Xception.ipynb

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      "── \u001b[1mAttaching core tidyverse packages\u001b[22m ──────────────────────── tidyverse 2.0.0 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mdplyr    \u001b[39m 1.1.0     \u001b[32m✔\u001b[39m \u001b[34mreadr    \u001b[39m 2.1.4\n",
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      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
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      "\u001b[36mℹ\u001b[39m Use the conflicted package (\u001b[3m\u001b[34m<http://conflicted.r-lib.org/>\u001b[39m\u001b[23m) to force all conflicts to become errors\n",
      "Loading required package: lattice\n",
      "\n",
      "\n",
      "Attaching package: ‘caret’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:purrr’:\n",
      "\n",
      "    lift\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:httr’:\n",
      "\n",
      "    progress\n",
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     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'brain-tumor-classification-mri'</li><li>'keras-pretrained-models'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'brain-tumor-classification-mri'\n",
       "\\item 'keras-pretrained-models'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'brain-tumor-classification-mri'\n",
       "2. 'keras-pretrained-models'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"brain-tumor-classification-mri\" \"keras-pretrained-models\"       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# This R environment comes with many helpful analytics packages installed\n",
    "# It is defined by the kaggle/rstats Docker image: https://github.com/kaggle/docker-rstats\n",
    "# For example, here's a helpful package to load\n",
    "library(keras)\n",
    "library(magrittr)\n",
    "library(reticulate)\n",
    "library(tidyverse) # metapackage of all tidyverse packages\n",
    "library(e1071)\n",
    "library(caTools)\n",
    "library(caret)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "list.files(path = \"../input\")\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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    },
    "tags": []
   },
   "source": [
    "# Data Preparation and Organisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3629a42",
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   },
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   "source": [
    "set.seed(1212)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2a81324",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_data_glioma <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/glioma_tumor\")\n",
    "train_data_meningioma <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/meningioma_tumor\")\n",
    "train_data_pituitary <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/pituitary_tumor\")\n",
    "train_data_notmr <- file.path(\"/kaggle/input/brain-tumor-classification-mri/Training/no_tumor\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae1d4f17",
   "metadata": {
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     "start_time": "2023-04-04T17:16:38.215297",
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    },
    "tags": []
   },
   "source": [
    "**Volume of data in each class**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44e56e75",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Glioma ::  826\"\n",
      "[1] \"Meningioma ::  822\"\n",
      "[1] \"No_tumour ::  395\"\n",
      "[1] \"Pituitary ::  827\"\n"
     ]
    }
   ],
   "source": [
    "print(paste(\"Glioma :: \",length(list.files(train_data_glioma))))\n",
    "print(paste(\"Meningioma :: \",length(list.files(train_data_meningioma))))\n",
    "print(paste(\"No_tumour :: \",length(list.files(train_data_notmr))))\n",
    "print(paste(\"Pituitary :: \",length(list.files(train_data_pituitary))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1578b2a6",
   "metadata": {
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     "start_time": "2023-04-04T17:16:38.867421",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Creating various working directories**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7ca7fbe9",
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   "outputs": [],
   "source": [
    "train_dir<-'./train';dir.create(train_dir)\n",
    "test_dir<-'./test';dir.create(test_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d02449a",
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   "outputs": [],
   "source": [
    "train_dir_glioma<-file.path(train_dir,\"glioma\");dir.create(train_dir_glioma)\n",
    "train_dir_meningioma<-file.path(train_dir,\"meningioma\");dir.create(train_dir_meningioma)\n",
    "train_dir_notmr<-file.path(train_dir,\"notmr\");dir.create(train_dir_notmr)\n",
    "train_dir_pituitary<-file.path(train_dir,\"pituitary\");dir.create(train_dir_pituitary)\n",
    "\n",
    "test_dir_glioma<-file.path(test_dir,\"glioma\");dir.create(test_dir_glioma)\n",
    "test_dir_meningioma<-file.path(test_dir,\"meningioma\");dir.create(test_dir_meningioma)\n",
    "test_dir_notmr<-file.path(test_dir,\"notmr\");dir.create(test_dir_notmr)\n",
