean", - "sha1_fr": "93d6359dc13b212c0e31346bd1dccbdbdde30f9f", - "sha1_en": "f767595d3244c88345da016e7ae72c682f8fb295" - }, { "kind": "lean", "fr": "MyIA.AI.Notebooks/SymbolicAI/Tweety/argumentation_lean/Argumentation/Basic.lean", @@ -3503,9 +3542,9 @@ } ], "counts": { - "notebook": 18, + "notebook": 20, "readme": 93, - "lean": 384 + "lean": 387 }, "orphans_en": [ "MyIA.AI.Notebooks/GameTheory/minimax_lean/Minimax.en.md",