2020-2-capstone-design2 / 2017110275

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2진수로 변경

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1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 3,
6 + "metadata": {
7 + "scrolled": true
8 + },
9 + "outputs": [],
10 + "source": [
11 + "import csv\n",
12 + "import numpy as np\n",
13 + "import os\n",
14 + "import pandas as pd"
15 + ]
16 + },
17 + {
18 + "cell_type": "markdown",
19 + "metadata": {},
20 + "source": [
21 + "# 10진수 변환 (한번만)"
22 + ]
23 + },
24 + {
25 + "cell_type": "code",
26 + "execution_count": 4,
27 + "metadata": {},
28 + "outputs": [],
29 + "source": [
30 + "def transferData(dir_, outputdir_) :\n",
31 + " outputfile = open(outputdir_, 'w', newline='\\n')\n",
32 + " fopen = open(dir_, 'r')\n",
33 + " csvreader = csv.reader(fopen)\n",
34 + " \n",
35 + " fw = csv.writer(outputfile)\n",
36 + " \n",
37 + " for line in csvreader :\n",
38 + " newdata = []\n",
39 + " can_id = int(line[1], 16)\n",
40 + " newdata.append(can_id)\n",
41 + " newdata.append(line[2])\n",
42 + " \n",
43 + " ll = len(line)\n",
44 + " num = int(line[2])\n",
45 + " \n",
46 + " data = []\n",
47 + " if num == 8 :\n",
48 + " data = line[3:11]\n",
49 + " else :\n",
50 + " data = line[3:3+num]\n",
51 + " for _ in range(8-num):\n",
52 + " data.append('0')\n",
53 + " #print(line)\n",
54 + " newdata.extend(list(map(lambda x:int(x, 16), data)))\n",
55 + " \n",
56 + " if line[-1] == 'T' :\n",
57 + " newdata.append(0)\n",
58 + " else :\n",
59 + " newdata.append(1)\n",
60 + " \n",
61 + " fw.writerow(newdata)\n",
62 + "\n",
63 + "def Preprocess(dir_) :\n",
64 + " f1 = open(dir_, 'r')\n",
65 + " fr = csv.reader(f1)\n",
66 + " d = [] \n",
67 + " for line in fr :\n",
68 + " l = []\n",
69 + " l.append(line[0])\n",
70 + " l.append(line[1])\n",
71 + " l.extend(line[2:11])\n",
72 + " \n",
73 + " d.append(l)\n",
74 + " return d"
75 + ]
76 + },
77 + {
78 + "cell_type": "code",
79 + "execution_count": 5,
80 + "metadata": {},
81 + "outputs": [],
82 + "source": [
83 + "#transferData('DoS_dataset.csv', 'DoS.csv')"
84 + ]
85 + },
86 + {
87 + "cell_type": "markdown",
88 + "metadata": {},
89 + "source": [
90 + "# 2진수 변환 후 저장 (한번만)"
91 + ]
92 + },
93 + {
94 + "cell_type": "code",
95 + "execution_count": 5,
96 + "metadata": {},
97 + "outputs": [],
98 + "source": [
99 + "f = open('DoS.csv', 'r')\n",
100 + "ff = open('DoSres.csv', 'w', newline='\\n')\n",
101 + "cr = csv.reader(f)\n",
102 + "fw = csv.writer(ff)\n",
103 + "\n",
104 + "a = []\n",
105 + "for line in cr:\n",
106 + " #a = []\n",
107 + " CANID = []\n",
108 + " b = []\n",
109 + " canid = bin(int(line[0]))\n",
110 + " #remove 'b0'\n",
111 + " CANID.extend(canid[2:])\n",
112 + " #padding Max len : 11\n",
113 + " n = len(canid[2:])\n",
114 + " if n < 11 :\n",
115 + " padding = ['0' for _ in range(11-n)]\n",
116 + " b.extend(padding)\n",
117 + " b.extend(CANID)\n",
118 + " \n",
119 + " # Data 0~7\n",
120 + " DATA = []\n",
121 + " for i in range(2, 10):\n",
122 + " data = bin(int(line[i]))\n",
123 + " #remove 'b0'\n",
124 + " DATA.extend(data[2:])\n",
125 + " #padding Max len : 8\n",
126 + " n = len(data[2:])\n",
127 + " if n < 8 :\n",
128 + " padding = ['0' for _ in range(8-n)]\n",
129 + " b.extend(padding)\n",
130 + " b.extend(DATA)\n",
131 + " \n",
132 + " # Class \n",
133 + " b.extend(line[-1])\n",
134 + " a.append(b)\n",
135 + " \n",
136 + " #fw.writerow(a)\n",
137 + "\n",
138 + "ff.close()\n",
139 + "f.close()"
140 + ]
141 + },
142 + {
143 + "cell_type": "code",
144 + "execution_count": 6,
145 + "metadata": {},
146 + "outputs": [
147 + {
148 + "name": "stdout",
149 + "output_type": "stream",
150 + "text": [
151 + "76 ['0', '1', '1', '0', '0', '0', '1', '0', '1', '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', '1', '0', '1', '1', '0', '0', '0', '0', '1', '1', '1', '0', '1', '0', '0', '0', '1', '0', '0', '1', '1', '0', '0', '0', '0', '1', '1', '0', '0', '0', '0', '1', '0', '1', '1', '0', '1', '1', '1', '1', '1']\n",
152 + "76 ['0', '0', '1', '1', '0', '0', '0', '1', '1', '1', '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', '0', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', '1', '1', '0', '0', '0', '1', '1', '1', '1', '0', '0', '0', '0', '1']\n",
153 + "3665771\n"
154 + ]
155 + }
156 + ],
157 + "source": [
158 + "print(len(a[0]),a[0])\n",
159 + "print(len(a[1]), a[1])\n",
160 + "print(len(a))"
161 + ]
162 + },
163 + {
164 + "cell_type": "markdown",
165 + "metadata": {},
166 + "source": [
167 + "# 76*76 배열 구성"
168 + ]
169 + },
170 + {
171 + "cell_type": "code",
172 + "execution_count": 1,
173 + "metadata": {},
174 + "outputs": [],
175 + "source": [
176 + "def create2D(data) :\n",
177 + " arr = []\n",
178 + " for i in range(0, len(data)- 75, 76): \n",
179 + " d = []\n",
180 + " for x in range(0, 76) :\n",
181 + " ll = list(map(float, data[i+x]))\n",
182 + " d.append(ll)\n",
183 + " arr.append(d)\n",
184 + " \n",
185 + " return arr"
186 + ]
187 + },
188 + {
189 + "cell_type": "code",
190 + "execution_count": 8,
191 + "metadata": {},
192 + "outputs": [],
193 + "source": [
194 + "resData = create2D(a)\n",
195 + "del a"
196 + ]
197 + },
198 + {
199 + "cell_type": "code",
200 + "execution_count": 9,
201 + "metadata": {},
202 + "outputs": [
203 + {
204 + "name": "stdout",
205 + "output_type": "stream",
206 + "text": [
207 + "48233\n"
208 + ]
209 + }
210 + ],
211 + "source": [
212 + "#print(len(resData[0]), resData[0])\n",
213 + "print(len(resData))"
214 + ]
215 + },
216 + {
217 + "cell_type": "code",
218 + "execution_count": 17,
219 + "metadata": {},
220 + "outputs": [
221 + {
222 + "name": "stdout",
223 + "output_type": "stream",
224 + "text": [
225 + "[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
226 + "76\n",
227 + "76\n"
228 + ]
229 + }
230 + ],
231 + "source": [
232 + "print(resData[0][0])\n",
233 + "print(len(resData[0][0]))\n",
234 + "print(len(resData[0]))"
235 + ]
236 + },
237 + {
238 + "cell_type": "markdown",
239 + "metadata": {},
240 + "source": [
241 + "# 비정상 메시지 포함 유무 체크"
242 + ]
243 + },
244 + {
245 + "cell_type": "code",
246 + "execution_count": 10,
247 + "metadata": {},
248 + "outputs": [],
249 + "source": [
250 + "def check(data):\n",
251 + " check = []\n",
252 + " \n",
253 + " for e in data :\n",
254 + " flag = False\n",
255 + " for i in e :\n",
256 + " if i[-1] == 0 :\n",
257 + " check.append(0)\n",
258 + " flag = True\n",
259 + " break\n",
260 + " if flag == False :\n",
261 + " check.append(1)\n",
262 + " \n",
263 + " return check\n",
264 + "\n",
265 + "checkblock = check(resData)"
266 + ]
267 + },
268 + {
269 + "cell_type": "code",
270 + "execution_count": 11,
271 + "metadata": {},
272 + "outputs": [
273 + {
274 + "name": "stdout",
275 + "output_type": "stream",
276 + "text": [
277 + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
278 + "48233\n"
279 + ]
280 + }
281 + ],
282 + "source": [
283 + "print(checkblock[10010:10030])\n",
284 + "print(len(checkblock))"
285 + ]
286 + },
287 + {
288 + "cell_type": "markdown",
289 + "metadata": {},
290 + "source": [
291 + "# 데이터 개수 계산"
292 + ]
293 + },
294 + {
295 + "cell_type": "code",
296 + "execution_count": 12,
297 + "metadata": {},
298 + "outputs": [
299 + {
300 + "name": "stdout",
301 + "output_type": "stream",
302 + "text": [
303 + "33760\n",
304 + "14473\n"
305 + ]
306 + }
307 + ],
308 + "source": [
309 + "cnt = 0\n",
310 + "for e in checkblock :\n",
311 + " if e == 1 :\n",
312 + " cnt += 1\n",
313 + " \n",
314 + "print(cnt)\n",
315 + "print(len(checkblock)-cnt)"
316 + ]
317 + },
318 + {
319 + "cell_type": "markdown",
320 + "metadata": {},
321 + "source": [
322 + "# 테스트, 트레인 데이터 나누기"
323 + ]
324 + },
325 + {
326 + "cell_type": "code",
327 + "execution_count": 18,
328 + "metadata": {},
329 + "outputs": [],
330 + "source": [
331 + "from sklearn.model_selection import train_test_split\n",
332 + "import tensorflow as tf"
333 + ]
334 + },
335 + {
336 + "cell_type": "code",
337 + "execution_count": 19,
338 + "metadata": {
339 + "scrolled": true
340 + },
341 + "outputs": [],
342 + "source": [
343 + "df = pd.DataFrame(resData)\n",
344 + "n = len(df)\n",
345 + "\n",
346 + "X_train = df.iloc[0:n, 0:76].values.tolist()\n",
347 + "Y_train = checkblock\n",
348 + "\n",
349 + "X_train = np.asarray(X_train)\n",
350 + "Y_train = np.asarray(Y_train)\n",
351 + "#X_train = tf.constant(X_train)\n",
352 + "#print(X_train[-1])\n",
353 + "#Y_train = tf.convert_to_tensor(Y_train)\n",
354 + "\n",
355 + "#Y_train = np.squeeze(Y_train, axis=1)\n",
356 + "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3, random_state=321)"
357 + ]
358 + },
359 + {
360 + "cell_type": "code",
361 + "execution_count": 20,
362 + "metadata": {
363 + "scrolled": false
364 + },
365 + "outputs": [
366 + {
367 + "name": "stdout",
368 + "output_type": "stream",
369 + "text": [
370 + "(48233, 76, 76)\n",
371 + "(48233,)\n"
372 + ]
373 + }
374 + ],
375 + "source": [
376 + "print(X_train.shape)\n",
377 + "print(Y_train.shape)"
378 + ]
379 + },
380 + {
381 + "cell_type": "code",
382 + "execution_count": 241,
383 + "metadata": {},
384 + "outputs": [],
385 + "source": [
386 + "for i in x_train:\n",
387 + " for j in i:\n",
388 + " if len(j) != 11:\n",
389 + " print(j)"
390 + ]
391 + },
392 + {
393 + "cell_type": "code",
394 + "execution_count": 21,
395 + "metadata": {
396 + "scrolled": true
397 + },
398 + "outputs": [
399 + {
400 + "data": {
401 + "text/html": [
402 + "<div>\n",
403 + "<style scoped>\n",
404 + " .dataframe tbody tr th:only-of-type {\n",
405 + " vertical-align: middle;\n",
406 + " }\n",
407 + "\n",
408 + " .dataframe tbody tr th {\n",
409 + " vertical-align: top;\n",
410 + " }\n",
411 + "\n",
412 + " .dataframe thead th {\n",
413 + " text-align: right;\n",
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415 + "</style>\n",
416 + "<table border=\"1\" class=\"dataframe\">\n",
417 + " <thead>\n",
418 + " <tr style=\"text-align: right;\">\n",
419 + " <th></th>\n",
420 + " <th>0</th>\n",
421 + " <th>1</th>\n",
422 + " <th>2</th>\n",
423 + " <th>3</th>\n",
424 + " <th>4</th>\n",
425 + " <th>5</th>\n",
426 + " <th>6</th>\n",
427 + " <th>7</th>\n",
428 + " <th>8</th>\n",
429 + " <th>9</th>\n",
430 + " <th>...</th>\n",
431 + " <th>66</th>\n",
432 + " <th>67</th>\n",
433 + " <th>68</th>\n",
434 + " <th>69</th>\n",
435 + " <th>70</th>\n",
436 + " <th>71</th>\n",
437 + " <th>72</th>\n",
438 + " <th>73</th>\n",
439 + " <th>74</th>\n",
440 + " <th>75</th>\n",
441 + " </tr>\n",
442 + " </thead>\n",
443 + " <tbody>\n",
444 + " <tr>\n",
445 + " <th>0</th>\n",
446 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
447 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
448 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
449 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
450 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
451 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
452 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
453 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
454 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
455 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
456 + " <td>...</td>\n",
457 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
458 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
459 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
460 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
461 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
462 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
463 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
464 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
465 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
466 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
467 + " </tr>\n",
468 + " <tr>\n",
469 + " <th>1</th>\n",
470 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
471 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
472 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
473 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
