├── README.md ├── __init__.py ├── dict ├── sentiment_stopword.txt ├── stopword.txt └── userdict.txt ├── mlmain.py ├── posnegmlfeature.py ├── posnegmlfeature.pyc ├── result ├── MLRESULTCOMPARE.doc ├── chengfengpolang_LRfinal.txt ├── chengfengpolang_MNBfinal.txt ├── chengfengpolang_SVMfinal.txt ├── greatewall_LRfinal.txt ├── greatewall_NBfinal.txt ├── greatewall_SVMfinal.txt ├── threemix_LRfinal.txt ├── threemix_NBfinal.txt ├── threemix_SVMfinal.txt ├── xiyoufuyaopian_LRfinal.txt ├── xiyoufuyaopian_MNBfinal.txt └── xiyoufuyaopian_SVMfinal.txt ├── seniment review set ├── CHENGFENGPOLANGNEG.xls ├── CHENGFENGPOLANGPOS.xls ├── CHENGFENGPOLANGTEST.xls ├── GREATEWALLNEG.xls ├── GREATEWALLPOS.xls ├── GREATEWALLTEST.xls ├── GREATEWALLTESTPOS.xls ├── THREEMIXNEG.xls ├── THREEMIXPOS.xls ├── THREEMIXTEST.xls ├── XIYOUFUYAOPIANNEG.xls ├── XIYOUFUYAOPIANPOS.xls ├── XIYOUFUYAOPIANTEST.xls └── oldresult │ ├── GRATEWALLTESTRESULTBERNOULLI.xls │ ├── GRATEWALLTESTRESULTLOGISTREGRESSION.xls │ ├── GRATEWALLTESTRESULTMUTIPLY.xls │ ├── GREATEWALLNEG.xls │ ├── GREATEWALLPOS.xls │ └── GREATEWALLTEST.xls ├── storesentimentclassifier.py ├── storesentimentclassifier.pyc ├── svmmain.py ├── textprocessing.py └── textprocessing.pyc /README.md: -------------------------------------------------------------------------------- 1 | # MoiveDataAnalysisByML 2 | 机器学习方法进行中文电影评论的情感分析 3 | -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/life-is-good/MoiveDataAnalysisByML/e80ea309a85086aae40aa2ac8c1f109193000db3/__init__.py -------------------------------------------------------------------------------- /dict/sentiment_stopword.txt: -------------------------------------------------------------------------------- 1 | 一番 2 | 一直 3 | 一个 4 | 一些 5 | 许多 6 | 种 7 | 有的是 8 | 也就是说 9 | 阿 10 | 哎呀 11 | 哎哟 12 | 俺 13 | 俺们 14 | 按 15 | 按照 16 | 吧 17 | 吧哒 18 | 把 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1346 | mean 1347 | meantime 1348 | meanwhile 1349 | merely 1350 | might 1351 | mightn't 1352 | mine 1353 | minus 1354 | miss 1355 | more 1356 | moreover 1357 | most 1358 | mostly 1359 | mr 1360 | mrs 1361 | much 1362 | must 1363 | mustn't 1364 | my 1365 | myself 1366 | n 1367 | name 1368 | namely 1369 | nd 1370 | near 1371 | nearly 1372 | necessary 1373 | need 1374 | needn't 1375 | needs 1376 | neither 1377 | never 1378 | neverf 1379 | neverless 1380 | nevertheless 1381 | new 1382 | next 1383 | nine 1384 | ninety 1385 | no 1386 | nobody 1387 | non 1388 | none 1389 | nonetheless 1390 | noone 1391 | no-one 1392 | nor 1393 | normally 1394 | not 1395 | nothing 1396 | notwithstanding 1397 | novel 1398 | now 1399 | nowhere 1400 | o 1401 | obviously 1402 | of 1403 | off 1404 | often 1405 | oh 1406 | ok 1407 | okay 1408 | old 1409 | on 1410 | once 1411 | one 1412 | ones 1413 | one's 1414 | only 1415 | onto 1416 | opposite 1417 | or 1418 | other 1419 | others 1420 | otherwise 1421 | ought 1422 | oughtn't 1423 | our 1424 | ours 1425 | ourselves 1426 | out 1427 | outside 1428 | over 1429 | overall 1430 | own 1431 | p 1432 | particular 1433 | particularly 1434 | past 1435 | per 1436 | perhaps 1437 | placed 1438 | please 1439 | plus 1440 | possible 1441 | presumably 1442 | probably 1443 | provided 1444 | provides 1445 | q 1446 | que 1447 | quite 1448 | qv 1449 | r 1450 | rather 1451 | rd 1452 | re 1453 | really 1454 | reasonably 1455 | recent 1456 | recently 1457 | regarding 1458 | regardless 1459 | regards 1460 | relatively 1461 | respectively 1462 | right 1463 | round 1464 | s 1465 | said 1466 | same 1467 | saw 1468 | say 1469 | saying 1470 | says 1471 | second 1472 | secondly 1473 | see 1474 | seeing 1475 | seem 1476 | seemed 1477 | seeming 1478 | seems 1479 | seen 1480 | self 1481 | selves 1482 | sensible 1483 | sent 1484 | serious 1485 | seriously 1486 | seven 1487 | several 1488 | shall 1489 | shan't 1490 | she 1491 | she'd 1492 | she'll 1493 | she's 1494 | should 1495 | 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where 1644 | whereafter 1645 | whereas 1646 | whereby 1647 | wherein 1648 | where's 1649 | whereupon 1650 | wherever 1651 | whether 1652 | which 1653 | whichever 1654 | while 1655 | whilst 1656 | whither 1657 | who 1658 | who'd 1659 | whoever 1660 | whole 1661 | who'll 1662 | whom 1663 | whomever 1664 | who's 1665 | whose 1666 | why 1667 | will 1668 | willing 1669 | wish 1670 | with 1671 | within 1672 | without 1673 | wonder 1674 | won't 1675 | would 1676 | wouldn't 1677 | x 1678 | y 1679 | yes 1680 | yet 1681 | you 1682 | you'd 1683 | you'll 1684 | your 1685 | you're 1686 | yours 1687 | yourself 1688 | yourselves 1689 | you've 1690 | z 1691 | zero -------------------------------------------------------------------------------- /dict/stopword.txt: -------------------------------------------------------------------------------- 1 | 一直 2 | 一个 3 | 一些 4 | 许多 5 | 种 6 | 有的是 7 | 也就是说 8 | 啊 9 | 阿 10 | 哎 11 | 哎呀 12 | 哎哟 13 | 唉 14 | 俺 15 | 俺们 16 | 按 17 | 按照 18 | 吧 19 | 吧哒 20 | 把 21 | 罢了 22 | 被 23 | 本 24 | 本着 25 | 比 26 | 比方 27 | 比如 28 | 鄙人 29 | 彼 30 | 彼此 31 | 边 32 | 别 33 | 别的 34 | 别说 35 | 并 36 | 并且 37 | 不比 38 | 不成 39 | 不单 40 | 不但 41 | 不独 42 | 不管 43 | 不光 44 | 不过 45 | 不仅 46 | 不拘 47 | 不论 48 | 不怕 49 | 不然 50 | 不如 51 | 不特 52 | 不惟 53 | 不问 54 | 不只 55 | 朝 56 | 朝着 57 | 趁 58 | 趁着 59 | 乘 60 | 冲 61 | 除 62 | 除此之外 63 | 除非 64 | 除了 65 | 此 66 | 此间 67 | 此外 68 | 从 69 | 从而 70 | 打 71 | 待 72 | 但 73 | 但是 74 | 当 75 | 当着 76 | 到 77 | 得 78 | 的 79 | 的话 80 | 等 81 | 等等 82 | 地 83 | 第 84 | 叮咚 85 | 对 86 | 对于 87 | 多 88 | 多少 89 | 而 90 | 而况 91 | 