chore: clean up code structure and remove unused code blocks

M2/Clustering In Practice/TP3.ipynb

@@ -2,10 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 1,
    "id": "31c5786f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import umap\n",
     "\n",
@@ -16,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 2,
    "id": "5fb57b5d",
    "metadata": {},
    "outputs": [],
@@ -26,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 3,
    "id": "bc49092e",
    "metadata": {},
    "outputs": [
@@ -80,7 +89,7 @@
          "type": "float"
         }
        ],
-       "ref": "56954716-828e-4056-a5a4-8dbdc6447d2d",
+       "ref": "806f15d4-470f-490a-b5a6-78d68fdb491b",
        "rows": [
         [
          "0",
@@ -824,7 +833,7 @@
        "[552 rows x 8 columns]"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -835,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 4,
    "id": "4364612a",
    "metadata": {},
    "outputs": [],
@@ -871,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 5,
    "id": "c1be071a",
    "metadata": {},
    "outputs": [
@@ -881,7 +890,7 @@
      "text": [
       "Computing t-SNE...\n",
       "[t-SNE] Computing 91 nearest neighbors...\n",
-      "[t-SNE] Indexed 552 samples in 0.001s...\n",
+      "[t-SNE] Indexed 552 samples in 0.002s...\n",
       "[t-SNE] Computed neighbors for 552 samples in 0.020s...\n",
       "[t-SNE] Computed conditional probabilities for sample 552 / 552\n",
       "[t-SNE] Mean sigma: 0.767389\n",
@@ -998,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 6,
    "id": "5276c9b4",
    "metadata": {},
    "outputs": [
@@ -1123,15 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6abd7740",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 7,
    "id": "3c52eb1a",
    "metadata": {},
    "outputs": [
@@ -1278,7 +1279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 8,
    "id": "60a8df77",
    "metadata": {},
    "outputs": [
@@ -1433,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 9,
    "id": "2da3589a",
    "metadata": {},
    "outputs": [
@@ -1483,7 +1484,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "studies",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },

M2/Reinforcement Learning/project/Project.ipynb

@@ -0,0 +1,80 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "7e37429a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ale_py\n",
+    "import gymnasium as gym\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85ff0eb4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A.L.E: Arcade Learning Environment (version 0.11.2+ecc1138)\n",
+      "[Powered by Stella]\n"
+     ]
+    },
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mLe noyau s’est bloqué lors de l’exécution du code dans une cellule active ou une cellule précédente. \n",
+      "\u001b[1;31mVeuillez vérifier le code dans la ou les cellules pour identifier une cause possible de l’échec. \n",
+      "\u001b[1;31mCliquez <a href='https://aka.ms/vscodeJupyterKernelCrash'>ici</a> pour plus d’informations. \n",
+      "\u001b[1;31mPour plus d’informations, consultez Jupyter <a href='command:jupyter.viewOutput'>log</a>."
+     ]
+    }
+   ],
+   "source": [
+    "gym.register_envs(ale_py)\n",
+    "\n",
+    "env = gym.make(\"ALE/Tennis-v5\", render_mode=\"human\")\n",
+    "obs, info = env.reset()\n",
+    "obs, reward, terminated, truncated, info = env.step(env.action_space.sample())\n",
+    "env.close()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89633751",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

M2/Reinforcement Learning/project/pyproject.toml

@@ -0,0 +1,11 @@
+[project]
+name = "project"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "ale-py>=0.11.2",
+    "numpy>=2.2.5",
+    "pyclustertend>=1.4.9",
+]

pyproject.toml

@@ -113,3 +113,8 @@ section-order = ["future", "standard-library", "third-party", "data-science", "m
 # On sépare les outils de manipulation de données des frameworks de ML lourds
 "data-science" = ["numpy", "pandas", "scipy", "matplotlib", "seaborn", "plotly"]
 "ml" = ["tensorflow", "keras", "torch", "sklearn", "xgboost", "catboost", "shap"]
+
+[tool.uv.workspace]
+members = [
+    "M2/Reinforcement Learning/project",
+]