474 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
475 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
476 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
477 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
478 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
479 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
480 + " <td>...</td>\n",
481 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
482 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
483 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
484 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
485 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
486 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
487 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
488 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
489 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
490 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
491 + " </tr>\n",
492 + " <tr>\n",
493 + " <th>2</th>\n",
494 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
495 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
496 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
497 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
498 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
499 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
500 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
501 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
502 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
503 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
504 + " <td>...</td>\n",
505 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
506 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
507 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
508 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
509 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
510 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
511 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
512 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
513 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
514 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
515 + " </tr>\n",
516 + " <tr>\n",
517 + " <th>3</th>\n",
518 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
519 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
520 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
521 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
522 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
523 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
524 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
525 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
526 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
527 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
528 + " <td>...</td>\n",
529 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
530 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
531 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
532 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
533 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
534 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
535 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
536 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
537 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
538 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
539 + " </tr>\n",
540 + " <tr>\n",
541 + " <th>4</th>\n",
542 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
543 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
544 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
545 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
546 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
547 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
548 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
549 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
550 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
551 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
552 + " <td>...</td>\n",
553 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
554 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
555 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
556 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
557 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
558 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
559 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
560 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
561 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
562 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
563 + " </tr>\n",
564 + " <tr>\n",
565 + " <th>...</th>\n",
566 + " <td>...</td>\n",
567 + " <td>...</td>\n",
568 + " <td>...</td>\n",
569 + " <td>...</td>\n",
570 + " <td>...</td>\n",
571 + " <td>...</td>\n",
572 + " <td>...</td>\n",
573 + " <td>...</td>\n",
574 + " <td>...</td>\n",
575 + " <td>...</td>\n",
576 + " <td>...</td>\n",
577 + " <td>...</td>\n",
578 + " <td>...</td>\n",
579 + " <td>...</td>\n",
580 + " <td>...</td>\n",
581 + " <td>...</td>\n",
582 + " <td>...</td>\n",
583 + " <td>...</td>\n",
584 + " <td>...</td>\n",
585 + " <td>...</td>\n",
586 + " <td>...</td>\n",
587 + " </tr>\n",
588 + " <tr>\n",
589 + " <th>48228</th>\n",
590 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
591 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
592 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
593 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
594 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
595 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
596 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
597 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
598 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
599 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
600 + " <td>...</td>\n",
601 + " <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
602 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
603 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
604 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
605 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
606 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
607 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
608 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
609 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
610 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
611 + " </tr>\n",
612 + " <tr>\n",
613 + " <th>48229</th>\n",
614 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
615 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
616 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
617 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
618 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
619 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
620 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
621 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
622 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
623 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
624 + " <td>...</td>\n",
625 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
626 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
627 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
628 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
629 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
630 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
631 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
632 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
633 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
634 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
635 + " </tr>\n",
636 + " <tr>\n",
637 + " <th>48230</th>\n",
638 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
639 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
640 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
641 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
642 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
643 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
644 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
645 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
646 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
647 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
648 + " <td>...</td>\n",
649 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
650 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
651 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
652 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
653 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
654 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
655 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
656 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
657 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
658 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
659 + " </tr>\n",
660 + " <tr>\n",
661 + " <th>48231</th>\n",
662 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
663 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
664 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
665 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
666 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
667 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
668 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
669 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
670 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
671 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
672 + " <td>...</td>\n",
673 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
674 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
675 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
676 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
677 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
678 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
679 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
680 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
681 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
682 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
683 + " </tr>\n",
684 + " <tr>\n",
685 + " <th>48232</th>\n",
686 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
687 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
688 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
689 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
690 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
691 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
692 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
693 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
694 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
695 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
696 + " <td>...</td>\n",
697 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
698 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
699 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
700 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
701 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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707 + " </tr>\n",
708 + " </tbody>\n",
709 + "</table>\n",
710 + "<p>48233 rows × 76 columns</p>\n",
711 + "</div>"
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911 + "1 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
912 + "2 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
913 + "3 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
914 + "4 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
915 + "... ... \n",
916 + "48228 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
917 + "48229 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
918 + "48230 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
919 + "48231 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