    "test_dir_pituitary<-file.path(test_dir,\"pituitary\");dir.create(test_dir_pituitary)"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "id": "a3ca359d",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item ⋯\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. ⋯\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "228. TRUE\n",
       "229. TRUE\n",
       "230. TRUE\n",
       "231. TRUE\n",
       "232. TRUE\n",
       "233. TRUE\n",
       "234. TRUE\n",
       "235. TRUE\n",
       "236. TRUE\n",
       "237. TRUE\n",
       "238. TRUE\n",
       "239. TRUE\n",
       "240. TRUE\n",
       "241. TRUE\n",
       "242. TRUE\n",
       "243. TRUE\n",
       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
       "247. TRUE\n",
       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
       "251. TRUE\n",
       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
       "259. TRUE\n",
       "260. TRUE\n",
       "261. TRUE\n",
       "262. TRUE\n",
       "263. TRUE\n",
       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
       "297. TRUE\n",
       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "301. TRUE\n",
       "302. TRUE\n",
       "303. TRUE\n",
       "304. TRUE\n",
       "305. TRUE\n",
       "306. TRUE\n",
       "307. TRUE\n",
       "308. TRUE\n",
       "309. TRUE\n",
       "310. TRUE\n",
       "311. TRUE\n",
       "312. TRUE\n",
       "313. TRUE\n",
       "314. TRUE\n",
       "315. TRUE\n",
       "316. TRUE\n",
       "317. TRUE\n",
       "318. TRUE\n",
       "319. TRUE\n",
       "320. TRUE\n",
       "321. TRUE\n",
       "322. TRUE\n",
       "323. TRUE\n",
       "324. TRUE\n",
       "325. TRUE\n",
       "326. TRUE\n",
       "327. TRUE\n",
       "328. TRUE\n",
       "329. TRUE\n",
       "330. TRUE\n",
       "331. TRUE\n",
       "332. TRUE\n",
       "333. TRUE\n",
       "334. TRUE\n",
       "335. TRUE\n",
       "336. TRUE\n",
       "337. TRUE\n",
       "338. TRUE\n",
       "339. TRUE\n",
       "340. TRUE\n",
       "341. TRUE\n",
       "342. TRUE\n",
       "343. TRUE\n",
       "344. TRUE\n",
       "345. TRUE\n",
       "346. TRUE\n",
       "347. TRUE\n",
       "348. TRUE\n",
       "349. TRUE\n",
       "350. TRUE\n",
       "351. TRUE\n",
       "352. TRUE\n",
       "353. TRUE\n",
       "354. TRUE\n",
       "355. TRUE\n",
       "356. TRUE\n",
       "357. TRUE\n",
       "358. TRUE\n",
       "359. TRUE\n",
       "360. TRUE\n",
       "361. TRUE\n",
       "362. TRUE\n",
       "363. TRUE\n",
       "364. TRUE\n",
       "365. TRUE\n",
       "366. TRUE\n",
       "367. TRUE\n",
       "368. TRUE\n",
       "369. TRUE\n",
       "370. TRUE\n",
       "371. TRUE\n",
       "372. TRUE\n",
       "373. TRUE\n",
       "374. TRUE\n",
       "375. TRUE\n",
       "376. TRUE\n",
       "377. TRUE\n",
       "378. TRUE\n",
       "379. TRUE\n",
       "380. TRUE\n",
       "381. TRUE\n",
       "382. TRUE\n",
       "383. TRUE\n",
       "384. TRUE\n",
       "385. TRUE\n",
       "386. TRUE\n",
       "387. TRUE\n",
       "388. TRUE\n",
       "389. TRUE\n",
       "390. TRUE\n",
       "391. TRUE\n",
       "392. TRUE\n",
       "393. TRUE\n",
       "394. TRUE\n",
       "395. TRUE\n",
       "396. TRUE\n",
       "397. TRUE\n",
       "398. TRUE\n",
       "399. TRUE\n",
       "400. TRUE\n",
       "401. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
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       "401. TRUE\n",
       "\n",
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      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "192. TRUE\n",
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       "194. TRUE\n",
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       "196. TRUE\n",
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       "198. TRUE\n",
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       "200. TRUE\n",
       "201. TRUE\n",
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       "203. TRUE\n",
       "204. TRUE\n",
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       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
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       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
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       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
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       "260. TRUE\n",
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       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
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       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "\\begin{enumerate*}\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
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       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
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       "43. TRUE\n",
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       "51. TRUE\n",
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       "55. TRUE\n",
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       "64. TRUE\n",
       "65. TRUE\n",
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       "71. TRUE\n",
       "72. TRUE\n",
