Refactor code in numerical methods notebooks

- Updated import order in Point_Fixe.ipynb for consistency.
- Changed lambda functions to regular function definitions for clarity in Point_Fixe.ipynb.
- Added numpy import in TP1_EDO_EulerExp.ipynb, TP2_Lokta_Volterra.ipynb, and TP3_Convergence.ipynb for better readability.
- Modified for loops in TP1_EDO_EulerExp.ipynb and TP2_Lokta_Volterra.ipynb to include strict=False for compatibility with future Python versions.

M1/Statistical Learning/TP2_KNN.ipynb

@@ -124,7 +124,9 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "## 1. K-NN classification for  `Iris`  <a class=\"anchor\" id=\"chapter1\"></a>"
+   "source": [
+    "## 1. K-NN classification for  `Iris`  <a class=\"anchor\" id=\"chapter1\"></a>"
+   ]
   },
   {
    "attachments": {
@@ -331,7 +333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2025-02-07T16:32:18.698079Z",
@@ -355,8 +357,9 @@
     }
    ],
    "source": [
-    "#  np.argsort\n",
+    "from collections import Counter\n",
     "\n",
+    "#  np.argsort\n",
     "distance_ex = np.array([4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 0.5, 0.2])\n",
     "print(\n",
     "    \"The indices where the 4 smallest digits are located are \\n\",\n",
@@ -367,9 +370,6 @@
     "print(\"\\n\")\n",
     "\n",
     "# counter.most_common()\n",
-    "\n",
-    "from collections import Counter\n",
-    "\n",
     "print(\n",
     "    \"In 'aabbbbccccccc', the frequencies of the letters are : \\n\",\n",
     "    Counter(\"aabbbbccccccc\").most_common(),\n",
@@ -1274,9 +1274,10 @@
    },
    "outputs": [],
    "source": [
+    "from itertools import product\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "from itertools import product\n",
     "from sklearn.neighbors import KNeighborsClassifier"
    ]
   },
@@ -1411,7 +1412,7 @@
     "f, axarr = plt.subplots(2, 3, sharex=\"col\", sharey=\"row\", figsize=(15, 12))\n",
     "\n",
     "for idx, clf, tt in zip(\n",
-    "    product([0, 1, 2], [0, 1, 2]), KNNs, [f\"KNN (k={k})\" for k in nb_neighbors]\n",
+    "    product([0, 1, 2], [0, 1, 2]), KNNs, [f\"KNN (k={k})\" for k in nb_neighbors], strict=False\n",
     "):\n",
     "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
     "    Z = Z.reshape(xx.shape)\n",