kmeans_yolo_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a956ed0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.cluster.vq import vq, kmeans, whiten\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "51931ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv(\"./test.csv\")\n",
    "train_df = pd.read_csv(\"./train.csv\")\n",
    "valid_df = pd.read_csv(\"./valid.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "414fb988",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new widths\n",
    "test_df[\"box_width\"] = test_df[\"xmax\"]-test_df[\"xmin\"]\n",
    "test_df[\"box_height\"] = test_df[\"ymax\"]-test_df[\"ymin\"]\n",
    "\n",
    "train_df[\"box_width\"] = train_df[\"xmax\"]-train_df[\"xmin\"]\n",
    "train_df[\"box_height\"] = train_df[\"ymax\"]-train_df[\"ymin\"]\n",
    "\n",
    "valid_df[\"box_width\"] = valid_df[\"xmax\"]-valid_df[\"xmin\"]\n",
    "valid_df[\"box_height\"] = valid_df[\"ymax\"]-valid_df[\"ymin\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2d6291a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_isolate = test_df[[\"box_width\", \"box_height\"]]\n",
    "train_isolate = train_df[[\"box_width\", \"box_height\"]]\n",
    "valid_isolate = valid_df[[\"box_width\", \"box_height\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "841089e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Valid width and height')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "fig.suptitle('test, train, valid')\n",
    "\n",
    "ax1.scatter(test_isolate[\"box_width\"], test_isolate[\"box_height\"])\n",
    "ax1.set_yticks([*range(0, 150, 25)])\n",
    "ax1.set_xticks([*range(0, 150, 25)])\n",
    "ax1.set_aspect(\"equal\")\n",
    "ax1.set_title(\"Test width and height\")\n",
    "\n",
    "fig, ax2 = plt.subplots()\n",
    "ax2.scatter(train_isolate[\"box_width\"], train_isolate[\"box_height\"])\n",
    "ax2.set_yticks([*range(0, 150, 25)])\n",
    "ax2.set_xticks([*range(0, 150, 25)])\n",
    "ax2.set_aspect(\"equal\")\n",
    "ax2.set_title(\"Train width and height\")\n",
    "\n",
    "\n",
    "fig, ax3 = plt.subplots()\n",
    "ax3.scatter(valid_isolate[\"box_width\"], valid_isolate[\"box_height\"])\n",
    "ax3.set_yticks([*range(0, 150, 25)])\n",
    "ax3.set_xticks([*range(0, 150, 25)])\n",
    "ax3.set_aspect(\"equal\")\n",
    "ax3.set_title(\"Valid width and height\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3396597b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.5359559613887035, intercept=6.791838297442851, rvalue=0.8713552009271357, pvalue=5.313485208167046e-33, stderr=0.030029453042896323, intercept_stderr=1.089410071630137)\n",
      "LinregressResult(slope=0.7953199427333166, intercept=8.654557606871524, rvalue=0.6828295083655842, pvalue=1.362037024128719e-185, stderr=0.023202506160475804, intercept_stderr=0.8723687116217632)\n",