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       "75. TRUE\n",
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       "80. TRUE\n",
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       "88. TRUE\n",
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       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
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       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
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       "119. TRUE\n",
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       "156. TRUE\n",
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       "158. TRUE\n",
       "159. TRUE\n",
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       "181. TRUE\n",
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       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
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       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
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       "203. TRUE\n",
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       "206. TRUE\n",
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       "208. TRUE\n",
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       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "228. TRUE\n",
       "229. TRUE\n",
       "230. TRUE\n",
       "231. TRUE\n",
       "232. TRUE\n",
       "233. TRUE\n",
       "234. TRUE\n",
       "235. TRUE\n",
       "236. TRUE\n",
       "237. TRUE\n",
       "238. TRUE\n",
       "239. TRUE\n",
       "240. TRUE\n",
       "241. TRUE\n",
       "242. TRUE\n",
       "243. TRUE\n",
       "244. TRUE\n",
       "245. TRUE\n",
       "246. TRUE\n",
       "247. TRUE\n",
       "248. TRUE\n",
       "249. TRUE\n",
       "250. TRUE\n",
       "251. TRUE\n",
       "252. TRUE\n",
       "253. TRUE\n",
       "254. TRUE\n",
       "255. TRUE\n",
       "256. TRUE\n",
       "257. TRUE\n",
       "258. TRUE\n",
       "259. TRUE\n",
       "260. TRUE\n",
       "261. TRUE\n",
       "262. TRUE\n",
       "263. TRUE\n",
       "264. TRUE\n",
       "265. TRUE\n",
       "266. TRUE\n",
       "267. TRUE\n",
       "268. TRUE\n",
       "269. TRUE\n",
       "270. TRUE\n",
       "271. TRUE\n",
       "272. TRUE\n",
       "273. TRUE\n",
       "274. TRUE\n",
       "275. TRUE\n",
       "276. TRUE\n",
       "277. TRUE\n",
       "278. TRUE\n",
       "279. TRUE\n",
       "280. TRUE\n",
       "281. TRUE\n",
       "282. TRUE\n",
       "283. TRUE\n",
       "284. TRUE\n",
       "285. TRUE\n",
       "286. TRUE\n",
       "287. TRUE\n",
       "288. TRUE\n",
       "289. TRUE\n",
       "290. TRUE\n",
       "291. TRUE\n",
       "292. TRUE\n",
       "293. TRUE\n",
       "294. TRUE\n",
       "295. TRUE\n",
       "296. TRUE\n",
       "297. TRUE\n",
       "298. TRUE\n",
       "299. TRUE\n",
       "300. TRUE\n",
       "301. TRUE\n",
       "302. TRUE\n",
       "303. TRUE\n",
       "304. TRUE\n",
       "305. TRUE\n",
       "306. TRUE\n",
       "307. TRUE\n",
       "308. TRUE\n",
       "309. TRUE\n",
       "310. TRUE\n",
       "311. TRUE\n",
       "312. TRUE\n",
       "313. TRUE\n",
       "314. TRUE\n",
       "315. TRUE\n",
       "316. TRUE\n",
       "317. TRUE\n",
       "318. TRUE\n",
       "319. TRUE\n",
       "320. TRUE\n",
       "321. TRUE\n",
       "322. TRUE\n",
       "323. TRUE\n",
       "324. TRUE\n",
       "325. TRUE\n",
       "326. TRUE\n",
       "327. TRUE\n",
       "328. TRUE\n",
       "329. TRUE\n",
       "330. TRUE\n",
       "331. TRUE\n",
       "332. TRUE\n",
       "333. TRUE\n",
       "334. TRUE\n",
       "335. TRUE\n",
       "336. TRUE\n",
       "337. TRUE\n",
       "338. TRUE\n",
       "339. TRUE\n",
       "340. TRUE\n",
       "341. TRUE\n",
       "342. TRUE\n",
       "343. TRUE\n",
       "344. TRUE\n",
       "345. TRUE\n",
       "346. TRUE\n",
       "347. TRUE\n",
       "348. TRUE\n",
       "349. TRUE\n",
       "350. TRUE\n",
       "351. TRUE\n",
       "352. TRUE\n",
       "353. TRUE\n",
       "354. TRUE\n",
       "355. TRUE\n",
       "356. TRUE\n",
       "357. TRUE\n",
       "358. TRUE\n",
       "359. TRUE\n",
       "360. TRUE\n",
       "361. TRUE\n",
       "362. TRUE\n",
       "363. TRUE\n",
       "364. TRUE\n",
       "365. TRUE\n",
       "366. TRUE\n",
       "367. TRUE\n",
       "368. TRUE\n",
       "369. TRUE\n",
       "370. TRUE\n",
       "371. TRUE\n",
       "372. TRUE\n",
       "373. TRUE\n",
       "374. TRUE\n",
       "375. TRUE\n",
       "376. TRUE\n",
       "377. TRUE\n",
       "378. TRUE\n",
       "379. TRUE\n",
       "380. TRUE\n",
       "381. TRUE\n",
       "382. TRUE\n",
       "383. TRUE\n",
       "384. TRUE\n",
       "385. TRUE\n",
       "386. TRUE\n",
       "387. TRUE\n",
       "388. TRUE\n",
       "389. TRUE\n",
       "390. TRUE\n",
       "391. TRUE\n",
       "392. TRUE\n",
       "393. TRUE\n",
       "394. TRUE\n",
       "395. TRUE\n",
       "396. TRUE\n",
       "397. TRUE\n",
       "398. TRUE\n",
       "399. TRUE\n",
       "400. TRUE\n",