而且 92 | 而是 93 | 而外 94 | 而言 95 | 而已 96 | 尔后 97 | 反过来 98 | 反过来说 99 | 反之 100 | 非但 101 | 非徒 102 | 否则 103 | 嘎 104 | 嘎登 105 | 该 106 | 赶 107 | 个 108 | 各 109 | 各个 110 | 各位 111 | 各种 112 | 各自 113 | 给 114 | 根据 115 | 跟 116 | 故 117 | 故此 118 | 固然 119 | 关于 120 | 管 121 | 归 122 | 果然 123 | 果真 124 | 过 125 | 哈 126 | 哈哈 127 | 呵 128 | 和 129 | 何 130 | 何处 131 | 何况 132 | 何时 133 | 嘿 134 | 哼 135 | 哼唷 136 | 呼哧 137 | 乎 138 | 哗 139 | 还是 140 | 还有 141 | 换句话说 142 | 换言之 143 | 或 144 | 或是 145 | 或者 146 | 极了 147 | 及 148 | 及其 149 | 及至 150 | 即 151 | 即便 152 | 即或 153 | 即令 154 | 即若 155 | 即使 156 | 几 157 | 几时 158 | 己 159 | 既 160 | 既然 161 | 既是 162 | 继而 163 | 加之 164 | 假如 165 | 假若 166 | 假使 167 | 鉴于 168 | 将 169 | 较 170 | 较之 171 | 叫 172 | 接着 173 | 结果 174 | 借 175 | 紧接着 176 | 进而 177 | 尽 178 | 尽管 179 | 经 180 | 经过 181 | 就 182 | 就是 183 | 就是说 184 | 据 185 | 具体地说 186 | 具体说来 187 | 开始 188 | 开外 189 | 靠 190 | 咳 191 | 可 192 | 可见 193 | 可是 194 | 可以 195 | 况且 196 | 啦 197 | 来 198 | 来着 199 | 离 200 | 例如 201 | 哩 202 | 连 203 | 连同 204 | 两者 205 | 了 206 | 临 207 | 另 208 | 另外 209 | 另一方面 210 | 论 211 | 嘛 212 | 吗 213 | 慢说 214 | 漫说 215 | 冒 216 | 么 217 | 每 218 | 每当 219 | 们 220 | 莫若 221 | 某 222 | 某个 223 | 某些 224 | 拿 225 | 哪 226 | 哪边 227 | 哪儿 228 | 哪个 229 | 哪里 230 | 哪年 231 | 哪怕 232 | 哪天 233 | 哪些 234 | 哪样 235 | 那 236 | 那边 237 | 那儿 238 | 那个 239 | 那会儿 240 | 那里 241 | 那么 242 | 那么些 243 | 那么样 244 | 那时 245 | 那些 246 | 那样 247 | 乃 248 | 乃至 249 | 呢 250 | 能 251 | 你 252 | 你们 253 | 您 254 | 宁 255 | 宁可 256 | 宁肯 257 | 宁愿 258 | 哦 259 | 呕 260 | 啪达 261 | 旁人 262 | 呸 263 | 凭 264 | 凭借 265 | 其 266 | 其次 267 | 其二 268 | 其他 269 | 其它 270 | 其一 271 | 其余 272 | 其中 273 | 起 274 | 起见 275 | 起见 276 | 岂但 277 | 恰恰相反 278 | 前后 279 | 前者 280 | 且 281 | 然而 282 | 然后 283 | 然则 284 | 让 285 | 人家 286 | 任 287 | 任何 288 | 任凭 289 | 如 290 | 如此 291 | 如果 292 | 如何 293 | 如其 294 | 如若 295 | 如上所述 296 | 若 297 | 若非 298 | 若是 299 | 啥 300 | 上下 301 | 尚且 302 | 设若 303 | 设使 304 | 甚而 305 | 甚么 306 | 甚至 307 | 省得 308 | 时候 309 | 什么 310 | 什么样 311 | 使得 312 | 是 313 | 是的 314 | 首先 315 | 谁 316 | 谁知 317 | 顺 318 | 顺着 319 | 似的 320 | 虽 321 | 虽然 322 | 虽说 323 | 虽则 324 | 随 325 | 随着 326 | 所 327 | 所以 328 | 他 329 | 他们 330 | 他人 331 | 它 332 | 它们 333 | 她 334 | 她们 335 | 倘 336 | 倘或 337 | 倘然 338 | 倘若 339 | 倘使 340 | 腾 341 | 替 342 | 通过 343 | 同 344 | 同时 345 | 哇 346 | 万一 347 | 往 348 | 望 349 | 为 350 | 为何 351 | 为了 352 | 为什么 353 | 为着 354 | 喂 355 | 嗡嗡 356 | 我 357 | 我们 358 | 呜 359 | 呜呼 360 | 乌乎 361 | 无论 362 | 无宁 363 | 毋宁 364 | 嘻 365 | 吓 366 | 相对而言 367 | 像 368 | 向 369 | 向着 370 | 嘘 371 | 呀 372 | 焉 373 | 沿 374 | 沿着 375 | 要 376 | 要不 377 | 要不然 378 | 要不是 379 | 要么 380 | 要是 381 | 也 382 | 也罢 383 | 也好 384 | 一 385 | 一般 386 | 一旦 387 | 一方面 388 | 一来 389 | 一切 390 | 一样 391 | 一则 392 | 依 393 | 依照 394 | 矣 395 | 以 396 | 以便 397 | 以及 398 | 以免 399 | 以至 400 | 以至于 401 | 以致 402 | 抑或 403 | 因 404 | 因此 