920 + "48232 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
921 + "\n",
922 + " 72 \\\n",
923 + "0 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
924 + "1 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
925 + "2 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
926 + "3 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
927 + "4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
928 + "... ... \n",
929 + "48228 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
930 + "48229 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
931 + "48230 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
932 + "48231 [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
933 + "48232 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
934 + "\n",
935 + " 73 \\\n",
936 + "0 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
937 + "1 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
938 + "2 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
939 + "3 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
940 + "4 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
941 + "... ... \n",
942 + "48228 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
943 + "48229 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
944 + "48230 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
945 + "48231 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
946 + "48232 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
947 + "\n",
948 + " 74 \\\n",
949 + "0 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
950 + "1 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
951 + "2 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
952 + "3 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
953 + "4 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
954 + "... ... \n",
955 + "48228 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
956 + "48229 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
957 + "48230 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
958 + "48231 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
959 + "48232 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
960 + "\n",
961 + " 75 \n",
962 + "0 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
963 + "1 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
964 + "2 [1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
965 + "3 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
966 + "4 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
967 + "... ... \n",
968 + "48228 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
969 + "48229 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
970 + "48230 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
971 + "48231 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
972 + "48232 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
973 + "\n",
974 + "[48233 rows x 76 columns]"
975 + ]
976 + },
977 + "execution_count": 21,
978 + "metadata": {},
979 + "output_type": "execute_result"
980 + }
981 + ],
982 + "source": [
983 + "df"
984 + ]
985 + },
986 + {
987 + "cell_type": "code",
988 + "execution_count": 22,
989 + "metadata": {},
990 + "outputs": [
991 + {
992 + "name": "stdout",
993 + "output_type": "stream",
994 + "text": [
995 + "(14470, 76, 76)\n"
996 + ]
997 + }
998 + ],
999 + "source": [
1000 + "print(x_test.shape)"
1001 + ]
1002 + },
1003 + {
1004 + "cell_type": "code",
1005 + "execution_count": 23,
1006 + "metadata": {},
1007 + "outputs": [],
1008 + "source": [
1009 + "# Model\n",
1010 + "from keras.models import Sequential\n",
1011 + "from keras.layers import Dense, LSTM, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D\n",
1012 + "from keras.utils import np_utils\n",
1013 + "from keras.layers.normalization import BatchNormalization"
1014 + ]
1015 + },
1016 + {
1017 + "cell_type": "code",
1018 + "execution_count": 28,
1019 + "metadata": {},
1020 + "outputs": [
1021 + {
1022 + "name": "stdout",
1023 + "output_type": "stream",
1024 + "text": [
1025 + "Model: \"sequential_1\"\n",
1026 + "_________________________________________________________________\n",
1027 + "Layer (type) Output Shape Param # \n",
1028 + "=================================================================\n",
1029 + "conv1d_1 (Conv1D) (None, 74, 64) 14656 \n",
1030 + "_________________________________________________________________\n",
1031 + "global_average_pooling1d_1 ( (None, 64) 0 \n",
1032 + "_________________________________________________________________\n",
1033 + "dropout_1 (Dropout) (None, 64) 0 \n",
1034 + "_________________________________________________________________\n",
1035 + "dense_1 (Dense) (None, 1) 65 \n",
1036 + "=================================================================\n",
1037 + "Total params: 14,721\n",
1038 + "Trainable params: 14,721\n",
1039 + "Non-trainable params: 0\n",
1040 + "_________________________________________________________________\n"
1041 + ]
1042 + }
1043 + ],
1044 + "source": [
1045 + "model = Sequential()\n",
1046 + "model.add(Conv1D(64, 3, input_shape=(76, 76), activation='relu'))\n",
1047 + "#model.add(Conv1D(64, 3, activation='relu'))\n",
1048 + "#model.add(MaxPooling1D(2))\n",
1049 + "#model.add(Conv1D(128, 2, activation='relu'))\n",
1050 + "#model.add(Conv1D(128, 2, activation='relu'))\n",
1051 + "model.add(GlobalAveragePooling1D())\n",
1052 + "model.add(Dropout(0.5))\n",
1053 + "model.add(Dense(1, activation='sigmoid'))\n",
1054 + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
1055 + "model.summary()\n",
1056 + "model.save('DoSCNN.h5')"
1057 + ]
1058 + },
1059 + {
1060 + "cell_type": "code",
1061 + "execution_count": 25,
1062 + "metadata": {},
1063 + "outputs": [
1064 + {
1065 + "name": "stdout",
1066 + "output_type": "stream",
1067 + "text": [
1068 + "Epoch 1/10\n",
1069 + "338/338 [==============================] - 3s 8ms/step - loss: 0.1279 - accuracy: 0.9501 - val_loss: 0.0247 - val_accuracy: 0.9948\n",
1070 + "Epoch 2/10\n",
1071 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0276 - accuracy: 0.9941 - val_loss: 0.0199 - val_accuracy: 0.9950\n",
1072 + "Epoch 3/10\n",
1073 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0217 - accuracy: 0.9952 - val_loss: 0.0175 - val_accuracy: 0.9953\n",
1074 + "Epoch 4/10\n",
1075 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0183 - accuracy: 0.9959 - val_loss: 0.0164 - val_accuracy: 0.9957\n",
1076 + "Epoch 5/10\n",
1077 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0174 - accuracy: 0.9963 - val_loss: 0.0163 - val_accuracy: 0.9960\n",
1078 + "Epoch 6/10\n",
1079 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0157 - accuracy: 0.9967 - val_loss: 0.0136 - val_accuracy: 0.9966\n",
1080 + "Epoch 7/10\n",
1081 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0142 - accuracy: 0.9971 - val_loss: 0.0126 - val_accuracy: 0.9970\n",
1082 + "Epoch 8/10\n",
1083 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0135 - accuracy: 0.9972 - val_loss: 0.0115 - val_accuracy: 0.9973\n",
1084 + "Epoch 9/10\n",
1085 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0118 - accuracy: 0.9975 - val_loss: 0.0112 - val_accuracy: 0.9973\n",
1086 + "Epoch 10/10\n",
1087 + "338/338 [==============================] - 2s 7ms/step - loss: 0.0114 - accuracy: 0.9977 - val_loss: 0.0101 - val_accuracy: 0.9976\n"
1088 + ]
1089 + }
1090 + ],
1091 + "source": [
1092 + "history = model.fit(x_train, y_train, batch_size=100, epochs=10,validation_data=(x_test, y_test))"
1093 + ]
1094 + },
1095 + {
1096 + "cell_type": "code",
1097 + "execution_count": 27,
1098 + "metadata": {},
1099 + "outputs": [
1100 + {
1101 + "name": "stdout",
1102 + "output_type": "stream",
1103 + "text": [
1104 + "453/453 [==============================] - 1s 1ms/step - loss: 0.0101 - accuracy: 0.9976\n",
1105 + "\n",
1106 + " Test accuracy : 0.9976\n"
1107 + ]
1108 + }
1109 + ],
1110 + "source": [
1111 + "print(\"\\n Test accuracy : %.4f\"%(model.evaluate(x_test, y_test)[1]))"
1112 + ]
1113 + },
1114 + {
1115 + "cell_type": "code",
1116 + "execution_count": null,
1117 + "metadata": {},
1118 + "outputs": [],
1119 + "source": []
1120 + }
1121 + ],
1122 + "metadata": {
1123 + "kernelspec": {
1124 + "display_name": "Python 3",
1125 + "language": "python",
1126 + "name": "python3"
1127 + },
1128 + "language_info": {
1129 + "codemirror_mode": {
1130 + "name": "ipython",
1131 + "version": 3
1132 + },
1133 + "file_extension": ".py",
1134 + "mimetype": "text/x-python",
1135 + "name": "python",
1136 + "nbconvert_exporter": "python",
1137 + "pygments_lexer": "ipython3",
1138 + "version": "3.8.5"
1139 + }
1140 + },
1141 + "nbformat": 4,
1142 + "nbformat_minor": 4
1143 +}
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 2,
6 + "metadata": {
7 + "scrolled": true
8 + },
9 + "outputs": [],
10 + "source": [
11 + "import csv\n",
12 + "import numpy as np\n",
13 + "import os\n",
14 + "import pandas as pd"
15 + ]
16 + },
17 + {
18 + "cell_type": "markdown",
19 + "metadata": {},
20 + "source": [
21 + "# 10진수 변환 (한번만)"
22 + ]
23 + },
24 + {
25 + "cell_type": "code",
26 + "execution_count": 4,
27 + "metadata": {},
28 + "outputs": [],
29 + "source": [
30 + "def transferData(dir_, outputdir_) :\n",
31 + " outputfile = open(outputdir_, 'w', newline='\\n')\n",
32 + " fopen = open(dir_, 'r')\n",
33 + " csvreader = csv.reader(fopen)\n",
34 + " \n",
35 + " fw = csv.writer(outputfile)\n",
36 + " \n",
37 + " for line in csvreader :\n",
38 + " newdata = []\n",
39 + " can_id = int(line[1], 16)\n",
40 + " newdata.append(can_id)\n",
41 + " newdata.append(line[2])\n",
42 + " \n",
43 + " ll = len(line)\n",
44 + " num = int(line[2])\n",
45 + " \n",
46 + " data = []\n",
47 + " if num == 8 :\n",
48 + " data = line[3:11]\n",
49 + " else :\n",
50 + " data = line[3:3+num]\n",
51 + " for _ in range(8-num):\n",
52 + " data.append('0')\n",
53 + " #print(line)\n",
54 + " newdata.extend(list(map(lambda x:int(x, 16), data)))\n",
55 + " \n",
56 + " if line[-1] == 'T' :\n",
57 + " newdata.append(0)\n",
58 + " else :\n",
59 + " newdata.append(1)\n",
60 + " \n",
61 + " fw.writerow(newdata)"
62 + ]
63 + },
64 + {
65 + "cell_type": "code",
66 + "execution_count": 5,
67 + "metadata": {},
68 + "outputs": [],
69 + "source": [
70 + "#transferData('Fuzzy_dataset.csv', 'Fuzzy.csv')"
71 + ]
72 + },
73 + {
74 + "cell_type": "markdown",
75 + "metadata": {},
76 + "source": [
77 + "# 2진수 변환 후 저장 (한번만) <br>\n",
78 + "Timestamp, DLC 제외"
79 + ]
80 + },
81 + {
82 + "cell_type": "code",
83 + "execution_count": 3,
84 + "metadata": {},
85 + "outputs": [],
86 + "source": [
87 + "f = open('Fuzzy.csv', 'r')\n",
88 + "#ff = open('DoSres.csv', 'w', newline='\\n')\n",
89 + "cr = csv.reader(f)\n",
90 + "#fw = csv.writer(ff)\n",
91 + "\n",
92 + "a = []\n",
93 + "for line in cr:\n",
94 + " #a = []\n",
95 + " CANID = []\n",
96 + " b = []\n",
97 + " canid = bin(int(line[0]))\n",
98 + " #remove 'b0'\n",
99 + " CANID.extend(canid[2:])\n",
100 + " #padding Max len : 11\n",
101 + " n = len(canid[2:])\n",
102 + " if n < 11 :\n",
103 + " padding = ['0' for _ in range(11-n)]\n",
104 + " b.extend(padding)\n",
105 + " b.extend(CANID)\n",
106 + " \n",
107 + " # Data 0~7\n",
108 + " DATA = []\n",
109 + " for i in range(2, 10):\n",
110 + " data = bin(int(line[i]))\n",
111 + " #remove 'b0'\n",
112 + " DATA.extend(data[2:])\n",
113 + " #padding Max len : 8\n",
114 + " n = len(data[2:])\n",
115 + " if n < 8 :\n",
116 + " padding = ['0' for _ in range(8-n)]\n",
117 + " b.extend(padding)\n",
118 + " b.extend(DATA)\n",
119 + " \n",
120 + " # Class \n",
121 + " b.extend(line[-1])\n",
122 + " a.append(b)\n",
123 + " \n",
124 + " #fw.writerow(a)\n",
125 + "\n",
126 + "#ff.close()\n",
127 + "f.close()"
128 + ]
129 + },
130 + {
131 + "cell_type": "code",
132 + "execution_count": 4,
133 + "metadata": {},
134 + "outputs": [
135 + {
136 + "name": "stdout",
137 + "output_type": "stream",
138 + "text": [