       "401. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[241] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[256] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[271] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[286] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[301] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[316] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[331] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[346] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[361] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[376] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[391] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[406] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[421] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[436] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[451] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[466] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[481] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[496] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[511] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[526] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[541] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[556] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[571] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[586] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
      ]
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
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       "\\begin{enumerate*}\n",
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      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. TRUE\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE"
      ]
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       "\\begin{enumerate*}\n",
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
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       "60. TRUE\n",
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       "78. TRUE\n",
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       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
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       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
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       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE"
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
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       "\\begin{enumerate*}\n",
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
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       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
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       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
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       "71. TRUE\n",
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       "91. TRUE\n",
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       "93. TRUE\n",
       "94. TRUE\n",
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       "\n",
       "\n"
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       " [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[91] TRUE TRUE TRUE TRUE TRUE"
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       "<ol class=list-inline><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li><li>TRUE</li></ol>\n"
      ],
      "text/latex": [
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       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "7. TRUE\n",
       "8. TRUE\n",
       "9. TRUE\n",
       "10. TRUE\n",
       "11. TRUE\n",
       "12. TRUE\n",
       "13. TRUE\n",
       "14. TRUE\n",
       "15. TRUE\n",
       "16. TRUE\n",
       "17. TRUE\n",
       "18. TRUE\n",
       "19. TRUE\n",
       "20. TRUE\n",
       "21. TRUE\n",
       "22. TRUE\n",
       "23. TRUE\n",
       "24. TRUE\n",
       "25. TRUE\n",
       "26. TRUE\n",
       "27. TRUE\n",
       "28. TRUE\n",
       "29. TRUE\n",
       "30. TRUE\n",
       "31. TRUE\n",
       "32. TRUE\n",
       "33. TRUE\n",
       "34. TRUE\n",
       "35. TRUE\n",
       "36. TRUE\n",
       "37. TRUE\n",
       "38. TRUE\n",
       "39. TRUE\n",
       "40. TRUE\n",
       "41. TRUE\n",
       "42. TRUE\n",
       "43. TRUE\n",
       "44. TRUE\n",
       "45. TRUE\n",
       "46. TRUE\n",
       "47. TRUE\n",
       "48. TRUE\n",
       "49. TRUE\n",
       "50. TRUE\n",
       "51. TRUE\n",
       "52. TRUE\n",
       "53. TRUE\n",
       "54. TRUE\n",
       "55. TRUE\n",
       "56. TRUE\n",
       "57. TRUE\n",
       "58. TRUE\n",
       "59. TRUE\n",
       "60. TRUE\n",
       "61. TRUE\n",
       "62. TRUE\n",
       "63. TRUE\n",
       "64. TRUE\n",
       "65. TRUE\n",
       "66. TRUE\n",
       "67. TRUE\n",
       "68. TRUE\n",
       "69. TRUE\n",
       "70. TRUE\n",
       "71. TRUE\n",
       "72. TRUE\n",
       "73. TRUE\n",
       "74. TRUE\n",
       "75. TRUE\n",
       "76. TRUE\n",
       "77. TRUE\n",
       "78. TRUE\n",
       "79. TRUE\n",
       "80. TRUE\n",
       "81. TRUE\n",
       "82. TRUE\n",
       "83. TRUE\n",
       "84. TRUE\n",
       "85. TRUE\n",
       "86. TRUE\n",
       "87. TRUE\n",
       "88. TRUE\n",
       "89. TRUE\n",
       "90. TRUE\n",
       "91. TRUE\n",