405 | 因而 406 | 因为 407 | 哟 408 | 用 409 | 由 410 | 由此可见 411 | 由于 412 | 有 413 | 有的 414 | 有关 415 | 有些 416 | 又 417 | 于 418 | 于是 419 | 于是乎 420 | 与 421 | 与此同时 422 | 与否 423 | 与其 424 | 越是 425 | 云云 426 | 哉 427 | 再说 428 | 再者 429 | 在 430 | 在下 431 | 咱 432 | 咱们 433 | 则 434 | 怎 435 | 怎么 436 | 怎么办 437 | 怎么样 438 | 怎样 439 | 咋 440 | 照 441 | 照着 442 | 者 443 | 这 444 | 这边 445 | 这儿 446 | 这个 447 | 这会儿 448 | 这就是说 449 | 这里 450 | 这么 451 | 这么点儿 452 | 这么些 453 | 这么样 454 | 这时 455 | 这些 456 | 这样 457 | 正如 458 | 吱 459 | 之 460 | 之类 461 | 之所以 462 | 之一 463 | 只是 464 | 只限 465 | 只要 466 | 只有 467 | 至 468 | 至于 469 | 诸位 470 | 着 471 | 着呢 472 | 自 473 | 自从 474 | 自个儿 475 | 自各儿 476 | 自己 477 | 自家 478 | 自身 479 | 综上所述 480 | 总的来看 481 | 总的来说 482 | 总的说来 483 | 总而言之 484 | 总之 485 | 纵 486 | 纵令 487 | 纵然 488 | 纵使 489 | 遵照 490 | 作为 491 | 兮 492 | 呃 493 | 呗 494 | 咚 495 | 咦 496 | 喏 497 | 啐 498 | 喔唷 499 | 嗬 500 | 嗯 501 | 嗳鹿晗井柏然吴邪小哥王母 502 | 。 503 | ， 504 | ？ 505 | ！ 506 | ： 507 | ； 508 | 、 509 | “ 510 | ” 511 | 【 512 | 】 513 | 《 514 | 》 515 | （ 516 | ） 517 | { 518 | } 519 | — 520 | ～ 521 | ＋ 522 | … 523 | …… 524 | . 525 | , 526 | ? 527 | ! 528 | : 529 | ; 530 | " 531 | " 532 | [ 533 | ] 534 | { 535 | } 536 | < 537 | > 538 | ( 539 | ) 540 | ~ 541 | @ 542 | # 543 | * 544 | & 545 | % 546 | ￥ 547 | $ 548 | - 549 | + 550 | = 551 | | 552 | \ 553 | / 554 | ` 555 | ... 556 | .... 557 | ...... 558 | ............ -------------------------------------------------------------------------------- /mlmain.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | import storesentimentclassifier as ssc # 分类器类 3 | import posnegmlfeature as pnf # 预测类 4 | import textprocessing as tp # 处理excel或者txt类 5 | import pickle 6 | from random import shuffle 7 | from nltk.classify.scikitlearn import SklearnClassifier 8 | import os 9 | import time 10 | 11 | if __name__ == "__main__": 12 | #三部电影《乘风破浪》《长城》《西游伏魔篇》，分别跑一遍，然后将三部电影合起来再跑一遍 13 | #使用MutiNB,LogisticRegression,SVM三种分类器 14 | 15 | print '开始训练分类器' 16 | # 1. Load positive and negative review data 17 | path = os.getcwd() 18 | print '当前路径'+path 19 | start_time = time.time() 20 | print start_time 21 | pos_review = tp.seg_fil_senti_excel(path+"\\seniment review set\\THREEMIXPOS.xls", 1, 1) 22 | neg_review = tp.seg_fil_senti_excel(path+"\\seniment review set\\THREEMIXNEG.xls", 1, 1) 23 | test_review = test_review = tp.seg_fil_senti_excel(path+"\\seniment review set\\THREEMIXTEST.xls", 1, 1) 24 | 25 | pos = pos_review 26 | neg = neg_review 27 | 28 | # 2. Feature extraction function 29 | # Choose word_scores extaction methods 30 | #word_scores = create_word_scores() 31 | #word_scores = create_bigram_scores() 32 | word_scores = ssc.create_word_bigram_scores(pos,neg) 33 | 34 | # 3. Transform review to features by setting labels to words in review 