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gabriel.marinoja
2025-09-23 14:30:36 +02:00
parent f0e1453d13
commit 36ad88bca9
2 changed files with 85 additions and 43 deletions
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10
PW-2/ex1-numpy/numpy-tutorial-stud.ipynb
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@@ -931,7 +931,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 24, "execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@@ -954,19 +954,21 @@
"\n", "\n",
"plt.figure(figsize=(15, 5))\n", "plt.figure(figsize=(15, 5))\n",
"\n", "\n",
"# RGB format : img[red, green, blue] -> img[:,:,0], img[:,:,1], img[:,:,2]\n",
"\n",
"# Red channel\n", "# Red channel\n",
"plt.subplot(1, 3, 1)\n", "plt.subplot(1, 3, 1)\n",
"plt.imshow(img[:, :, 0], cmap='Reds')\n", "plt.imshow(img[:,:,0], cmap='Reds')\n",
"plt.title('Red Channel')\n", "plt.title('Red Channel')\n",
"\n", "\n",
"# Green channel\n", "# Green channel\n",
"plt.subplot(1, 3, 2)\n", "plt.subplot(1, 3, 2)\n",
"plt.imshow(img[:, :, 1], cmap='Greens')\n", "plt.imshow(img[:,:,1], cmap='Greens')\n",
"plt.title('Green Channel')\n", "plt.title('Green Channel')\n",
"\n", "\n",
"# Blue channel\n", "# Blue channel\n",
"plt.subplot(1, 3, 3)\n", "plt.subplot(1, 3, 3)\n",
"plt.imshow(img[:, :, 2], cmap='Blues')\n", "plt.imshow(img[:,:,2], cmap='Blues')\n",
"plt.title('Blue Channel')\n", "plt.title('Blue Channel')\n",
"\n", "\n",
"plt.show()\n", "plt.show()\n",

118
PW-2/ex2-to-loan-or-not-to-loan/to-loan-or-not-to-loan-stud.ipynb
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@@ -26,7 +26,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 5,
"id": "26758936", "id": "26758936",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -63,7 +63,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 6,
"id": "a23f62b5", "id": "a23f62b5",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -81,7 +81,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 7,
"id": "f4bec500", "id": "f4bec500",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@@ -186,7 +186,7 @@
"4 Y " "4 Y "
] ]
}, },
"execution_count": 3, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
@@ -228,7 +228,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 8,
"id": "2c56efa5", "id": "2c56efa5",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -246,7 +246,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 9,
"id": "dc5f9cda", "id": "dc5f9cda",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -264,7 +264,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 10,
"id": "83beacfb", "id": "83beacfb",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -283,7 +283,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 11,
"id": "9c567bb7", "id": "9c567bb7",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -301,7 +301,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 12,
"id": "c0db7c1f", "id": "c0db7c1f",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -319,7 +319,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 13,
"id": "b05be2cc", "id": "b05be2cc",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -345,7 +345,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": null,
"id": "30919672", "id": "30919672",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -356,36 +356,20 @@
" \"\"\"\n", " \"\"\"\n",
" Initialize the class.\n", " Initialize the class.\n",
" \"\"\"\n", " \"\"\"\n",
" pass\n", " self.classes_ = None\n",
" \n", " \n",
" def fit(self, X, y):\n", " def fit(self, X, y):\n",
" \"\"\"\n", " \"\"\"\n",
" Fit the dummy classifier.\n", " Fit the dummy classifier.\n",
" \n",
" Parameters\n",
" ----------\n",
" X : Numpy array or Pandas DataFrame of shape (n_samples, n_features)\n",
" Training data.\n",
" y : Numpy array or Pandas DataFrame of shape (n_samples,)\n",
" Target values.\n",
" \"\"\"\n", " \"\"\"\n",
" pass\n", " self.classes_ = np.unique(y)\n",
" \n", " \n",
" def predict(self, X):\n", " def predict(self, X):\n",
" \"\"\"\n", " \"\"\"\n",
" Predict the class labels for the provided data.\n", " Predict the class labels for the provided data.\n",
"\n",
" Parameters\n",
" ----------\n",
" X : Numpy array or Pandas DataFrame of shape (n_queries, n_features)\n",
" Test samples.\n",
"\n",
" Returns\n",
" -------\n",
" y : Numpy array or Pandas DataFrame of shape (n_queries,)\n",
" Class labels for each data sample.\n",
" \"\"\"\n", " \"\"\"\n",
" pass" " n_samples = len(X)\n",
" return np.random.choice(self.classes_, size=n_samples)\n"
] ]
}, },
{ {
@@ -398,7 +382,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 15,
"id": "184f3905", "id": "184f3905",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -441,7 +425,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 16,
"id": "759e924e", "id": "759e924e",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@@ -457,7 +441,10 @@
" n_neighbors : int, default=3\n", " n_neighbors : int, default=3\n",
" Number of neighbors to use by default.\n", " Number of neighbors to use by default.\n",
" \"\"\"\n", " \"\"\"\n",
" pass\n", "\n",
" self.n_neighbors = n_neighbors\n",
" self.X_train = None\n",
" self.y_train = None\n",
" \n", " \n",
" def fit(self, X, y):\n", " def fit(self, X, y):\n",
" \"\"\"\n", " \"\"\"\n",
@@ -470,7 +457,9 @@
" y : Numpy array or Pandas DataFrame of shape (n_samples,)\n", " y : Numpy array or Pandas DataFrame of shape (n_samples,)\n",
" Target values.\n", " Target values.\n",
" \"\"\"\n", " \"\"\"\n",
" pass\n", "\n",
" self.X_train = np.array(X)\n",
" self.y_train = np.array(y)\n",
" \n", " \n",
" @staticmethod\n", " @staticmethod\n",
" def _euclidian_distance(a, b):\n", " def _euclidian_distance(a, b):\n",
@@ -484,7 +473,23 @@
" b : Numpy array or Pandas DataFrame\n", " b : Numpy array or Pandas DataFrame\n",
" Second operand.\n", " Second operand.\n",
" \"\"\"\n", " \"\"\"\n",
" pass\n", "\n",
" return np.sqrt(np.sum((a - b) ** 2, axis=1))\n",
" \n",
" @staticmethod\n",
" def _manhattan_distance(a, b):\n",
" \"\"\"\n",
" Utility function to compute the Manhattan distance.\n",
" \n",
" Parameters\n",
" ----------\n",
" a : Numpy array or Pandas DataFrame\n",
" First operand.\n",
" b : Numpy array or Pandas DataFrame\n",
" Second operand.\n",
" \"\"\"\n",
" \n",
" return np.sum(np.abs(a - b), axis=1)\n",
" \n", " \n",
" def predict(self, X):\n", " def predict(self, X):\n",
" \"\"\"\n", " \"\"\"\n",
@@ -500,7 +505,16 @@
" y : Numpy array or Pandas DataFrame of shape (n_queries,)\n", " y : Numpy array or Pandas DataFrame of shape (n_queries,)\n",
" Class labels for each data sample.\n", " Class labels for each data sample.\n",
" \"\"\"\n", " \"\"\"\n",
" pass" " X = np.array(X)\n",
" predictions = []\n",
" for x in X:\n",
" distances = self._euclidian_distance(self.X_train, x)\n",
" neighbor_idxs = np.argsort(distances)[:self.n_neighbors]\n",
" neighbor_labels = self.y_train[neighbor_idxs]\n",
" \n",
" counts = np.bincount(neighbor_labels.astype(int))\n",
" predictions.append(np.argmax(counts))\n",
" return np.array(predictions)"
] ]
}, },
{ {
@@ -511,6 +525,32 @@
"Compute the performance of the system as a function of $k = 1...7$." "Compute the performance of the system as a function of $k = 1...7$."
] ]
}, },
{
"cell_type": "code",
"execution_count": 18,
"id": "4214d221",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"KNN accuracies for k=1 to 7: [None, None, None, None, None, None, None]\n"
]
}
],
"source": [
"knn_accuracies = []\n",
"for k in range(1, 8):\n",
" knn = KNNClassifier(n_neighbors=k)\n",
" knn.fit(X_train, y_train)\n",
" y_pred = knn.predict(X_test)\n",
" acc = accuracy_score(y_test, y_pred)\n",
" knn_accuracies.append(acc)\n",
"\n",
"print(\"KNN accuracies for k=1 to 7:\", knn_accuracies)"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
@@ -578,7 +618,7 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3 (ipykernel)", "display_name": "MLvenv",
"language": "python", "language": "python",
"name": "python3" "name": "python3"
}, },
@@ -592,7 +632,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.11" "version": "3.10.18"
} }
}, },
"nbformat": 4, "nbformat": 4,