       "92. TRUE\n",
       "93. TRUE\n",
       "94. TRUE\n",
       "95. TRUE\n",
       "96. TRUE\n",
       "97. TRUE\n",
       "98. TRUE\n",
       "99. TRUE\n",
       "100. TRUE\n",
       "101. TRUE\n",
       "102. TRUE\n",
       "103. TRUE\n",
       "104. TRUE\n",
       "105. TRUE\n",
       "106. TRUE\n",
       "107. TRUE\n",
       "108. TRUE\n",
       "109. TRUE\n",
       "110. TRUE\n",
       "111. TRUE\n",
       "112. TRUE\n",
       "113. TRUE\n",
       "114. TRUE\n",
       "115. TRUE\n",
       "116. TRUE\n",
       "117. TRUE\n",
       "118. TRUE\n",
       "119. TRUE\n",
       "120. TRUE\n",
       "121. TRUE\n",
       "122. TRUE\n",
       "123. TRUE\n",
       "124. TRUE\n",
       "125. TRUE\n",
       "126. TRUE\n",
       "127. TRUE\n",
       "128. TRUE\n",
       "129. TRUE\n",
       "130. TRUE\n",
       "131. TRUE\n",
       "132. TRUE\n",
       "133. TRUE\n",
       "134. TRUE\n",
       "135. TRUE\n",
       "136. TRUE\n",
       "137. TRUE\n",
       "138. TRUE\n",
       "139. TRUE\n",
       "140. TRUE\n",
       "141. TRUE\n",
       "142. TRUE\n",
       "143. TRUE\n",
       "144. TRUE\n",
       "145. TRUE\n",
       "146. TRUE\n",
       "147. TRUE\n",
       "148. TRUE\n",
       "149. TRUE\n",
       "150. TRUE\n",
       "151. TRUE\n",
       "152. TRUE\n",
       "153. TRUE\n",
       "154. TRUE\n",
       "155. TRUE\n",
       "156. TRUE\n",
       "157. TRUE\n",
       "158. TRUE\n",
       "159. TRUE\n",
       "160. TRUE\n",
       "161. TRUE\n",
       "162. TRUE\n",
       "163. TRUE\n",
       "164. TRUE\n",
       "165. TRUE\n",
       "166. TRUE\n",
       "167. TRUE\n",
       "168. TRUE\n",
       "169. TRUE\n",
       "170. TRUE\n",
       "171. TRUE\n",
       "172. TRUE\n",
       "173. TRUE\n",
       "174. TRUE\n",
       "175. TRUE\n",
       "176. TRUE\n",
       "177. TRUE\n",
       "178. TRUE\n",
       "179. TRUE\n",
       "180. TRUE\n",
       "181. TRUE\n",
       "182. TRUE\n",
       "183. TRUE\n",
       "184. TRUE\n",
       "185. TRUE\n",
       "186. TRUE\n",
       "187. TRUE\n",
       "188. TRUE\n",
       "189. TRUE\n",
       "190. TRUE\n",
       "191. TRUE\n",
       "192. TRUE\n",
       "193. TRUE\n",
       "194. TRUE\n",
       "195. TRUE\n",
       "196. TRUE\n",
       "197. TRUE\n",
       "198. TRUE\n",
       "199. TRUE\n",
       "200. TRUE\n",
       "201. TRUE\n",
       "202. TRUE\n",
       "203. TRUE\n",
       "204. TRUE\n",
       "205. TRUE\n",
       "206. TRUE\n",
       "207. TRUE\n",
       "208. TRUE\n",
       "209. TRUE\n",
       "210. TRUE\n",
       "211. TRUE\n",
       "212. TRUE\n",
       "213. TRUE\n",
       "214. TRUE\n",
       "215. TRUE\n",
       "216. TRUE\n",
       "217. TRUE\n",
       "218. TRUE\n",
       "219. TRUE\n",
       "220. TRUE\n",
       "221. TRUE\n",
       "222. TRUE\n",
       "223. TRUE\n",
       "224. TRUE\n",
       "225. TRUE\n",
       "226. TRUE\n",
       "227. TRUE\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [16] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [31] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [46] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [61] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [76] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       " [91] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[106] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[121] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[136] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[151] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[166] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[181] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[196] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[211] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE\n",
       "[226] TRUE TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gliomavector <- c(list.files(train_data_glioma))\n",
    "meningiomavector <- c(list.files(train_data_meningioma))\n",
    "notmrvector <- c(list.files(train_data_notmr))\n",
    "pituitaryvector <- c(list.files(train_data_pituitary))\n",
    "\n",
    "# all other dir 600->train and rest test except for notmr -> 300 train and rest test\n",
    "\n",
    "file.copy(file.path(train_data_glioma,gliomavector[1:600]),train_dir_glioma)\n",
    "file.copy(file.path(train_data_meningioma,meningiomavector[1:600]),train_dir_meningioma)\n",
    "file.copy(file.path(train_data_notmr,notmrvector[1:300]),train_dir_notmr)\n",
    "file.copy(file.path(train_data_pituitary,pituitaryvector[1:600]),train_dir_pituitary)\n",
    "\n",
    "#filling the test_dir \n",
    "file.copy(file.path(train_data_glioma,gliomavector[601:length(gliomavector)]),test_dir_glioma)\n",
    "file.copy(file.path(train_data_meningioma,meningiomavector[601:length(meningiomavector)]),test_dir_meningioma)\n",
    "file.copy(file.path(train_data_notmr,notmrvector[301:length(notmrvector)]),test_dir_notmr)\n",
    "file.copy(file.path(train_data_pituitary,pituitaryvector[601:length(pituitaryvector)]),test_dir_pituitary)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3d56e205",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:16:57.073513Z",