139 + "76 ['1', '0', '1', '0', '1', '0', '0', '0', '1', '0', '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', '0', '0', '0', '0', '0', '1', '1', '0', '1', '1', '0', '0', '0', '0', '0', '1', '0', '0', '0', '1', '0', '1', '0', '0', '0', '0', '0', '1']\n",
140 + "76 ['0', '1', '0', '1', '0', '1', '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', '0', '0', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '1', '0', '1', '0', '1', '1', '0', '0', '1', '0', '0', '1', '0', '0', '0', '1']\n",
141 + "3838860\n"
142 + ]
143 + }
144 + ],
145 + "source": [
146 + "print(len(a[0]),a[0])\n",
147 + "print(len(a[1]), a[1])\n",
148 + "print(len(a))"
149 + ]
150 + },
151 + {
152 + "cell_type": "markdown",
153 + "metadata": {},
154 + "source": [
155 + "# 76*76 배열 구성"
156 + ]
157 + },
158 + {
159 + "cell_type": "code",
160 + "execution_count": 5,
161 + "metadata": {},
162 + "outputs": [],
163 + "source": [
164 + "def create2D(data) :\n",
165 + " arr = []\n",
166 + " for i in range(0, len(data)- 75, 76): \n",
167 + " d = []\n",
168 + " for x in range(0, 76) :\n",
169 + " ll = list(map(float, data[i+x]))\n",
170 + " d.append(ll)\n",
171 + " arr.append(d)\n",
172 + " \n",
173 + " return arr"
174 + ]
175 + },
176 + {
177 + "cell_type": "code",
178 + "execution_count": 6,
179 + "metadata": {},
180 + "outputs": [],
181 + "source": [
182 + "resData = create2D(a)\n",
183 + "del a"
184 + ]
185 + },
186 + {
187 + "cell_type": "code",
188 + "execution_count": 10,
189 + "metadata": {},
190 + "outputs": [
191 + {
192 + "name": "stdout",
193 + "output_type": "stream",
194 + "text": [
195 + "50511\n"
196 + ]
197 + },
198 + {
199 + "data": {
200 + "text/plain": [
201 + "3838836"
202 + ]
203 + },
204 + "execution_count": 10,
205 + "metadata": {},
206 + "output_type": "execute_result"
207 + }
208 + ],
209 + "source": [
210 + "#print(len(resData[0]), resData[0])\n",
211 + "print(len(resData))"
212 + ]
213 + },
214 + {
215 + "cell_type": "code",
216 + "execution_count": 13,
217 + "metadata": {},
218 + "outputs": [
219 + {
220 + "name": "stdout",
221 + "output_type": "stream",
222 + "text": [
223 + "[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]\n",
224 + "76\n",
225 + "76\n"
226 + ]
227 + }
228 + ],
229 + "source": [
230 + "print(resData[0][0])\n",
231 + "print(len(resData[0][0]))\n",
232 + "print(len(resData[0]))"
233 + ]
234 + },
235 + {
236 + "cell_type": "markdown",
237 + "metadata": {},
238 + "source": [
239 + "# 비정상 메시지 포함 유무 체크"
240 + ]
241 + },
242 + {
243 + "cell_type": "code",
244 + "execution_count": 14,
245 + "metadata": {},
246 + "outputs": [],
247 + "source": [
248 + "def check(data):\n",
249 + " check = []\n",
250 + " \n",
251 + " for e in data :\n",
252 + " flag = False\n",
253 + " for i in e :\n",
254 + " if i[-1] == 0 :\n",
255 + " check.append(0)\n",
256 + " flag = True\n",
257 + " break\n",
258 + " if flag == False :\n",
259 + " check.append(1)\n",
260 + " \n",
261 + " return check\n",
262 + "\n",
263 + "checkblock = check(resData)"
264 + ]
265 + },
266 + {
267 + "cell_type": "code",
268 + "execution_count": 15,
269 + "metadata": {},
270 + "outputs": [
271 + {
272 + "name": "stdout",
273 + "output_type": "stream",
274 + "text": [
275 + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]\n",
276 + "50511\n"
277 + ]
278 + }
279 + ],
280 + "source": [
281 + "print(checkblock[10010:10030])\n",
282 + "print(len(checkblock))"
283 + ]
284 + },
285 + {
286 + "cell_type": "markdown",
287 + "metadata": {},
288 + "source": [
289 + "# 데이터 개수 계산"
290 + ]
291 + },
292 + {
293 + "cell_type": "code",
294 + "execution_count": 16,
295 + "metadata": {},
296 + "outputs": [
297 + {
298 + "name": "stdout",
299 + "output_type": "stream",
300 + "text": [
301 + "33352\n",
302 + "17159\n"
303 + ]
304 + }
305 + ],
306 + "source": [
307 + "cnt = 0\n",
308 + "for e in checkblock :\n",
309 + " if e == 1 :\n",
310 + " cnt += 1\n",
311 + " \n",
312 + "print(cnt)\n",
313 + "print(len(checkblock)-cnt)"
314 + ]
315 + },
316 + {
317 + "cell_type": "markdown",
318 + "metadata": {},
319 + "source": [
320 + "# 테스트, 트레인 데이터 나누기"
321 + ]
322 + },
323 + {
324 + "cell_type": "code",
325 + "execution_count": 17,
326 + "metadata": {},
327 + "outputs": [],
328 + "source": [
329 + "from sklearn.model_selection import train_test_split\n",
330 + "import tensorflow as tf"
331 + ]
332 + },
333 + {
334 + "cell_type": "code",
335 + "execution_count": 18,
336 + "metadata": {
337 + "scrolled": true
338 + },
339 + "outputs": [],
340 + "source": [
341 + "df = pd.DataFrame(resData)\n",
342 + "n = len(df)\n",
343 + "\n",
344 + "X_train = df.iloc[0:n, 0:76].values.tolist()\n",
345 + "Y_train = checkblock\n",
346 + "\n",
347 + "X_train = np.asarray(X_train)\n",
348 + "Y_train = np.asarray(Y_train)\n",
349 + "#X_train = tf.constant(X_train)\n",
350 + "#print(X_train[-1])\n",
351 + "#Y_train = tf.convert_to_tensor(Y_train)\n",
352 + "\n",
353 + "#Y_train = np.squeeze(Y_train, axis=1)\n",
354 + "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3, random_state=321)"
355 + ]
356 + },
357 + {
358 + "cell_type": "code",
359 + "execution_count": 19,
360 + "metadata": {
361 + "scrolled": false
362 + },
363 + "outputs": [
364 + {
365 + "name": "stdout",
366 + "output_type": "stream",
367 + "text": [
368 + "(50511, 76, 76)\n",
369 + "(50511,)\n"
370 + ]
371 + }
372 + ],
373 + "source": [
374 + "print(X_train.shape)\n",
375 + "print(Y_train.shape)"
376 + ]
377 + },
378 + {
379 + "cell_type": "code",
380 + "execution_count": 241,
381 + "metadata": {},
382 + "outputs": [],
383 + "source": [
384 + "for i in x_train:\n",
385 + " for j in i:\n",
386 + " if len(j) != 11:\n",
387 + " print(j)"
388 + ]
389 + },
390 + {
391 + "cell_type": "code",
392 + "execution_count": 20,
393 + "metadata": {
394 + "scrolled": true
395 + },
396 + "outputs": [
397 + {
398 + "data": {
399 + "text/html": [
400 + "<div>\n",
401 + "<style scoped>\n",
402 + " .dataframe tbody tr th:only-of-type {\n",
403 + " vertical-align: middle;\n",
404 + " }\n",
405 + "\n",
406 + " .dataframe tbody tr th {\n",
407 + " vertical-align: top;\n",
408 + " }\n",
409 + "\n",
410 + " .dataframe thead th {\n",
411 + " text-align: right;\n",
412 + " }\n",
413 + "</style>\n",
414 + "<table border=\"1\" class=\"dataframe\">\n",
415 + " <thead>\n",
416 + " <tr style=\"text-align: right;\">\n",
417 + " <th></th>\n",
418 + " <th>0</th>\n",
419 + " <th>1</th>\n",
420 + " <th>2</th>\n",
421 + " <th>3</th>\n",
422 + " <th>4</th>\n",
423 + " <th>5</th>\n",
424 + " <th>6</th>\n",
425 + " <th>7</th>\n",
426 + " <th>8</th>\n",
427 + " <th>9</th>\n",
428 + " <th>...</th>\n",
429 + " <th>66</th>\n",
430 + " <th>67</th>\n",
431 + " <th>68</th>\n",
432 + " <th>69</th>\n",
433 + " <th>70</th>\n",
434 + " <th>71</th>\n",
435 + " <th>72</th>\n",
436 + " <th>73</th>\n",
437 + " <th>74</th>\n",
438 + " <th>75</th>\n",
439 + " </tr>\n",
440 + " </thead>\n",
441 + " <tbody>\n",
442 + " <tr>\n",
443 + " <th>0</th>\n",
444 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
445 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
446 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
447 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
448 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
449 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
450 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
451 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
452 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
453 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
454 + " <td>...</td>\n",
455 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
456 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
457 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
458 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
459 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
460 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
461 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
462 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
463 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
464 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
465 + " </tr>\n",
466 + " <tr>\n",
467 + " <th>1</th>\n",
468 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
469 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
470 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
471 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
472 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
473 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
474 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
475 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
476 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
477 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
478 + " <td>...</td>\n",
479 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
480 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
481 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
482 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
483 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
484 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
485 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
486 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
487 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
488 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
489 + " </tr>\n",
490 + " <tr>\n",
491 + " <th>2</th>\n",
492 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
493 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
494 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
495 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
496 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
497 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
498 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
499 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
500 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
501 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
502 + " <td>...</td>\n",
503 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
504 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
505 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
506 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
507 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
508 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
509 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
510 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
511 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
512 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
513 + " </tr>\n",
514 + " <tr>\n",
515 + " <th>3</th>\n",
516 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
517 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
518 + " <td>[1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
519 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
520 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
521 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
522 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
523 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
524 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
525 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
526 + " <td>...</td>\n",
527 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
528 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
529 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
530 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
531 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
532 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
533 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
534 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
535 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
536 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
537 + " </tr>\n",
538 + " <tr>\n",
539 + " <th>4</th>\n",
540 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
541 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
