Refactor error messages and function signatures across multiple notebooks for clarity and consistency

- Updated error messages in Gauss method and numerical methods to use variables for better readability.
- Added return type hints to function signatures in various notebooks to improve code documentation.
- Corrected minor grammatical issues in docstrings for better clarity.
- Adjusted print statements and list concatenations for improved output formatting.
- Enhanced plotting functions to ensure consistent figure handling.

M2/Deep Learning/TP3 - Compléments/TP3 - Starter.ipynb

@@ -295,7 +295,7 @@
    ],
    "source": [
     "def get_model() -> keras.Model:\n",
-    "    model = keras.Sequential(\n",
+    "    return keras.Sequential(\n",
     "        [\n",
     "            keras.layers.InputLayer(shape=(32, 32, 3)),\n",
     "            keras.layers.Conv2D(\n",
@@ -330,7 +330,6 @@
     "        ],\n",
     "    )\n",
     "\n",
-    "    return model\n",
     "\n",
     "\n",
     "model = get_model()\n",
@@ -371,14 +370,12 @@
     "        metrics=[\"accuracy\"],\n",
     "    )\n",
     "\n",
-    "    history = model.fit(\n",
+    "    return model.fit(\n",
     "        X_train,\n",
     "        y_train,\n",
     "        validation_data=(X_valid, y_valid),\n",
     "        **kwargs,\n",
-    "    )\n",
-    "\n",
-    "    return history"
+    "    )\n"
    ]
   },
   {

M2/Deep Learning/TP4 - Récurrents/TP4 - Starter.ipynb

@@ -177,7 +177,7 @@
     "import _pickle as pickle\n",
     "\n",
     "train_size = 0.8\n",
-    "train_index = int(round(len(sequences) * train_size))\n",
+    "train_index = round(len(sequences) * train_size)\n",
     "X_train = X[:train_index, :, :]\n",
     "y_train = y[:train_index, :]\n",
     "\n",
@@ -471,7 +471,7 @@
    },
    "outputs": [],
    "source": [
-    "def save_model(model, name):\n",
+    "def save_model(model, name) -> None:\n",
     "    \"\"\"Save a Keras model to JSON and H5 files.\"\"\"\n",
     "    model_json = model.to_json()\n",
     "    with open(name + \".json\", \"w\") as json_file:\n",

M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb

@@ -226,7 +226,7 @@
    "source": [
     "print(range(4, 10))\n",
     "print(range(5, 50, 3))\n",
-    "print([3, 1, 4] + [1, 5, 9])\n",
+    "print([3, 1, 4, 1, 5, 9])\n",
     "print(len(range(4, 10)))"
    ]
   },
@@ -282,7 +282,7 @@
     "\n",
     "print(num in [1, 2, 3] and num > 0)\n",
     "\n",
-    "print(not 5 == 3)"
+    "print(5 != 3)"
    ]
   },
   {

M2/Reinforcement Learning/Lab 2 - Maze Game as a Markov Decision Process Part 1.ipynb

@@ -381,7 +381,7 @@
     }
    ],
    "source": [
-    "def plot_maze_with_states():\n",
+    "def plot_maze_with_states() -> None:\n",
     "    \"\"\"Plot the maze with state indices.\"\"\"\n",
     "    grid = np.ones(\n",
     "        (n_rows, n_cols),\n",
@@ -391,7 +391,7 @@
     "            if maze_str[i][j] == \"#\":\n",
     "                grid[i, j] = 0  # We replace walls (#) with 0\n",
     "\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _fig, ax = plt.subplots()\n",
     "    ax.imshow(grid, cmap=\"gray\", alpha=0.7)\n",
     "\n",
     "    # Plot state indices\n",
@@ -1142,7 +1142,7 @@
     "        ]  # For each reachable cell, we write the value V[s] in the grid.\n",
     "        # Walls # never get values, and they stay as NaN.\n",
     "\n",
-    "    fig, ax = plt.subplots()\n",
+    "    _fig, ax = plt.subplots()\n",
     "    im = ax.imshow(grid_values, cmap=\"magma\")\n",
     "    plt.colorbar(im, ax=ax)\n",
     "\n",

M2/Reinforcement Learning/Lab 2 - Second maze.ipynb

@@ -186,7 +186,7 @@
     }
    ],
    "source": [
-    "def plot_maze_with_states():\n",
+    "def plot_maze_with_states() -> None:\n",
     "    \"\"\"Plot the maze with state indices.\"\"\"\n",
     "    grid = np.ones(\n",
     "        (n_rows, n_cols),\n",
@@ -196,7 +196,7 @@
     "            if maze_str[i][j] == \"#\":\n",
     "                grid[i, j] = 0  # We replace walls (#) with 0\n",
     "\n",
-    "    fig, ax = plt.subplots(figsize=figsize)\n",
+    "    _fig, ax = plt.subplots(figsize=figsize)\n",
     "    ax.imshow(grid, cmap=\"gray\", alpha=0.7)\n",
     "\n",
     "    # Plot state indices\n",
@@ -565,7 +565,7 @@
     "        ]  # For each reachable cell, we write the value V[s] in the grid.\n",
     "        # Walls # never get values, and they stay as NaN.\n",
     "\n",
-    "    fig, ax = plt.subplots(figsize=figsize)\n",
+    "    _fig, ax = plt.subplots(figsize=figsize)\n",
     "    im = ax.imshow(grid_values, cmap=\"magma\")\n",
     "    plt.colorbar(im, ax=ax)\n",
     "\n",