      "LinregressResult(slope=0.845244631360196, intercept=0.946385138519652, rvalue=0.8513594954558641, pvalue=4.576051503197446e-50, stderr=0.03971176388966724, intercept_stderr=1.3103256719034588)\n"
     ]
    }
   ],
   "source": [
    "train_results = linregress(train_isolate[\"box_width\"], train_isolate[\"box_height\"])\n",
    "\n",
    "test_results = linregress(test_isolate[\"box_width\"], test_isolate[\"box_height\"])\n",
    "\n",
    "valid_results = linregress(valid_isolate[\"box_width\"], valid_isolate[\"box_height\"])\n",
    "\n",
    "\n",
    "print(test_results)\n",
    "print(train_results)\n",
    "print(valid_results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bf91973b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>box_width</th>\n",
       "      <th>box_height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>55</td>\n",
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       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
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       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1342</th>\n",
       "      <td>31</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>1343</th>\n",
       "      <td>30</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>32</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1346</th>\n",
       "      <td>65</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1347 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      box_width  box_height\n",
       "0            82          61\n",
       "1            75          55\n",
       "2            18          37\n",
       "3             8          32\n",
       "4            46          43\n",
       "...         ...         ...\n",
       "1342         31          50\n",
       "1343         30          53\n",
       "1344         32          52\n",
       "1345         31          51\n",
       "1346         65          46\n",
       "\n",
       "[1347 rows x 2 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_isolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f482da3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>box_width</th>\n",
       "      <th>box_height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53</td>\n",
       "      <td>41</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>44</td>\n",
       "      <td>37</td>\n",
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       "    <tr>\n",
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       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>41</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>51</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1621</th>\n",