35 | best_words = ssc.find_best_words(word_scores, 1500) # Set dimension and initiallize most informative words 36 | 37 | # posFeatures = ssc.pos_features(ssc.bigrams) 38 | # negFeatures = ssc.neg_features(ssc.bigrams) 39 | 40 | # posFeatures = ssc.pos_features(ssc.bigram_words) 41 | # negFeatures = ssc.neg_features(ssc.bigram_words) 42 | 43 | # posFeatures = ssc.pos_features(ssc.best_word_features) 44 | # negFeatures = ssc.neg_features(ssc.best_word_features) 45 | 46 | posFeatures = ssc.pos_features(pos,ssc.best_word_features_com,best_words) 47 | negFeatures = ssc.neg_features(neg,ssc.best_word_features_com,best_words) 48 | 49 | # 4. Train classifier and examing classify accuracy 50 | # Make the feature set ramdon 51 | shuffle(posFeatures) 52 | shuffle(negFeatures) 53 | 54 | # 5. After finding the best classifier,store it and then check different dimension classification accuracy 55 | # 75% of features used as training set (in fact, it have a better way by using cross validation function) 56 | size_pos = int(len(pos_review) * 0.75) 57 | size_neg = int(len(neg_review) * 0.75) 58 | 59 | train_set = posFeatures[:size_pos] + negFeatures[:size_neg] 60 | test_set = posFeatures[size_pos:] + negFeatures[size_neg:] 61 | 62 | test, tag_test = zip(*test_set) 63 | 64 | classifier = [] 65 | classifier = ssc.cal_classifier_accuracy(train_set,test,tag_test) 66 | #选择准确度最高的分类器,正常情况下是选择准确性最高的 67 | # object = ssc.find_score_max(classifier) 68 | #分别测试多项分布贝叶斯，逻辑回归，svm比较性能 69 | # object = classifier[1][0] #多项分布 70 | object = classifier[2][0] #逻辑回归 71 | # object = classifier[4][0] #svm 72 | print '选择的分类器是：' 73 | print object 74 | # 存储分类器 75 | # ssc.store_classifier(object, train_set, path) 76 | print '存储分类器' 77 | object_classifier = SklearnClassifier(object) 78 | object_classifier.train(train_set) 79 | pickle.dump(object_classifier, open(path+'/great_LRclassifier.pkl','w')) 80 | print '结束训练分类器' 81 | print '开始预测' 82 | # test_word_scores = pnf.create_words_bigrams_scores() 83 | # test_best_words = pnf.find_best_words(test_word_scores, 1500) 84 | clf = pickle.load(open(path+'/great_LRclassifier.pkl')) 85 | pred = clf.prob_classify_many(pnf.extract_features(test_review,best_words)) 86 | print '存储预测结果' 87 | pnf.store_predict_result(path,pred) 88 | print '结束预测' 89 | end_time = time.time() 90 | print end_time 91 | print end_time-start_time 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | -------------------------------------------------------------------------------- /posnegmlfeature.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | import textprocessing as tp 3 | import pickle 4 | import itertools 5 | from random import shuffle 6 | import nltk 7 | from nltk.collocations import BigramCollocationFinder 8 | from nltk.metrics import BigramAssocMeasures 9 | from nltk.probability import FreqDist, ConditionalFreqDist 10 | import sklearn 11 | import os 12 | 13 | # 1. 