542 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
543 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
544 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
545 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
546 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
547 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
548 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
549 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
550 + " <td>...</td>\n",
551 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
552 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
553 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
554 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
555 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
556 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
557 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
558 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
559 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
560 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
561 + " </tr>\n",
562 + " <tr>\n",
563 + " <th>...</th>\n",
564 + " <td>...</td>\n",
565 + " <td>...</td>\n",
566 + " <td>...</td>\n",
567 + " <td>...</td>\n",
568 + " <td>...</td>\n",
569 + " <td>...</td>\n",
570 + " <td>...</td>\n",
571 + " <td>...</td>\n",
572 + " <td>...</td>\n",
573 + " <td>...</td>\n",
574 + " <td>...</td>\n",
575 + " <td>...</td>\n",
576 + " <td>...</td>\n",
577 + " <td>...</td>\n",
578 + " <td>...</td>\n",
579 + " <td>...</td>\n",
580 + " <td>...</td>\n",
581 + " <td>...</td>\n",
582 + " <td>...</td>\n",
583 + " <td>...</td>\n",
584 + " <td>...</td>\n",
585 + " </tr>\n",
586 + " <tr>\n",
587 + " <th>50506</th>\n",
588 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
589 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
590 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
591 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
592 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
593 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
594 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
595 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
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597 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
598 + " <td>...</td>\n",
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600 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
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608 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
609 + " </tr>\n",
610 + " <tr>\n",
611 + " <th>50507</th>\n",
612 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
613 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
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616 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
617 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
618 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
619 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
620 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
621 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
622 + " <td>...</td>\n",
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624 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
625 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
626 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
627 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
628 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
629 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
630 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
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632 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
633 + " </tr>\n",
634 + " <tr>\n",
635 + " <th>50508</th>\n",
636 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
637 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
638 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
639 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
640 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
641 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
642 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
643 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
644 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
645 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
646 + " <td>...</td>\n",
647 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
648 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
649 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
650 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
651 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
652 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
653 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
654 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
655 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
656 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
657 + " </tr>\n",
658 + " <tr>\n",
659 + " <th>50509</th>\n",
660 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
661 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
662 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
663 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
664 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
665 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
666 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
667 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
668 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
669 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
670 + " <td>...</td>\n",
671 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
672 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
673 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
674 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
675 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
676 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
677 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
678 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
679 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
680 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
681 + " </tr>\n",
682 + " <tr>\n",
683 + " <th>50510</th>\n",
684 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
685 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
686 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
687 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
688 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
689 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
690 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
691 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
692 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
693 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
694 + " <td>...</td>\n",
695 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
696 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
697 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
698 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
699 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
700 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
701 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
702 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
703 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
704 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
705 + " </tr>\n",
706 + " </tbody>\n",
707 + "</table>\n",
708 + "<p>50511 rows × 76 columns</p>\n",
709 + "</div>"
710 + ],
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802 + "\n",
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809 + "... ... \n",
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815 + "\n",
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828 + "\n",
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854 + "\n",
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874 + "... ... \n",
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880 + "\n",
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893 + "\n",
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900 + "... ... \n",
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906 + "\n",
907 + " 71 \\\n",
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912 + "4 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
913 + "... ... \n",
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919 + "\n",
920 + " 72 \\\n",
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924 + "3 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
925 + "4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
926 + "... ... \n",
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928 + "50507 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
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930 + "50509 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
931 + "50510 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
932 + "\n",
933 + " 73 \\\n",
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937 + "3 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
938 + "4 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
939 + "... ... \n",
940 + "50506 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
941 + "50507 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
942 + "50508 [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
943 + "50509 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
944 + "50510 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
945 + "\n",
946 + " 74 \\\n",
947 + "0 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
948 + "1 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
949 + "2 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
950 + "3 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
951 + "4 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
952 + "... ... \n",
953 + "50506 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
954 + "50507 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
955 + "50508 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
956 + "50509 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
957 + "50510 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
958 + "\n",
959 + " 75 \n",
960 + "0 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
961 + "1 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
962 + "2 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
963 + "3 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
964 + "4 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
965 + "... ... \n",
966 + "50506 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
967 + "50507 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
968 + "50508 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
969 + "50509 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
970 + "50510 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
971 + "\n",
972 + "[50511 rows x 76 columns]"
973 + ]
974 + },
975 + "execution_count": 20,
976 + "metadata": {},
977 + "output_type": "execute_result"
978 + }
979 + ],
980 + "source": [
981 + "df"
982 + ]
983 + },
984 + {
985 + "cell_type": "code",
986 + "execution_count": 21,
987 + "metadata": {},
988 + "outputs": [
989 + {
990 + "name": "stdout",
991 + "output_type": "stream",
992 + "text": [
993 + "(15154, 76, 76)\n"
994 + ]
995 + }
996 + ],
997 + "source": [
998 + "print(x_test.shape)"
999 + ]
1000 + },
1001 + {
1002 + "cell_type": "code",
1003 + "execution_count": 22,
1004 + "metadata": {},
1005 + "outputs": [],
1006 + "source": [
1007 + "# Model\n",
1008 + "from keras.models import Sequential\n",
1009 + "from keras.layers import Dense, LSTM, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D\n",
1010 + "from keras.utils import np_utils\n",
1011 + "from keras.layers.normalization import BatchNormalization"
1012 + ]
1013 + },
1014 + {
1015 + "cell_type": "code",
1016 + "execution_count": 23,
1017 + "metadata": {},
1018 + "outputs": [
1019 + {
1020 + "name": "stdout",
1021 + "output_type": "stream",
1022 + "text": [
1023 + "Model: \"sequential\"\n",
1024 + "_________________________________________________________________\n",
1025 + "Layer (type) Output Shape Param # \n",
1026 + "=================================================================\n",
1027 + "conv1d (Conv1D) (None, 74, 64) 14656 \n",
1028 + "_________________________________________________________________\n",
1029 + "global_average_pooling1d (Gl (None, 64) 0 \n",
1030 + "_________________________________________________________________\n",
1031 + "dropout (Dropout) (None, 64) 0 \n",
1032 + "_________________________________________________________________\n",
1033 + "dense (Dense) (None, 1) 65 \n",
1034 + "=================================================================\n",
1035 + "Total params: 14,721\n",
1036 + "Trainable params: 14,721\n",
1037 + "Non-trainable params: 0\n",
1038 + "_________________________________________________________________\n"
1039 + ]
1040 + }
1041 + ],
1042 + "source": [
1043 + "model = Sequential()\n",
1044 + "model.add(Conv1D(64, 3, input_shape=(76, 76), activation='relu'))\n",
1045 + "#model.add(Conv1D(64, 3, activation='relu'))\n",
1046 + "#model.add(MaxPooling1D(2))\n",
1047 + "#model.add(Conv1D(128, 2, activation='relu'))\n",
1048 + "#model.add(Conv1D(128, 2, activation='relu'))\n",
1049 + "model.add(GlobalAveragePooling1D())\n",
1050 + "model.add(Dropout(0.5))\n",
1051 + "model.add(Dense(1, activation='sigmoid'))\n",
1052 + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
1053 + "model.summary()\n",
1054 + "model.save('FuzzyCNN.h5')"
1055 + ]
1056 + },
1057 + {
1058 + "cell_type": "code",
1059 + "execution_count": 24,
1060 + "metadata": {},
1061 + "outputs": [
1062 + {
1063 + "name": "stdout",
1064 + "output_type": "stream",
1065 + "text": [
1066 + "Epoch 1/10\n",
1067 + "354/354 [==============================] - 3s 8ms/step - loss: 0.0752 - accuracy: 0.9806 - val_loss: 0.0163 - val_accuracy: 0.9957782 - accuracy: \n",
1068 + "Epoch 2/10\n",
1069 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0164 - accuracy: 0.9964 - val_loss: 0.0136 - val_accuracy: 0.9965\n",
1070 + "Epoch 3/10\n",
1071 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0127 - accuracy: 0.9972 - val_loss: 0.0130 - val_accuracy: 0.9968\n",
1072 + "Epoch 4/10\n",
1073 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0103 - accuracy: 0.9979 - val_loss: 0.0105 - val_accuracy: 0.9976\n",
1074 + "Epoch 5/10\n",
1075 + "354/354 [==============================] - 3s 7ms/step - loss: 0.0094 - accuracy: 0.9982 - val_loss: 0.0092 - val_accuracy: 0.9978\n",
1076 + "Epoch 6/10\n",
1077 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0080 - accuracy: 0.9984 - val_loss: 0.0086 - val_accuracy: 0.9979\n",
1078 + "Epoch 7/10\n",
1079 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0075 - accuracy: 0.9985 - val_loss: 0.0093 - val_accuracy: 0.9979\n",
1080 + "Epoch 8/10\n",
1081 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0066 - accuracy: 0.9987 - val_loss: 0.0074 - val_accuracy: 0.9983\n",
1082 + "Epoch 9/10\n",
1083 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0064 - accuracy: 0.9987 - val_loss: 0.0064 - val_accuracy: 0.9985\n",
1084 + "Epoch 10/10\n",
1085 + "354/354 [==============================] - 2s 7ms/step - loss: 0.0056 - accuracy: 0.9989 - val_loss: 0.0056 - val_accuracy: 0.9988\n"
1086 + ]
1087 + }
1088 + ],
1089 + "source": [
1090 + "history = model.fit(x_train, y_train, batch_size=100, epochs=10,validation_data=(x_test, y_test))"
1091 + ]
1092 + },
1093 + {
1094 + "cell_type": "code",
1095 + "execution_count": 25,
1096 + "metadata": {},
1097 + "outputs": [
1098 + {
1099 + "name": "stdout",
1100 + "output_type": "stream",
1101 + "text": [
1102 + "474/474 [==============================] - 1s 1ms/step - loss: 0.0056 - accuracy: 0.9988\n",
1103 + "\n",
1104 + " Test accuracy : 0.9988\n"
1105 + ]
1106 + }
1107 + ],
1108 + "source": [
1109 + "print(\"\\n Test accuracy : %.4f\"%(model.evaluate(x_test, y_test)[1]))"
1110 + ]
1111 + },
1112 + {
1113 + "cell_type": "code",
1114 + "execution_count": null,
1115 + "metadata": {},
1116 + "outputs": [],
1117 + "source": []
1118 + }
1119 + ],
1120 + "metadata": {
1121 + "kernelspec": {
1122 + "display_name": "Python 3",
1123 + "language": "python",
1124 + "name": "python3"
1125 + },
1126 + "language_info": {
1127 + "codemirror_mode": {
1128 + "name": "ipython",
1129 + "version": 3
1130 + },
1131 + "file_extension": ".py",
1132 + "mimetype": "text/x-python",
1133 + "name": "python",
1134 + "nbconvert_exporter": "python",
1135 + "pygments_lexer": "ipython3",
1136 + "version": "3.8.5"
1137 + }
1138 + },
1139 + "nbformat": 4,
1140 + "nbformat_minor": 4
1141 +}
1 +{
2 + "cells": [
3 + {
4 + "cell_type": "code",
5 + "execution_count": 1,
6 + "metadata": {
7 + "scrolled": true
8 + },
9 + "outputs": [],
10 + "source": [
11 + "import csv\n",
12 + "import numpy as np\n",
13 + "import os\n",
14 + "import pandas as pd"
15 + ]
16 + },
17 + {
18 + "cell_type": "markdown",
19 + "metadata": {},
20 + "source": [
21 + "# 10진수 변환 (한번만)"
22 + ]
23 + },
24 + {
25 + "cell_type": "code",
26 + "execution_count": 4,
27 + "metadata": {},
28 + "outputs": [],
29 + "source": [
30 + "def transferData(dir_, outputdir_) :\n",
31 + " outputfile = open(outputdir_, 'w', newline='\\n')\n",
32 + " fopen = open(dir_, 'r')\n",
33 + " csvreader = csv.reader(fopen)\n",
34 + " \n",
35 + " fw = csv.writer(outputfile)\n",
36 + " \n",
37 + " for line in csvreader :\n",
38 + " newdata = []\n",
39 + " can_id = int(line[1], 16)\n",
40 + " newdata.append(can_id)\n",
41 + " newdata.append(line[2])\n",
42 + " \n",
43 + " ll = len(line)\n",
44 + " num = int(line[2])\n",
45 + " \n",
46 + " data = []\n",
47 + " if num == 8 :\n",
48 + " data = line[3:11]\n",
49 + " else :\n",
50 + " data = line[3:3+num]\n",
51 + " for _ in range(8-num):\n",
52 + " data.append('0')\n",
53 + " #print(line)\n",
54 + " newdata.extend(list(map(lambda x:int(x, 16), data)))\n",
55 + " \n",
56 + " if line[-1] == 'T' :\n",
57 + " newdata.append(0)\n",
58 + " else :\n",
59 + " newdata.append(1)\n",
60 + " \n",
61 + " fw.writerow(newdata)"
62 + ]
63 + },
64 + {
65 + "cell_type": "code",
66 + "execution_count": 5,
67 + "metadata": {},
68 + "outputs": [],
69 + "source": [
70 + "#transferData('Spoofing_dataset.csv', 'Spoofing.csv')"
71 + ]
72 + },
73 + {
74 + "cell_type": "markdown",
75 + "metadata": {},
76 + "source": [
77 + "# 2진수 변환 후 저장 (한번만) <br>\n",
78 + "Timestamp, DLC 제외"
79 + ]
80 + },
81 + {
82 + "cell_type": "code",
83 + "execution_count": 4,
84 + "metadata": {},
85 + "outputs": [],
86 + "source": [
87 + "f = open('Spoofing.csv', 'r')\n",
88 + "#ff = open('.csv', 'w', newline='\\n')\n",
89 + "cr = csv.reader(f)\n",
90 + "#fw = csv.writer(ff)\n",
91 + "\n",
92 + "a = []\n",
93 + "for line in cr:\n",
94 + " #a = []\n",
95 + " CANID = []\n",
96 + " b = []\n",
97 + " canid = bin(int(line[0]))\n",
98 + " #remove 'b0'\n",
99 + " CANID.extend(canid[2:])\n",
100 + " #padding Max len : 11\n",
101 + " n = len(canid[2:])\n",
102 + " if n < 11 :\n",
103 + " padding = ['0' for _ in range(11-n)]\n",
104 + " b.extend(padding)\n",
105 + " b.extend(CANID)\n",
106 + " \n",
107 + " # Data 0~7\n",
108 + " DATA = []\n",
109 + " for i in range(2, 10):\n",
110 + " data = bin(int(line[i]))\n",
111 + " #remove 'b0'\n",
112 + " DATA.extend(data[2:])\n",
113 + " #padding Max len : 8\n",
114 + " n = len(data[2:])\n",
115 + " if n < 8 :\n",
116 + " padding = ['0' for _ in range(8-n)]\n",
117 + " b.extend(padding)\n",
118 + " b.extend(DATA)\n",
119 + " \n",
120 + " # Class \n",
121 + " b.extend(line[-1])\n",
122 + " a.append(b)\n",
123 + " \n",
124 + " #fw.writerow(a)\n",
125 + "\n",
126 + "#ff.close()\n",
127 + "f.close()"
128 + ]
129 + },
130 + {
131 + "cell_type": "code",
132 + "execution_count": 5,
133 + "metadata": {},
134 + "outputs": [
135 + {
136 + "name": "stdout",
137 + "output_type": "stream",
138 + "text": [
139 + "76 ['0', '0', '1', '0', '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', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '0', '0', '0', '1', '0', '1', '0', '0', '1', '1', '0', '1', '0', '1', '0', '1', '0', '0', '1', '0', '0', '1']\n",
140 + "76 ['0', '1', '0', '1', '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', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '1', '0', '1', '0', '1', '0', '0', '0', '0', '0', '0', '0', '1']\n",
141 + "4443142\n"
142 + ]
143 + }
144 + ],
145 + "source": [
146 + "print(len(a[0]),a[0])\n",
147 + "print(len(a[1]), a[1])\n",
148 + "print(len(a))"
149 + ]
150 + },
151 + {
152 + "cell_type": "markdown",
153 + "metadata": {},
154 + "source": [
155 + "# 76*76 배열 구성"
156 + ]
157 + },
158 + {
159 + "cell_type": "code",
160 + "execution_count": 6,
161 + "metadata": {},
162 + "outputs": [],
163 + "source": [
164 + "def create2D(data) :\n",
165 + " arr = []\n",
166 + " for i in range(0, len(data)- 75, 76): \n",
167 + " d = []\n",
168 + " for x in range(0, 76) :\n",
169 + " ll = list(map(float, data[i+x]))\n",
170 + " d.append(ll)\n",
171 + " arr.append(d)\n",
172 + " \n",
173 + " return arr"
174 + ]
175 + },
176 + {
177 + "cell_type": "code",
178 + "execution_count": 7,
179 + "metadata": {},
180 + "outputs": [],
181 + "source": [
182 + "resData = create2D(a)\n",
183 + "del a"
184 + ]
185 + },
186 + {
187 + "cell_type": "code",
188 + "execution_count": 8,
189 + "metadata": {},
190 + "outputs": [
191 + {
192 + "name": "stdout",
193 + "output_type": "stream",
194 + "text": [
195 + "58462\n"
196 + ]
197 + }
198 + ],
199 + "source": [
200 + "#print(len(resData[0]), resData[0])\n",
201 + "print(len(resData))"
202 + ]
203 + },
204 + {
205 + "cell_type": "code",
206 + "execution_count": 9,
207 + "metadata": {},
208 + "outputs": [
209 + {
210 + "name": "stdout",
211 + "output_type": "stream",
212 + "text": [
213 + "[0.0, 0.0, 1.0, 0.0, 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, 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, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]\n",
214 + "76\n",
215 + "76\n"
216 + ]
217 + }
218 + ],
219 + "source": [
220 + "print(resData[0][0])\n",
221 + "print(len(resData[0][0]))\n",
222 + "print(len(resData[0]))"
223 + ]
224 + },
225 + {
226 + "cell_type": "markdown",
227 + "metadata": {},
228 + "source": [
229 + "# 비정상 메시지 포함 유무 체크"
230 + ]
231 + },
232 + {
233 + "cell_type": "code",
234 + "execution_count": 10,
235 + "metadata": {},
236 + "outputs": [],
237 + "source": [
238 + "def check(data):\n",
239 + " check = []\n",
240 + " \n",
241 + " for e in data :\n",
242 + " flag = False\n",
243 + " for i in e :\n",
244 + " if i[-1] == 0 :\n",
245 + " check.append(0)\n",
246 + " flag = True\n",
247 + " break\n",
248 + " if flag == False :\n",
249 + " check.append(1)\n",
250 + " \n",
251 + " return check\n",
252 + "\n",
253 + "checkblock = check(resData)"
254 + ]
255 + },
256 + {
257 + "cell_type": "code",
258 + "execution_count": 11,
259 + "metadata": {},
260 + "outputs": [
261 + {
262 + "name": "stdout",
263 + "output_type": "stream",
264 + "text": [
265 + "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
266 + "58462\n"
267 + ]
268 + }
269 + ],
270 + "source": [
271 + "print(checkblock[10010:10030])\n",
272 + "print(len(checkblock))"
273 + ]
274 + },
275 + {
276 + "cell_type": "markdown",
277 + "metadata": {},
278 + "source": [
279 + "# 데이터 개수 계산"
280 + ]
281 + },
282 + {
283 + "cell_type": "code",
284 + "execution_count": 12,
285 + "metadata": {},
286 + "outputs": [
287 + {
288 + "name": "stdout",
289 + "output_type": "stream",
290 + "text": [
291 + "33370\n",
292 + "25092\n"
293 + ]
294 + }
295 + ],
296 + "source": [
297 + "cnt = 0\n",
298 + "for e in checkblock :\n",
299 + " if e == 1 :\n",
300 + " cnt += 1\n",
301 + " \n",
302 + "print(cnt)\n",
303 + "print(len(checkblock)-cnt)"
304 + ]
305 + },
306 + {
307 + "cell_type": "markdown",
308 + "metadata": {},