       "      <td>52</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1622</th>\n",
       "      <td>55</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>52</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1624 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      box_width  box_height\n",
       "0            47          37\n",
       "1            53          41\n",
       "2            44          37\n",
       "3            42          32\n",
       "4            41          38\n",
       "...         ...         ...\n",
       "1619         51          82\n",
       "1620         52          56\n",
       "1621         52          56\n",
       "1622         55          36\n",
       "1623         52          41\n",
       "\n",
       "[1624 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sum up results of test, train valid sets\n",
    "complete_set = None\n",
    "complete_set = pd.concat([test_isolate, train_isolate, valid_isolate])\n",
    "complete_set = complete_set.reset_index(drop=True)\n",
    "complete_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c06d5eb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Complete set')"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph the complete set of data\n",
    "\n",
    "fig, ax4 = plt.subplots()\n",
    "fig.suptitle('test, train, valid')\n",
    "\n",
    "ax4.scatter(test_isolate[\"box_width\"], test_isolate[\"box_height\"])\n",
    "ax4.set_aspect(\"equal\")\n",
    "ax4.set_xlabel(\"Widths\")\n",
    "ax4.set_ylabel(\"heights\")\n",
    "ax4.set_title(\"Complete set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c10a55a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.788277766515715, intercept=7.649891266452862, rvalue=0.7052257515799225, pvalue=1.5113809893609685e-244, stderr=0.01967711400147661, intercept_stderr=0.7290148582903162)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get linear regression of the complete set\n",
    "\n",
    "linregress(complete_set[\"box_width\"], complete_set[\"box_height\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4988e546",
   "metadata": {},
   "outputs": [],
   "source": [
    "# start k-means clustering\n",
    "\n",
    "# pandas frame needs to be floats in order for this to work\n",
    "complete_set.box_width = complete_set[\"box_width\"].astype(float)\n",
    "complete_set.box_height = complete_set[\"box_height\"].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "7b3fb10b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# start k-means clustering\n",
    "\n",
    "clusters = kmeans(complete_set, 4)\n",
    "\n",
    "clusters = clusters[0]\n",
    "xval = [x[0] for x in clusters]\n",