加载测试集 14 | # path = os.getcwd() 15 | # test_review = tp.seg_fil_senti_excel(path+"/seniment review set/GREATEWALLTEST.xls", 1, 6) 16 | 17 | # 2. 提取特征 18 | # Used for transform review to features, so it can calculate sentiment probability by classifier 19 | # def create_words_bigrams_scores(): 20 | # posdata = tp.seg_fil_senti_excel(path+"/seniment review set/GREATEWALLPOS.xls", 1, 6) 21 | # negdata = tp.seg_fil_senti_excel(path+"/seniment review set/GREATEWALLNEG.xls", 1, 6) 22 | # 23 | # posWords = list(itertools.chain(*posdata)) 24 | # negWords = list(itertools.chain(*negdata)) 25 | # 26 | # bigram_finder_pos = BigramCollocationFinder.from_words(posWords) 27 | # bigram_finder_neg = BigramCollocationFinder.from_words(negWords) 28 | # posBigrams = bigram_finder_pos.nbest(BigramAssocMeasures.chi_sq, 5000) 29 | # negBigrams = bigram_finder_neg.nbest(BigramAssocMeasures.chi_sq, 5000) 30 | # 31 | # pos = posWords + posBigrams 32 | # neg = negWords + negBigrams 33 | # 34 | # word_fd = FreqDist() 35 | # cond_word_fd = ConditionalFreqDist() 36 | # for word in pos: 37 | # word_fd[word] += 1 38 | # cond_word_fd['pos'][word] += 1 39 | # for word in neg: 40 | # word_fd[word] += 1 41 | # cond_word_fd['neg'][word] += 1 42 | # 43 | # pos_word_count = cond_word_fd['pos'].N() 44 | # neg_word_count = cond_word_fd['neg'].N() 45 | # total_word_count = pos_word_count + neg_word_count 46 | # 47 | # word_scores = {} 48 | # for word, freq in word_fd.iteritems(): 49 | # pos_score = BigramAssocMeasures.chi_sq(cond_word_fd['pos'][word], (freq, pos_word_count), total_word_count) 50 | # neg_score = BigramAssocMeasures.chi_sq(cond_word_fd['neg'][word], (freq, neg_word_count), total_word_count) 51 | # word_scores[word] = pos_score + neg_score 52 | # 53 | # return word_scores 54 | # 55 | # def find_best_words(word_scores, number): 56 | # best_vals = sorted(word_scores.iteritems(), key=lambda (w, s): s, reverse=True)[:number] 57 | # best_words = set([w for w, s in best_vals]) 58 | # return best_words 59 | 60 | # Initiallize word's information score and extracting most informative words 61 | # 计算每个词的信息熵，提取信息量最大的词作为特征词 62 | # word_scores = create_words_bigrams_scores() 63 | # best_words = find_best_words(word_scores, 1500) # Be aware of the dimentions 64 | 65 | def best_word_features(words,best_words): 66 | return dict([(word, True) for word in words if word in best_words]) 67 | 68 | def best_word_features_com(words,best_words): 69 | 70 | d1 = dict([(word, True) for word in words if word in best_words]) 71 | d2 = dict([(word, True) for word in nltk.bigrams(words) if word in best_words]) 72 | d3 = dict(d1, **d2) 73 | return d3 74 | 75 | # 3. 测试集提取特征 76 | def extract_features(dataset,best_words): 77 | feat = [] 78 | for i in dataset: 79 | # feat.append(best_word_features(i,best_words)) 80 | feat.append(best_word_features_com(i,best_words)) 81 | return feat 82 | 83 | # 4. 加载分类器 84 | # clf = pickle.load(open(path+'/classifier.pkl')) 85 | 86 | # 计算测试集的概率 87 | # pred = clf.prob_classify_many(extract_features(test_review)) 88 | 89 | #保存 90 | def store_predict_result(path,pred): 91 | p_file = open(path+'/result/greate_LRfinal.txt', 'w') 92 | for i in pred: 93 | p_file.write(str(i.prob('pos')) + ' ' + str(i.prob('neg')) + '\n') 94 | p_file.close() 95 | 96 | pred2 = [] 97 | pos_count=0 98 | neg_count=0 99 | for i in pred: 100 | pred2.append([i.prob('pos'), i.prob('neg')]) 101 | if i.prob('pos')>i.prob('neg'): 102 | pos_count += 1 103 | else: 104 | neg_count += 1 105 | 106 | print '好评占：','%f'%((pos_count*1.0)/((pos_count+neg_count)*1.0)) 107 | print '差评占：','%f'%((neg_count*1.0)/((pos_count+neg_count)*1.0)) -------------------------------------------------------------------------------- /posnegmlfeature.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/life-is-good/MoiveDataAnalysisByML/e80ea309a85086aae40aa2ac8c1f109193000db3/posnegmlfeature.pyc -------------------------------------------------------------------------------- /result/chengfengpolang_LRfinal.txt: -------------------------------------------------------------------------------- 1 | 0.201373127273 0.798626872727 2 | 0.551875141469 0.448124858531/ 3 | 0.772938517018 0.227061482982/ 4 | 0.332432435046 0.667567564954 5 | 0.0258683676811 0.974131632319 6 | 0.348730822647 0.651269177353 7 | 0.00145013555549 0.998549864445 8 | 0.000123112173514 0.999876887826 9 | 0.192815900155 0.807184099845 10 | 0.667459297446 0.332540702554/ 11 | 0.125235156414 0.874764843586 12 | 0.192020743102 0.807979256898 13 | 0.247133117623 0.752866882377 14 | 0.0356462063851 0.964353793615 15 | 0.245009936825 0.754990063175 16 | 0.00497522869782 0.995024771302 17 | 0.646636047293 0.353363952707/ 18 | 0.240592127596 0.759407872404 19 | 0.388851193319 0.611148806681 20 | 0.0205984812005 0.9794015188 21 | 0.119253078042 0.880746921958 22 | 0.0511607682354 0.948839231765 23 | 0.350027667661 0.649972332339 24 | 0.0948503599393 0.905149640061 25 | 0.0235750283263 0.976424971674 26 | 0.333034754356 0.666965245644 27 | 0.000214316582662 0.999785683417 28 | 0.206664368793 0.793335631207 29 | 0.00132116502155 0.998678834978 30 | 0.00874020780999 0.99125979219 31 | 0.388851193319 0.611148806681 32 | 0.260239479241 0.739760520759 33 | 0.0245783619897 0.97542163801 34 | 0.0619772960975 0.938022703903 35 | 0.20557172071 0.79442827929 