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| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 61, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "import re\n", |
| 10 | + "import numpy as np" |
| 11 | + ] |
| 12 | + }, |
| 13 | + { |
| 14 | + "cell_type": "code", |
| 15 | + "execution_count": 62, |
| 16 | + "metadata": {}, |
| 17 | + "outputs": [ |
| 18 | + { |
| 19 | + "data": { |
| 20 | + "text/plain": [ |
| 21 | + "254" |
| 22 | + ] |
| 23 | + }, |
| 24 | + "execution_count": 62, |
| 25 | + "metadata": {}, |
| 26 | + "output_type": "execute_result" |
| 27 | + } |
| 28 | + ], |
| 29 | + "source": [ |
| 30 | + "d = {}\n", |
| 31 | + "lines = []\n", |
| 32 | + "with open('sentences.txt') as f:\n", |
| 33 | + " for count, line in enumerate(f.readlines()):\n", |
| 34 | + " line = line.lower()\n", |
| 35 | + " line = re.split('[^a-z]', line)\n", |
| 36 | + " \n", |
| 37 | + " lines.append(line)\n", |
| 38 | + "\n", |
| 39 | + " lines[count] = [tok for tok in lines[count] if tok != '']\n", |
| 40 | + "\n", |
| 41 | + " line = lines[count]\n", |
| 42 | + "\n", |
| 43 | + " for key, token in enumerate(line):\n", |
| 44 | + " if (len(token)):\n", |
| 45 | + " if (token not in d):\n", |
| 46 | + " d[token] = 1\n", |
| 47 | + " else:\n", |
| 48 | + " d[token] += 1\n", |
| 49 | + " \n", |
| 50 | + "len(d)" |
| 51 | + ] |
| 52 | + }, |
| 53 | + { |
| 54 | + "cell_type": "code", |
| 55 | + "execution_count": 63, |
| 56 | + "metadata": {}, |
| 57 | + "outputs": [], |
| 58 | + "source": [ |
| 59 | + "pred = []\n", |
| 60 | + "lines = list(lines)\n", |
| 61 | + "\n", |
| 62 | + "words = list(d)\n", |
| 63 | + "\n", |
| 64 | + "for i in range(count):\n", |
| 65 | + " temp = []\n", |
| 66 | + " for j in range(len(words)):\n", |
| 67 | + " temp.append(lines[i].count(words[j]))\n", |
| 68 | + " pred.append(np.array(temp))" |
| 69 | + ] |
| 70 | + }, |
| 71 | + { |
| 72 | + "cell_type": "code", |
| 73 | + "execution_count": 64, |
| 74 | + "metadata": {}, |
| 75 | + "outputs": [ |
| 76 | + { |
| 77 | + "name": "stdout", |
| 78 | + "output_type": "stream", |
| 79 | + "text": [ |
| 80 | + "[4. 6.]\n" |
| 81 | + ] |
| 82 | + } |
| 83 | + ], |
| 84 | + "source": [ |
| 85 | + "from scipy import spatial\n", |
| 86 | + "res = np.zeros(shape=(20,2))\n", |
| 87 | + "\n", |
| 88 | + "for i in range(len(lines) - 2):\n", |
| 89 | + " res[i] = (i+1, spatial.distance.cosine(pred[0], pred[i+1]))\n", |
| 90 | + "\n", |
| 91 | + "\n", |
| 92 | + "res = res[res[:, 1].argsort()]\n", |
| 93 | + "\n", |
| 94 | + "answ = res[0:2, 0]\n", |
| 95 | + "answ.sort()\n", |
| 96 | + "print(answ)\n", |
| 97 | + "\n", |
| 98 | + "f = open(\"submission-1.txt\", \"w\")\n", |
| 99 | + "f.write(\"%i %i\" % (answ[0], answ[1]))\n", |
| 100 | + "f.close()" |
| 101 | + ] |
| 102 | + } |
| 103 | + ], |
| 104 | + "metadata": { |
| 105 | + "interpreter": { |
| 106 | + "hash": "02769d244be1e9c182a881d7d25bc03ab28e10cd9282ec6a7530b69182abd7c2" |
| 107 | + }, |
| 108 | + "kernelspec": { |
| 109 | + "display_name": "Python 3.10.2 64-bit", |
| 110 | + "language": "python", |
| 111 | + "name": "python3" |
| 112 | + }, |
| 113 | + "language_info": { |
| 114 | + "codemirror_mode": { |
| 115 | + "name": "ipython", |
| 116 | + "version": 3 |
| 117 | + }, |
| 118 | + "file_extension": ".py", |
| 119 | + "mimetype": "text/x-python", |
| 120 | + "name": "python", |
| 121 | + "nbconvert_exporter": "python", |
| 122 | + "pygments_lexer": "ipython3", |
| 123 | + "version": "3.10.2" |
| 124 | + }, |
| 125 | + "orig_nbformat": 4 |
| 126 | + }, |
| 127 | + "nbformat": 4, |
| 128 | + "nbformat_minor": 2 |
| 129 | +} |
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