chore: clean up code structure and remove unused code blocks

M2/Clustering In Practice/TP3.ipynb

@@ -2,10 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 1,
    "id": "31c5786f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n"
+     ]
+    }
+   ],
    "source": [
     "import umap\n",
     "\n",
@@ -16,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 2,
    "id": "5fb57b5d",
    "metadata": {},
    "outputs": [],
@@ -26,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 3,
    "id": "bc49092e",
    "metadata": {},
    "outputs": [
@@ -80,7 +89,7 @@
          "type": "float"
         }
        ],
-       "ref": "56954716-828e-4056-a5a4-8dbdc6447d2d",
+       "ref": "806f15d4-470f-490a-b5a6-78d68fdb491b",
        "rows": [
         [
          "0",
@@ -824,7 +833,7 @@
        "[552 rows x 8 columns]"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -835,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 4,
    "id": "4364612a",
    "metadata": {},
    "outputs": [],
@@ -871,7 +880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 5,
    "id": "c1be071a",
    "metadata": {},
    "outputs": [
@@ -881,7 +890,7 @@
      "text": [
       "Computing t-SNE...\n",
       "[t-SNE] Computing 91 nearest neighbors...\n",
-      "[t-SNE] Indexed 552 samples in 0.001s...\n",
+      "[t-SNE] Indexed 552 samples in 0.002s...\n",
       "[t-SNE] Computed neighbors for 552 samples in 0.020s...\n",
       "[t-SNE] Computed conditional probabilities for sample 552 / 552\n",
       "[t-SNE] Mean sigma: 0.767389\n",
@@ -998,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 6,
    "id": "5276c9b4",
    "metadata": {},
    "outputs": [
@@ -1123,15 +1132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "6abd7740",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 7,
    "id": "3c52eb1a",
    "metadata": {},
    "outputs": [
@@ -1278,7 +1279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 8,
    "id": "60a8df77",
    "metadata": {},
    "outputs": [
@@ -1433,7 +1434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 9,
    "id": "2da3589a",
    "metadata": {},
    "outputs": [
@@ -1483,7 +1484,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "studies",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },