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/TP7_Kmeans.ipynb

@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -41,9 +41,8 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
+    "import numpy as np\n",
     "\n",
     "np.random.seed(12)\n",
     "num_observations = 400\n",
@@ -1554,7 +1553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1574,7 +1573,7 @@
     }
    ],
    "source": [
-    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
     "\n",
     "print(accuracy_score(cluster_to_label, labels))\n",
     "print(confusion_matrix(cluster_to_label, labels))"