    "yval = [x[1] for x in clusters]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3de41b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Complete set')"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H5gP7Ap2pCQNgEtDdhNjMzJqmGQn5MWAfSWMlCTgQuB+4CfibtM9xwLVNiM3MrGma0YZ8G9nNuzuApSmGecCXgM9Legh4LXBBo2OzOrjsMpgyBUaNyr5edlmzIzIrrNF97zL0IuJ04PSy4oeBdzYhHKuXyy6D44+HtWuz9eXLs3WAmTObF5dZQflJPaufU099NRmXrF2blZvZFpyQrX4ee6x/5WYtzgnZ6mfy5P6Vm7U4J2Srn69/HcaO3bxs7Nis3My24IRs9TNzJsybB7vsAlL2dd4839Azq6IpvSyshcyc6QRsViPXkM3MCqKmhCzpbyVtm5ZPkzRf0t71Dc3MrLXUWkP+ckS8IOm9ZGNRXACcV7+wzMxaT60JeWP6+kFgXkRcB3hGDzOzIVRrQu6W9H3g74DrJY3px7FmZlaDWpPqR4BFwPSI6AF2AGbVKygzs1ZUa0L+fkTMj4gHASJiFfCx+oVlZtZ6ak3IU/MrktqAdwx9OGZmravXhCxptqQXgLdKWpNeLwBP4QHkzcyGVK8JOSLOiohtgbkRsV16bRsRr42I2Q2K0cysJdT06HREzJbUBeySPyYibqlXYGZmraamhCxpDnAU2dx3pT7JATghm5kNkVoHF/prYPeIeKmewZiZtbJae1k8DLTXMxAzs1bXaw1Z0r+RNU2sBe6SdCOwqZYcEZ+tb3hmZq2jryaLJenr7cDCOsdiZtbSek3IEXFxowIxM2t1tfayWErWdJH3PFkN+syIeKY/byqpEzgfeEs67yeBZcCVwBTgUeAjEfFcf85rZjac1XpT76fAdcDM9PoxWTJ+ArhoAO97LnBDRLwJeBvwAHAKcGNE7AbcmNbNzFpGrd3eDoqI/AwhSyXdERF7SzqmP28oaXtgP+DjABHxMvCypCOA/dNuFwM3A1/qz7nNzIazWmvIbZLeWVqR9BdAW1rd0M/33BVYDfyHpDslnS9pG2DHNIocZDXvHSsdLOl4SUskLVm9enU/39rMrLhqTch/D1wg6RFJj5JN4fS/UiI9q5/vORrYGzgvIvYCXqSseSIigi3brEvb5kXEtIiYNmHChH6+tZlZcdU6lsXvgD1TcwMR8Xxu81X9fM8VwIqIuC2tX02WkJ+UtFNErJK0E9mIcmZmLaOvB0OOiYhLJX2+rByAiPhmf98wIp6Q9Lik3SNiGXAg2RgZ9wPHAXPSVw/vaWYtpa8a8jbp67ZD/L7/CFwmaSuyx7I/QdZ8cpWkTwHLyaaNMjNrGX09GPL99PWrQ/mmEXEXMK3CpgOH8n3MzIaTmm7qSXqjpBsl3ZvW3yrptPqGZmbWWmrtZfHvwGxgPUBE3EM2PrKZmQ2RWhPy2Ij4bVlZf/sfm5lZL2pNyE9LegOpb7CkvwFW9X6ImZn1R62PTp8AzAPeJKkbeIRsTAszMxsitSbkbuA/gJuAHYA1ZH2Fv1anuMzMWk6tCflaoAe4A1hZt2jMzFpYrQl5UkQcXNdIzMxaXK039W6VtGddIzEza3F9jWVRmilkNPAJSQ+TTXIqskHZ3lr/EM3MWkNfTRaHNSQKMzPrcyyL5Y0KxMys1dXahmxmZnXmhGxmVhBOyGZmBeGEbGZWEE7IZmYFUeuTetZCFtzZzdxFy1jZs46JnR3Mmr47M/bqanZYZkOuaD/rTsi2mdMWLOWFCy7mylsuYeKap1m53Xi+fdvH4SsnDsukXLRfOCuOBXd2M3v+Utat3whAd886Zs9fCtC0nxE3WdgmC+7s5oULLuasG77DpDWrGUUwac1qvvaTf+WuOd9tdnj9VvqF6+5ZR/DqL9yCO7ubHZoVwNxFyzYl45J16zcyd9GyJkXkhGxkiWvfOb/kpCvvYtYtlzB2w0ubbR+74SX+/obzmxTdwBXxF86KY2XPun6VN4ITcovL1yIBJq55uuJ+1cqLrIi/cFYcEzs7+lXeCE7ILa68Frlyu/EV9/vTThO3KCvVrHc95Tr2nfPLwjUFFPEXzopj1vTd6Whv26yso72NWdN3b1JETUzIktok3SnpJ2l9V0m3SXpI0pWStmpWbK2kvLZ4zn7Hsnb0mM3KXh6zNWPnnr1Z2XBony3iL5wVx4y9ujjryD3p6uxAQFdnB2cduWfL9rI4EXgA2C6tnw18KyKukPQ94FPAec0KrlVM7OzY1FwBsHDqAQCcnHpZ/GmniVkynrn5FIq9tc8WpRdDKQ73srBqZuzVVaifB0VE499UmgRcDHwd+DzwIWA18LqI2CDp3cAZETG9t/NMmzYtlixZUvd4R7IFd3Zz0pV3Vdwm4JE5H6y4bddTrqPST05vx5iNdJJuj4hpAz2+WTXkbwMnA9um9dcCPRGxIa2vACr+2ZJ0PHA8wOTJk+sb5QhUqV/uuLHtPLd2/Rb79tbWWl6zruWYwXB/YmsFDW9DlnQY8FRE3D6Q4yNiXkRMi4hpEyZMGOLoRrZq7b4ffOtO/W5rbWT77HBorzYbCs24qbcvcLikR4ErgPcD5wKdkko19kmAf9uGWLV23+vuWcWY0a/+KGyzVRtjRo/ic1feVbX3RCNviLg/sbWKhjdZRMRsYDaApP2BL0bETEk/BP6GLEkfB1zb6NhGumr9b8ubK158eSPQ9+OkjbohUqlppLdys+GqSP2QvwR8XtJDZG3KFzQ5nhFnoO27za6Ntkn9Kjcbrpo6uFBE3AzcnJYfBt7ZzHhGulnTd99sMJX+KNWum3FzbWOVnkDVys2GqyLVkK3OKrX7dna013TsxM6Opt1c66pSs69WbjZcefjNAqu1NtqfWmt5u29v/ZBLSr0nmvUwSKWavZ+4s5HICbmgah2rtbf9lix/lstve3yzf+27yhL2jL26+OqP76vYD7l8/89VSdz1HqzHT9xZq3BCLqhaa6PV9ps9/x7WrX9li/NWSuynf2hqxRpoeTe2Rj8Mkle0R1zN6sFtyAVV69CR1farlIxf3baRL1x196ZR2oDN2pbHjW2v2A/Zg/WY1ZdryAVVa2202n59KTVjdPesY9YP7+Y1W4+mZ+16tu9o548vbWD9xle3l9eo3XRgVh9NGVxoqIzkwYXK24ahcjNCtf1e2rCRV4bwW9vV2cGvT3n/0J3QbAQaroMLWR+WLH+W6Xf/gi/+KhsGc9V24/nOQZ/kc6kduVQzrVZrXbL8WS5d/NiQxZNvGvFAP2b14YRcQKctWMqaCy5mzg3f2TS/Xdea1Xx54bd58eANLJx6wGbNCNVueP3n4seo3pLcP6WmkiLO1Gs2UvimXkHkp0O6dPFjnFxlstGTb7kE6Ptx5rmLlg1ZMs7fuPNAP2b144RcAOVPwEFtk41W6mFRSuy93eg7Zp/Jm3pUdHa00962+ZgQ7aPEuLHtFUdx88ShZvXjJosCqFTrXLndeCatWb3FvvlJSMt7XFS6wVeuq7ODM2fsucVxtbYJN7MvstlI54TcYJWSX6Xa5Tn7HbtZGzLA2tFjOGe/Y4HK/X8rJfa8an2G+/PQhR9jNqsfJ+QGqnZDbPuOdnrWbf7ocvlkoyu3G885+x3LwqkH0NnRzhmHT90iifbWbFDtmP5yX2Sz+nFCbqBqN8S2bh9FR3vbFtsWTj1gU2LOe2lD5dt1vT0kss2Y0UOWNP0Ys1l9+KZeA1WrwfasXb/FsJi9Ke/VUMuNPN90Mys+15AbqLcbYuW1zimnXNfrufIDxtcy6LxvupkVn2vIDZCvwZZPOlTthti4sb0PHF9KsH3dyOvtPcysWJyQ6yzfxxggYFNS7m2m5tM/NHWL/sEl+QTbW1NEvWeDNrOh5SaLOqtUgw36Hqwn35uhu2cdbRIbI7YYYL5zbHvFweXHjW3nzq98YOg+iJnVnRNyndX6ZFu1hzP6qtlWG6xvGA/iZ9aynJDrrJYn2/oasCefrDvHthMBz69bz8TOji36L5c8X6XczIrLbch1VsssG70N2FM+zsVza9fTs279plmfK7cyu1eF2XDU8IQsaWdJN0m6X9J9kk5M5TtI+rmkB9PXcY2OrR5m7NXFWUfuSWfHq70mtm7f/LL31qzRVy+K/E3CEveqMBuemlFD3gB8ISL2APYBTpC0B3AKcGNE7AbcmNZHjPzTdc+tXc/s+Us3zVVXrTY7sbOjpgc6SjcJ3avCbHhreBtyRKwCVqXlFyQ9AHQBRwD7p90uBm4GvtTo+Orhqz++r2KTxBeuuhvImjVmXX33pnnsANrbxKzpu2/qZdEbT69kNjI09aaepCnAXsBtwI4pWQM8AexY5ZjjgeMBJk+e3IAo+yd/A277jnbWb3yFF1+u3OSwMYLZ85fy4Xd0QXmviLReaXS1PDdPmI0cTbupJ+k1wI+AkyJiTX5bZDOvVuy4FRHzImJaREybMGFCAyKtXfkNuJ5166sm45J16zdy+W2Ps75sRtL1rwRzFy3b1AZdapIYN7adzo7Kg8eb2fDWlBqypHayZHxZRMxPxU9K2ikiVknaCXiqGbENRi2PMVeysUqn4e6edSy4s9ujq5m1iIYnZEkCLgAeiIhv5jYtBI4D5qSv1zY6tv4qf5ijr7beakpP4VXiCUTNWkczmiz2BT4GvF/SXel1KFki/itJDwIHpfXCKm+eGGgy7mhv4+h37bxFX+USTyBq1jqa0cviv9my62zJgY2MZTAG2jxRrtQGPG2XHTjpyrsq7uOxjM1ag5/UG6ChSJJdaRxkyJokqg1M76fuzFqDx7KoUXl7caV58PpDsEV3NU8gatbanJBrUGnwn7ZRW7a6HH7fTRUnJa0k2PJGnScQNWttTsg1qNRevLGs3/Dh993EnBu+w9gNLwEwac1q5tzwHYCKSbla84S7uJm1Lrch16CW9uKTb7lkUzIuGbvhJU6+5ZIt9nUzhJlV4oRcg1puqk1c83TV8s6Odg/+Y2Z9cpNFDQ540wQuXfxYr/us3G48k9as3qJ81fYTOOPwqU7AZtYn15BrcM0d3X3uc85+x7J29JjNytaOHsPKWV92MjazmriG3IfTFiztc4AgePXGXb6Xxdz9juXcUz9T7xDNbIRwQq5iwZ3dnLHwvn71NV449YDNelRU60lhZlaJE3IFC+7sZtYP795iSMz+ck8KM+sPtyFXMHfRskEn486Odrcdm1m/OCFXMNhxKjra2zjj8KlDFI2ZtQon5AoGM5hPm+R+xmY2IE7IFRzwpoFPDfWNj7zNydjMBsQJuYKbfr/lAx61OGafyU7GZjZg7mVRwUDakL/9d293MjazQXENuYKBtCE7GZvZYDkhVzBr+u5V57gzM6sXN1lUkB8ovpbJS9tUbYpAM7PauYZcxYy9uvj1Ke+vad+j37VznaMxs1bghNyH3sajaJM4Zp/JnDljzwZGZGYjlZss+lBt4lE//GFmQ61wCVnSwcC5QBtwfkTMGapzl88cXcsEop541MwapVAJWVIb8F3gr4AVwO8kLYyI+wd77kozR8+evxTou8uaJx41s0YoWhvyO4GHIuLhiHgZuAI4YihOXGnm6HXrNzJ30bKhOL2Z2aAVLSF3AY/n1leksk0