     "iopub.status.busy": "2023-04-04T17:16:57.072281Z",
     "iopub.status.idle": "2023-04-04T17:16:57.130041Z",
     "shell.execute_reply": "2023-04-04T17:16:57.128545Z"
    },
    "papermill": {
     "duration": 0.075979,
     "end_time": "2023-04-04T17:16:57.132230",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.056251",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Train Dataset :: \"\n",
      "[1] \"Glioma train ::  600\"\n",
      "[1] \"Meningioma train ::  600\"\n",
      "[1] \"Notmr train ::  300\"\n",
      "[1] \"Pituitary train ::  600\"\n",
      "[1] \"----------\"\n",
      "[1] \"Test Dataset\"\n",
      "[1] \"Glioma test ::  226\"\n",
      "[1] \"Meningioma test ::  222\"\n",
      "[1] \"Notmr test ::  95\"\n",
      "[1] \"Pituitary test ::  227\"\n"
     ]
    }
   ],
   "source": [
    "#printing the len of the train and test data\n",
    "print(\"Train Dataset :: \")\n",
    "print(paste(\"Glioma train :: \",length(list.files(train_dir_glioma))))\n",
    "print(paste(\"Meningioma train :: \",length(list.files(train_dir_meningioma))))\n",
    "print(paste(\"Notmr train :: \",length(list.files(train_dir_notmr))))\n",
    "print(paste(\"Pituitary train :: \",length(list.files(train_dir_pituitary))))\n",
    "\n",
    "print(\"----------\")\n",
    "print(\"Test Dataset\")\n",
    "print(paste(\"Glioma test :: \",length(list.files(test_dir_glioma))))\n",
    "print(paste(\"Meningioma test :: \",length(list.files(test_dir_meningioma))))\n",
    "print(paste(\"Notmr test :: \",length(list.files(test_dir_notmr))))\n",
    "print(paste(\"Pituitary test :: \",length(list.files(test_dir_pituitary))))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555c0c2d",
   "metadata": {
    "papermill": {
     "duration": 0.015682,
     "end_time": "2023-04-04T17:16:57.163741",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.148059",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Test and Train Data Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66b38f7d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:16:57.198076Z",
     "iopub.status.busy": "2023-04-04T17:16:57.196831Z",
     "iopub.status.idle": "2023-04-04T17:17:05.871636Z",
     "shell.execute_reply": "2023-04-04T17:17:05.869310Z"
    },
    "papermill": {
     "duration": 8.695283,
     "end_time": "2023-04-04T17:17:05.874935",
     "exception": false,
     "start_time": "2023-04-04T17:16:57.179652",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "train_datagen<-image_data_generator(rescale=1/255,\n",
    "                                   zoom_range=0.2,\n",
    "                                   height_shift_range=0.2,\n",
    "                                   width_shift_range=0.2,\n",
    "                                   fill_mode='nearest')\n",
    "\n",
    "test_datagen<-image_data_generator(rescale = 1/255)\n",
    "\n",
    "train_generator<-flow_images_from_directory(directory=train_dir,\n",
    "                                           generator=train_datagen,\n",
    "                                           class_mode=\"categorical\",\n",
    "                                           target_size=c(224,224),\n",
    "                                           batch_size=32,\n",
    "                                           seed = 123)\n",
    "\n",
    "test_generator<-flow_images_from_directory(directory=test_dir,\n",
    "                                           generator=test_datagen,\n",
    "                                           class_mode=\"categorical\",\n",
    "                                           target_size=c(224,224),\n",
    "                                           batch_size=32,\n",
    "                                           shuffle = FALSE,\n",
    "                                           seed = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2b4fd22c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:17:05.909498Z",
     "iopub.status.busy": "2023-04-04T17:17:05.907855Z",
     "iopub.status.idle": "2023-04-04T17:17:05.931217Z",
     "shell.execute_reply": "2023-04-04T17:17:05.929243Z"
    },
    "papermill": {
     "duration": 0.043077,
     "end_time": "2023-04-04T17:17:05.933651",
     "exception": false,
     "start_time": "2023-04-04T17:17:05.890574",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "\n",
       "\n"
      ],
      "text/plain": [
       "  [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " [38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       " [75] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[112] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[149] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[186] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
       "[223] 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[408] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
       "[445] 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
       "[482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
       "[519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[556] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[593] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[630] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[667] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[704] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