309 + "source": [
310 + "# 테스트, 트레인 데이터 나누기"
311 + ]
312 + },
313 + {
314 + "cell_type": "code",
315 + "execution_count": 13,
316 + "metadata": {},
317 + "outputs": [],
318 + "source": [
319 + "from sklearn.model_selection import train_test_split\n",
320 + "import tensorflow as tf"
321 + ]
322 + },
323 + {
324 + "cell_type": "code",
325 + "execution_count": 14,
326 + "metadata": {
327 + "scrolled": true
328 + },
329 + "outputs": [],
330 + "source": [
331 + "df = pd.DataFrame(resData)\n",
332 + "n = len(df)\n",
333 + "\n",
334 + "X_train = df.iloc[0:n, 0:76].values.tolist()\n",
335 + "Y_train = checkblock\n",
336 + "\n",
337 + "X_train = np.asarray(X_train)\n",
338 + "Y_train = np.asarray(Y_train)\n",
339 + "#X_train = tf.constant(X_train)\n",
340 + "#print(X_train[-1])\n",
341 + "#Y_train = tf.convert_to_tensor(Y_train)\n",
342 + "\n",
343 + "#Y_train = np.squeeze(Y_train, axis=1)\n",
344 + "x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.3, random_state=321)"
345 + ]
346 + },
347 + {
348 + "cell_type": "code",
349 + "execution_count": 15,
350 + "metadata": {
351 + "scrolled": false
352 + },
353 + "outputs": [
354 + {
355 + "name": "stdout",
356 + "output_type": "stream",
357 + "text": [
358 + "(58462, 76, 76)\n",
359 + "(58462,)\n"
360 + ]
361 + }
362 + ],
363 + "source": [
364 + "print(X_train.shape)\n",
365 + "print(Y_train.shape)"
366 + ]
367 + },
368 + {
369 + "cell_type": "code",
370 + "execution_count": 241,
371 + "metadata": {},
372 + "outputs": [],
373 + "source": [
374 + "for i in x_train:\n",
375 + " for j in i:\n",
376 + " if len(j) != 11:\n",
377 + " print(j)"
378 + ]
379 + },
380 + {
381 + "cell_type": "code",
382 + "execution_count": 16,
383 + "metadata": {
384 + "scrolled": true
385 + },
386 + "outputs": [
387 + {
388 + "data": {
389 + "text/html": [
390 + "<div>\n",
391 + "<style scoped>\n",
392 + " .dataframe tbody tr th:only-of-type {\n",
393 + " vertical-align: middle;\n",
394 + " }\n",
395 + "\n",
396 + " .dataframe tbody tr th {\n",
397 + " vertical-align: top;\n",
398 + " }\n",
399 + "\n",
400 + " .dataframe thead th {\n",
401 + " text-align: right;\n",
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403 + "</style>\n",
404 + "<table border=\"1\" class=\"dataframe\">\n",
405 + " <thead>\n",
406 + " <tr style=\"text-align: right;\">\n",
407 + " <th></th>\n",
408 + " <th>0</th>\n",
409 + " <th>1</th>\n",
410 + " <th>2</th>\n",
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427 + " <th>74</th>\n",
428 + " <th>75</th>\n",
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432 + " <tr>\n",
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436 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
437 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
438 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
439 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
440 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
441 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
442 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
443 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
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446 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
447 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
448 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
449 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
450 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
451 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
452 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
453 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
454 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
455 + " </tr>\n",
456 + " <tr>\n",
457 + " <th>1</th>\n",
458 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
459 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
460 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
461 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
462 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
463 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
464 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
465 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
466 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
467 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
468 + " <td>...</td>\n",
469 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
470 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
471 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
472 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
473 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
474 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
475 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
476 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
477 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
478 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
479 + " </tr>\n",
480 + " <tr>\n",
481 + " <th>2</th>\n",
482 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
483 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
484 + " <td>[1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
485 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
486 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
487 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
488 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
489 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
490 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
491 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
492 + " <td>...</td>\n",
493 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
494 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
495 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
496 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
497 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
498 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
499 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
500 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
501 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
502 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
503 + " </tr>\n",
504 + " <tr>\n",
505 + " <th>3</th>\n",
506 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
507 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
508 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
509 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
510 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
511 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
512 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
513 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
514 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
515 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
516 + " <td>...</td>\n",
517 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
518 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
519 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
520 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
521 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
522 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
523 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
524 + " <td>[0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
525 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
526 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
527 + " </tr>\n",
528 + " <tr>\n",
529 + " <th>4</th>\n",
530 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
531 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
532 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
533 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
534 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
535 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
536 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
537 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
538 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
539 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
540 + " <td>...</td>\n",
541 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
542 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
543 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
544 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
545 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
546 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
547 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
548 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
549 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
550 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
551 + " </tr>\n",
552 + " <tr>\n",
553 + " <th>...</th>\n",
554 + " <td>...</td>\n",
555 + " <td>...</td>\n",
556 + " <td>...</td>\n",
557 + " <td>...</td>\n",
558 + " <td>...</td>\n",
559 + " <td>...</td>\n",
560 + " <td>...</td>\n",
561 + " <td>...</td>\n",
562 + " <td>...</td>\n",
563 + " <td>...</td>\n",
564 + " <td>...</td>\n",
565 + " <td>...</td>\n",
566 + " <td>...</td>\n",
567 + " <td>...</td>\n",
568 + " <td>...</td>\n",
569 + " <td>...</td>\n",
570 + " <td>...</td>\n",
571 + " <td>...</td>\n",
572 + " <td>...</td>\n",
573 + " <td>...</td>\n",
574 + " <td>...</td>\n",
575 + " </tr>\n",
576 + " <tr>\n",
577 + " <th>58457</th>\n",
578 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
579 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
580 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
581 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
582 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
583 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
584 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
585 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
586 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
587 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
588 + " <td>...</td>\n",
589 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
590 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
591 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
592 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
593 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
594 + " <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
595 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
596 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
597 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
598 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
599 + " </tr>\n",
600 + " <tr>\n",
601 + " <th>58458</th>\n",
602 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
603 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
604 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
605 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
606 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
607 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
608 + " <td>[1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
609 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
610 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
611 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
612 + " <td>...</td>\n",
613 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
614 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
615 + " <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
616 + " <td>[1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
617 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
618 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
619 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
620 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
621 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
622 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
623 + " </tr>\n",
624 + " <tr>\n",
625 + " <th>58459</th>\n",
626 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
627 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