kHS9piaQlq1fX/ohztafvBjuym5nZUClaQu5TRMyLiGkRMW3ChNoHAar29N1gRnYzMxtKRUvI3UC+U++kVDZolZ6+62hv86weZlYYhbqpB/wO2E3SrmSJ+Cjgo0NxYveWMLOiK1RCjogNkj4DLCLr9nZhRNw3VOd3bwkzK7JCJWSAiLgeuL7ZcZiZNVrR2pDNzFqWE7KZWUE4IZuZFYQTsplZQSgimh3DgElaDSwfwKHjgaeHOJyhUuTYwPENluMbnCLHNx7YJiIGPG39sE7IAyVpSURMa3YclRQ5NnB8g+X4BqfI8Q1FbG6yMDMrCCdkM7OCaNWEPK/ZAfSiyLGB4xssxzc4RY5v0LG1ZBuymVkRtWoN2cyscFoqIUs6WNIySQ9JOqUA8ews6SZJ90u6T9KJqfwMSd2S7kqvQ5sY46OSlqY4lqSyHST9XNKD6eu4JsW2e+4a3SVpjaSTmnn9JF0o6SlJ9+bKKl4vZf41/TzeI2nvJsQ2V9Lv0/tfI6kzlU+RtC53Db9Xz9h6ia/q91LS7HTtlkma3qT4rszF9qiku1L5wK5fRLTEi2z0uD8Arwe2Au4G9mhyTDsBe6flbYH/AfYAzgC+2OxrluJ6FBhfVnYOcEpaPgU4uwBxtgFPALs08/oB+wF7A/f2db2AQ4GfAgL2AW5rQmwfAEan5bNzsU3J79fEa1fxe5l+T+4GxgC7pt/ttkbHV7b9G8BXBnP9WqmGXLf5+gYqIlZFxB1p+QXgAcqmrCqoI4CL0/LFwIzmhbLJgcAfImIgDwoNmYi4BXi2rLja9ToCuCQyi4FOSTs1MraI+FlEbEiri8kmhWiKKteumiOAKyLipYh4BHiI7He8bnqLT5KAjwCXD+Y9Wikh9zlfXzNJmgLsBdyWij6T/o28sFlNAkkAP5N0u6TjU9mOEbEqLT8B7Nic0DZzFJv/MhTl+kH161W0n8lPktXYS3aVdKekX0l6X7OCovL3smjX7n3AkxHxYK6s39evlRJyYUl6DfAj4KSIWAOcB7wBeDuwiuxfoWZ5b0TsDRwCnCBpv/zGyP4/a2pXHUlbAYcDP0xFRbp+mynC9apE0qnABuCyVLQKmBwRewGfB/5T0nZNCK2w38syR7N5hWBA16+VEnLd5usbDEntZMn4soiYDxART0bExoh4Bfh36vyvWG8iojt9fQq4JsXyZOlf6/T1qWbFlxwC3BERT0Kxrl9S7XoV4mdS0seBw4CZ6Q8GqSngmbR8O1kb7RsbHVsv38tCXDsASaOBI4ErS2UDvX6tlJA3zdeXalRHAQubGVBqd7oAeCAivpkrz7cj/jVwb/mxjSBpG0nblpbJbgDdS3bdjku7HQdc24z4cjarnRTl+uVUu14LgWNTb4t9gOdzTRsNIelg4GTg8IhYmyufIKktLb8e2A14uJGxpfeu9r1cCBwlaYyyOTh3A37b6PiSg4DfR8SKUsGAr1+j7qAW4UV2V/t/yP5anVqAeN5L9u/rPcBd6XUo8ANgaSpfCOzUpPheT3Yn+27gvtI1A14L3Ag8CPwC2KGJ13Ab4Blg+1xZ064f2R+GVcB6snbNT1W7XmS9K76bfh6XAtOaENtDZG2xpZ+/76V9P5y+53cBdwAfatK1q/q9BE5N124ZcEgz4kvlFwGfLtt3QNfPT+qZmRVEKzVZmJkVmhOymVlBOCGbmRWEE7KZWUE4IZuZFYQTsg17kr4l6aTc+iJJ5+fWvyHpK6oywp+kP6avUyR9NFf+cUnfqWPoZptxQraR4NfAewAkjSKb/Xdqbvt7gJ9FxJw+zjMF+Ggf+5jVjROyjQS3Au9Oy1PJnuZ6QdI4SWOANwNvLdV209Oav1E2zvOZufPMAd6Xxq/9XCqbKOkGZWMZn5OOb5N0kaR70zk+h9kQGN3sAMwGKyJWStogaTJZbfg3ZCN/vRt4nuxJr5dzh5wLnBcRl0g6IVd+CtnYu4fBpjEe3k42Ct9LwDJJ/wb8GdAVEW9J+3XW79NZK3EN2UaKW8mScSkh/ya3/uuyfffl1bEvftDHeW+MiOcj4k/A/WQD4D8MvF7Sv6WxINYMzUewVueEbCNFqR15T7Imi8VkNeT3kCXrcrWOGfBSbnkj2ewazwFvA24GPg2cX+E4s35zQraR4layISSfjWy4xmeBTrKkXJ6Qf0022h/AzFz5C2RTafVK0nhgVET8CDiNbFofs0FzQraRYilZ74rFZWXPR8TTZfueSDbY/lI2n2XiHmCjpLv7uFHXBdycJrS8FJg92ODNAI/2ZmZWFK4hm5kVhBOymVlBOCGbmRWEE7KZWUE4IZuZFYQTsplZQTghm5kVhBOymVlB/H8Ts9chFG1Q6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, kmn = plt.subplots()\n",
    "fig.suptitle('kmeans overlapped')\n",
    "\n",
    "kmn.scatter(test_isolate[\"box_width\"], test_isolate[\"box_height\"])\n",
    "kmn.scatter(xval, yval, color='r')\n",
    "kmn.set_aspect(\"equal\")\n",
    "kmn.set_xlabel(\"Widths\")\n",
    "kmn.set_ylabel(\"heights\")\n",
    "kmn.set_title(\"Complete set\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}