       "[741] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_generator$classes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb90e9a1",
   "metadata": {
    "papermill": {
     "duration": 0.021093,
     "end_time": "2023-04-04T17:31:42.779878",
     "exception": false,
     "start_time": "2023-04-04T17:31:42.758785",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Xception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ac217c17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:42.825493Z",
     "iopub.status.busy": "2023-04-04T17:31:42.824150Z",
     "iopub.status.idle": "2023-04-04T17:31:42.918143Z",
     "shell.execute_reply": "2023-04-04T17:31:42.916409Z"
    },
    "papermill": {
     "duration": 0.119413,
     "end_time": "2023-04-04T17:31:42.920520",
     "exception": false,
     "start_time": "2023-04-04T17:31:42.801107",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_datagen = image_data_generator(\n",
    "    rescale=1/255,\n",
    "    zoom_range=0.2,\n",
    "    height_shift_range=0.2,\n",
    "    width_shift_range=0.2,\n",
    "    fill_mode='nearest',\n",
    ")\n",
    "\n",
    "test_datagen = image_data_generator(\n",
    "    rescale=1/255\n",
    ")\n",
    "\n",
    "train_generator = flow_images_from_directory(\n",
    "    directory = train_dir,\n",
    "    generator = train_datagen,\n",
    "    class_mode = \"categorical\",\n",
    "    target_size = c(224, 224),\n",
    "    batch_size = 20\n",
    ")\n",
    "\n",
    "test_generator = flow_images_from_directory(\n",
    "    directory = test_dir,\n",
    "    generator = test_datagen,\n",
    "    class_mode = \"categorical\",\n",
    "    target_size = c(224, 224),\n",
    "    batch_size = 10,\n",
    "    shuffle=FALSE,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "52d5ae84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:42.968485Z",
     "iopub.status.busy": "2023-04-04T17:31:42.967255Z",
     "iopub.status.idle": "2023-04-04T17:31:45.121037Z",
     "shell.execute_reply": "2023-04-04T17:31:45.119452Z"
    },
    "papermill": {
     "duration": 2.180209,
     "end_time": "2023-04-04T17:31:45.123776",
     "exception": false,
     "start_time": "2023-04-04T17:31:42.943567",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "conv_baseX = application_xception(\n",
    "    weights = 'imagenet',\n",
    "    include_top=FALSE,\n",
    "    input_shape=c(224,224,3)\n",
    ")\n",
    "\n",
    "freeze_weights(conv_baseX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bc5983cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:45.178038Z",
     "iopub.status.busy": "2023-04-04T17:31:45.176846Z",
     "iopub.status.idle": "2023-04-04T17:31:45.567492Z",
     "shell.execute_reply": "2023-04-04T17:31:45.565237Z"
    },
    "papermill": {
     "duration": 0.424665,
     "end_time": "2023-04-04T17:31:45.570050",
     "exception": false,
     "start_time": "2023-04-04T17:31:45.145385",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "________________________________________________________________________________\n",
      "Layer (type)                        Output Shape                    Param #     \n",
      "================================================================================\n",
      "xception (Functional)               (None, 7, 7, 2048)              20861480    \n",
      "________________________________________________________________________________\n",
      "global_average_pooling2d_5 (GlobalA (None, 2048)                    0           \n",
      "________________________________________________________________________________\n",
      "dense_14 (Dense)                    (None, 128)                     262272      \n",
      "________________________________________________________________________________\n",
      "batch_normalization_105 (BatchNorma (None, 128)                     512         \n",
      "________________________________________________________________________________\n",
      "dense_13 (Dense)                    (None, 128)                     16512       \n",
      "________________________________________________________________________________\n",
      "batch_normalization_104 (BatchNorma (None, 128)                     512         \n",
      "________________________________________________________________________________\n",
      "dense_12 (Dense)                    (None, 4)                       516         \n",
      "================================================================================\n",
      "Total params: 21,141,804\n",
      "Trainable params: 279,812\n",
      "Non-trainable params: 20,861,992\n",
      "________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model<-keras_model_sequential() %>%\n",
    "    conv_baseX %>%\n",
    "    layer_global_average_pooling_2d(trainable = T)%>%\n",
    "    layer_dense(units=128, activation=\"relu\",kernel_regularizer=regularizer_l1(1e-8)) %>%\n",
    "    layer_batch_normalization() %>%\n",
    "    layer_dense(units=128, activation=\"relu\",kernel_regularizer=regularizer_l1(1e-8)) %>%\n",
    "    layer_batch_normalization() %>%\n",