628 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
629 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
630 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
631 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
632 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
633 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
634 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
635 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
636 + " <td>...</td>\n",
637 + " <td>[1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
638 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
639 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
640 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
641 + " <td>[0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
642 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ...</td>\n",
643 + " <td>[1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ...</td>\n",
644 + " <td>[0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
645 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ...</td>\n",
646 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
647 + " </tr>\n",
648 + " <tr>\n",
649 + " <th>58460</th>\n",
650 + " <td>[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
651 + " <td>[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
652 + " <td>[1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
653 + " <td>[0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
654 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
655 + " <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
656 + " <td>[0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ...</td>\n",
657 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
658 + " <td>[0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ...</td>\n",
659 + " <td>[0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
660 + " <td>...</td>\n",
661 + " <td>[0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ...</td>\n",
662 + " <td>[0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ...</td>\n",
663 + " <td>[0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ...</td>\n",
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671 + " </tr>\n",
672 + " <tr>\n",
673 + " <th>58461</th>\n",
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684 + " <td>...</td>\n",
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695 + " </tr>\n",
696 + " </tbody>\n",
697 + "</table>\n",
698 + "<p>58462 rows × 76 columns</p>\n",
699 + "</div>"
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870 + "\n",
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876 + "4 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
877 + "... ... \n",
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882 + "58461 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
883 + "\n",
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890 + "... ... \n",
891 + "58457 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
892 + "58458 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
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896 + "\n",
897 + " 71 \\\n",
898 + "0 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
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902 + "4 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
903 + "... ... \n",
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908 + "58461 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
909 + "\n",
910 + " 72 \\\n",
911 + "0 [0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
912 + "1 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
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914 + "3 [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
915 + "4 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
916 + "... ... \n",
917 + "58457 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
918 + "58458 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
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920 + "58460 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
921 + "58461 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
922 + "\n",
923 + " 73 \\\n",
924 + "0 [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
925 + "1 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
926 + "2 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
927 + "3 [0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
928 + "4 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
929 + "... ... \n",
930 + "58457 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
931 + "58458 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
932 + "58459 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
933 + "58460 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
934 + "58461 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
935 + "\n",
936 + " 74 \\\n",
937 + "0 [0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
938 + "1 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
939 + "2 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
940 + "3 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
941 + "4 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
942 + "... ... \n",
943 + "58457 [0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, ... \n",
944 + "58458 [0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, ... \n",
945 + "58459 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, ... \n",
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947 + "58461 [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
948 + "\n",
949 + " 75 \n",
950 + "0 [0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, ... \n",
951 + "1 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
952 + "2 [0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, ... \n",
953 + "3 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, ... \n",
954 + "4 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ... \n",
955 + "... ... \n",
956 + "58457 [0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, ... \n",
957 + "58458 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, ... \n",
958 + "58459 [0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, ... \n",
959 + "58460 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, ... \n",
960 + "58461 [1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, ... \n",
961 + "\n",
962 + "[58462 rows x 76 columns]"
963 + ]
964 + },
965 + "execution_count": 16,
966 + "metadata": {},
967 + "output_type": "execute_result"
968 + }
969 + ],
970 + "source": [
971 + "df"
972 + ]
973 + },
974 + {
975 + "cell_type": "code",
976 + "execution_count": 17,
977 + "metadata": {},
978 + "outputs": [
979 + {
980 + "name": "stdout",
981 + "output_type": "stream",
982 + "text": [
983 + "(17539, 76, 76)\n"
984 + ]
985 + }
986 + ],
987 + "source": [
988 + "print(x_test.shape)"
989 + ]
990 + },
991 + {
992 + "cell_type": "code",
993 + "execution_count": 18,
994 + "metadata": {},
995 + "outputs": [],
996 + "source": [
997 + "# Model\n",
998 + "from keras.models import Sequential\n",
999 + "from keras.layers import Dense, LSTM, Conv1D, Dropout, GlobalAveragePooling1D, MaxPooling1D\n",
1000 + "from keras.utils import np_utils\n",
1001 + "from keras.layers.normalization import BatchNormalization"
1002 + ]
1003 + },
1004 + {
1005 + "cell_type": "code",
1006 + "execution_count": 19,
1007 + "metadata": {},
1008 + "outputs": [
1009 + {
1010 + "name": "stdout",
1011 + "output_type": "stream",
1012 + "text": [
1013 + "Model: \"sequential\"\n",
1014 + "_________________________________________________________________\n",
1015 + "Layer (type) Output Shape Param # \n",
1016 + "=================================================================\n",
1017 + "conv1d (Conv1D) (None, 74, 64) 14656 \n",
1018 + "_________________________________________________________________\n",
1019 + "global_average_pooling1d (Gl (None, 64) 0 \n",
1020 + "_________________________________________________________________\n",
1021 + "dropout (Dropout) (None, 64) 0 \n",
1022 + "_________________________________________________________________\n",
1023 + "dense (Dense) (None, 1) 65 \n",
1024 + "=================================================================\n",
1025 + "Total params: 14,721\n",
1026 + "Trainable params: 14,721\n",
1027 + "Non-trainable params: 0\n",
1028 + "_________________________________________________________________\n"
1029 + ]
1030 + }
1031 + ],
1032 + "source": [
1033 + "model = Sequential()\n",
1034 + "model.add(Conv1D(64, 3, input_shape=(76, 76), activation='relu'))\n",
1035 + "model.add(GlobalAveragePooling1D())\n",
1036 + "model.add(Dropout(0.5))\n",
1037 + "model.add(Dense(1, activation='sigmoid'))\n",
1038 + "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
1039 + "model.summary()\n",
1040 + "model.save('SpoofingCNN.h5')"
1041 + ]
1042 + },
1043 + {
1044 + "cell_type": "code",
1045 + "execution_count": 20,
1046 + "metadata": {},
1047 + "outputs": [
1048 + {
1049 + "name": "stdout",
1050 + "output_type": "stream",
1051 + "text": [
1052 + "Epoch 1/10\n",
1053 + "410/410 [==============================] - 3s 8ms/step - loss: 0.0596 - accuracy: 0.9876 - val_loss: 0.0148 - val_accuracy: 0.9960\n",
1054 + "Epoch 2/10\n",
1055 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0137 - accuracy: 0.9970 - val_loss: 0.0136 - val_accuracy: 0.9965\n",
1056 + "Epoch 3/10\n",
1057 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0108 - accuracy: 0.9976 - val_loss: 0.0108 - val_accuracy: 0.9971\n",
1058 + "Epoch 4/10\n",
1059 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0096 - accuracy: 0.9980 - val_loss: 0.0104 - val_accuracy: 0.9975\n",
1060 + "Epoch 5/10\n",
1061 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0086 - accuracy: 0.9983 - val_loss: 0.0089 - val_accuracy: 0.9978\n",
1062 + "Epoch 6/10\n",
1063 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0069 - accuracy: 0.9986 - val_loss: 0.0081 - val_accuracy: 0.9979\n",
1064 + "Epoch 7/10\n",
1065 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0060 - accuracy: 0.9988 - val_loss: 0.0073 - val_accuracy: 0.9981\n",
1066 + "Epoch 8/10\n",
1067 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0054 - accuracy: 0.9989 - val_loss: 0.0076 - val_accuracy: 0.9981\n",
1068 + "Epoch 9/10\n",
1069 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0051 - accuracy: 0.9989 - val_loss: 0.0064 - val_accuracy: 0.9983\n",
1070 + "Epoch 10/10\n",
1071 + "410/410 [==============================] - 3s 7ms/step - loss: 0.0047 - accuracy: 0.9991 - val_loss: 0.0053 - val_accuracy: 0.9987\n"
1072 + ]
1073 + }
1074 + ],
1075 + "source": [
1076 + "history = model.fit(x_train, y_train, batch_size=100, epochs=10,validation_data=(x_test, y_test))"
1077 + ]
1078 + },
1079 + {
1080 + "cell_type": "code",
1081 + "execution_count": 21,
1082 + "metadata": {},
1083 + "outputs": [
1084 + {
1085 + "name": "stdout",
1086 + "output_type": "stream",
1087 + "text": [
1088 + "549/549 [==============================] - 1s 1ms/step - loss: 0.0053 - accuracy: 0.9987\n",
1089 + "\n",
1090 + " Test accuracy : 0.9987\n"
1091 + ]
1092 + }
1093 + ],
1094 + "source": [
1095 + "print(\"\\n Test accuracy : %.4f\"%(model.evaluate(x_test, y_test)[1]))"
1096 + ]
1097 + },
1098 + {
1099 + "cell_type": "code",
1100 + "execution_count": null,
1101 + "metadata": {},
1102 + "outputs": [],
1103 + "source": []
1104 + }
1105 + ],
1106 + "metadata": {
1107 + "kernelspec": {
1108 + "display_name": "Python 3",
1109 + "language": "python",
1110 + "name": "python3"
1111 + },
1112 + "language_info": {
1113 + "codemirror_mode": {
1114 + "name": "ipython",
1115 + "version": 3
1116 + },
1117 + "file_extension": ".py",
1118 + "mimetype": "text/x-python",
1119 + "name": "python",
1120 + "nbconvert_exporter": "python",
1121 + "pygments_lexer": "ipython3",
1122 + "version": "3.8.5"
1123 + }
1124 + },
1125 + "nbformat": 4,
1126 + "nbformat_minor": 4
1127 +}
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