Refactor code for improved readability and consistency across multiple Jupyter notebooks

- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.

M1/Numerical Methods/TP2.ipynb

@@ -38,6 +38,7 @@
    ],
    "source": [
     "import numpy as np\n",
+    "\n",
     "from sklearn.datasets import make_classification\n",
     "from sklearn.metrics import accuracy_score\n",
     "from sklearn.model_selection import train_test_split\n",
@@ -47,12 +48,18 @@
     "\n",
     "for _ in range(10):\n",
     "    X, y = make_classification(\n",
-    "        n_samples=1000, n_features=4, n_classes=3, n_clusters_per_class=1\n",
+    "        n_samples=1000,\n",
+    "        n_features=4,\n",
+    "        n_classes=3,\n",
+    "        n_clusters_per_class=1,\n",
     "    )\n",
     "\n",
     "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
     "    model = MLPClassifier(\n",
-    "        hidden_layer_sizes=(5, 7), activation=\"relu\", max_iter=10000, solver=\"adam\"\n",
+    "        hidden_layer_sizes=(5, 7),\n",
+    "        activation=\"relu\",\n",
+    "        max_iter=10000,\n",
+    "        solver=\"adam\",\n",
     "    )\n",
     "    model.fit(X_train, y_train)\n",
     "\n",