    "    layer_dense(units=4, activation=\"softmax\")\n",
    "\n",
    "\n",
    "model %>% compile(\n",
    "    metrics=\"accuracy\",\n",
    "    loss=\"categorical_crossentropy\",\n",
    "    optimizer = optimizer_adam(learning_rate = 0.01, decay = 1e-6),\n",
    ")\n",
    "\n",
    "summary(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2a2e0928",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:31:45.616423Z",
     "iopub.status.busy": "2023-04-04T17:31:45.615163Z",
     "iopub.status.idle": "2023-04-04T17:34:45.451895Z",
     "shell.execute_reply": "2023-04-04T17:34:45.449339Z"
    },
    "papermill": {
     "duration": 179.863449,
     "end_time": "2023-04-04T17:34:45.455512",
     "exception": false,
     "start_time": "2023-04-04T17:31:45.592063",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "history<-model %>% fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=20,\n",
    "    epoch=20,\n",
    "    validation_data=test_generator,\n",
    "    validation_step=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "71076beb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:34:45.529520Z",
     "iopub.status.busy": "2023-04-04T17:34:45.527922Z",
     "iopub.status.idle": "2023-04-04T17:34:45.953473Z",
     "shell.execute_reply": "2023-04-04T17:34:45.950783Z"
    },
    "papermill": {
     "duration": 0.456745,
     "end_time": "2023-04-04T17:34:45.956663",
     "exception": false,
     "start_time": "2023-04-04T17:34:45.499918",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Final epoch (plot to see history):\n",
       "        loss: 0.4021\n",
       "    accuracy: 0.835\n",
       "    val_loss: 0.5186\n",
       "val_accuracy: 0.81 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[1m\u001b[22m`geom_smooth()` using formula = 'y ~ x'\n"
     ]
    },
    {
     "data": {
      "image/png": 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GgVBDvtjbBb91iFYnU0pmE9AAAAkKcQ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\nQmCC"
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "history;plot(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "233c9a8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:34:46.013447Z",
     "iopub.status.busy": "2023-04-04T17:34:46.011932Z",
     "iopub.status.idle": "2023-04-04T17:34:46.030443Z",
     "shell.execute_reply": "2023-04-04T17:34:46.028622Z"
    },
    "papermill": {
     "duration": 0.049514,
     "end_time": "2023-04-04T17:34:46.033090",
     "exception": false,
     "start_time": "2023-04-04T17:34:45.983576",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "  0   1   2   3 \n",
       "226 222  95 227 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table(test_generator$classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67d43c45",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bbfa39b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-04-04T17:34:46.090715Z",
     "iopub.status.busy": "2023-04-04T17:34:46.089151Z",
     "iopub.status.idle": "2023-04-04T17:34:52.014533Z",
     "shell.execute_reply": "2023-04-04T17:34:52.012461Z"
    },
    "papermill": {
     "duration": 5.957854,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "pred_class\n",
       "  0   1   2   3 \n",
       "335 111  83 241 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_class <- function(vec) { return(which.max(vec)-1) }\n",
    "\n",
    "pred <- predict(model, test_generator)\n",
    "pred_class <- apply(pred, 1, max_class)\n",
    "\n",
    "table(pred_class)"
   ]
  },
  {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Confusion Matrix and Statistics\n",
       "\n",
       "   pred_class\n",
       "      0   1   2   3\n",
       "  0 202  18   0   6\n",
       "  1 122  73   6  21\n",
       "  2   6   9  76   4\n",
       "  3   5  11   1 210\n",
       "\n",
       "Overall Statistics\n",
       "                                          \n",
       "               Accuracy : 0.7286          \n",
       "                 95% CI : (0.6957, 0.7597)\n",
       "    No Information Rate : 0.4351          \n",
       "    P-Value [Acc > NIR] : < 2.2e-16       \n",
       "                                          \n",
       "                  Kappa : 0.6257          \n",
       "                                          \n",
       " Mcnemar's Test P-Value : < 2.2e-16       \n",
       "\n",
       "Statistics by Class:\n",
       "\n",
       "                     Class: 0 Class: 1 Class: 2 Class: 3\n",
       "Sensitivity            0.6030  0.65766   0.9157   0.8714\n",
       "Specificity            0.9448  0.77390   0.9723   0.9679\n",
       "Pos Pred Value         0.8938  0.32883   0.8000   0.9251\n",
       "Neg Pred Value         0.7555  0.93066   0.9896   0.9429\n",
       "Prevalence             0.4351  0.14416   0.1078   0.3130\n",
       "Detection Rate         0.2623  0.09481   0.0987   0.2727\n",
       "Detection Prevalence   0.2935  0.28831   0.1234   0.2948\n",
       "Balanced Accuracy      0.7739  0.71578   0.9440   0.9196"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_mat = table(test_generator$classes, pred_class)\n",
    "confusionMatrix(conf_mat)"
   ]
  }
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