36 | 0.6588771719 0.3411228281/ 37 | 0.625710111989 0.374289888011/ 38 | 0.0378227925178 0.962177207482 39 | 0.188802902589 0.811197097411 40 | 0.146612024081 0.853387975919 41 | 0.594060375428 0.405939624572/ 42 | 0.442567706041 0.557432293959 43 | 0.00439212260818 0.995607877392 44 | 0.169811325076 0.830188674924 45 | 0.0287687577563 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0.174788549624 0.825211450376 74 | 0.13177317547 0.86822682453 75 | 0.0109000458352 0.989099954165 76 | 0.212606835392 0.787393164608 77 | 0.542065031142 0.457934968858/ 78 | 0.541618419947 0.458381580053/ 79 | 0.0286387479223 0.971361252078 80 | 0.247266341832 0.752733658168 81 | 0.356172039257 0.643827960743 82 | 0.364613228604 0.635386771396 83 | 0.00467217954952 0.99532782045 84 | 0.461361472735 0.538638527265 85 | 0.00440416386302 0.995595836137 86 | 0.0471636731547 0.952836326845 87 | 0.467500675594 0.532499324406 88 | 0.630822860327 0.369177139673/ 89 | 0.223975779952 0.776024220048 90 | 0.0990910169714 0.900908983029 91 | 0.227883207458 0.772116792542 92 | 0.103811694292 0.896188305708 93 | 0.185177361915 0.814822638085 94 | 0.118595842884 0.881404157116 95 | 0.372121988545 0.627878011455 96 | 0.45599680183 0.54400319817 97 | 0.284797980454 0.715202019546 98 | 0.208603329651 0.791396670349 99 | 0.226003372159 0.773996627841 100 | 0.219746736804 0.780253263196 101 | 0.0118186366975 0.988181363302 102 | 0.352911552781 0.647088447219 103 | 0.144714463241 0.855285536759 104 | 0.533197566334 0.466802433666/ 105 | 0.0434960145129 0.956503985487 106 | 0.145794856819 0.854205143181 107 | 0.0548030735714 0.945196926429 108 | 0.525255501087 0.474744498913/ 109 | 0.388851193319 0.611148806681 110 | 0.000348460207914 0.999651539792 111 | 0.169955189775 0.830044810225 112 | 0.0239451082778 0.976054891722 113 | 0.000221365931462 0.999778634069 114 | 0.301499602423 0.698500397577 115 | 0.023669126375 0.976330873625 116 | 0.0357233859786 0.964276614021 117 | 0.0356462063851 0.964353793615 118 | 0.533681597789 0.466318402211/ 119 | 0.0768189107249 0.923181089275 120 | 0.2095466302 0.7904533698 121 | 0.62523932146 0.37476067854/ 122 | 0.326816964104 0.673183035896 123 | 0.024369105743 0.975630894257 124 | 0.388851193319 0.611148806681 125 | 0.388851193319 0.611148806681 126 | 0.00224361283521 0.997756387165 127 | 0.319450560587 0.680549439413 128 | 0.0542080372372 0.945791962763 129 | 0.0356462063851 0.964353793615 130 | 0.165076768339 0.834923231661 131 | 0.42854182109 0.57145817891 132 | 0.314687537857 0.685312462143 133 | 0.111371191647 0.888628808353 134 | 0.677787740188 0.322212259812/ 135 | 0.323217470179 0.676782529821 136 | 0.932706879528 0.0672931204717/ 137 | 0.0625722359246 0.937427764075 138 | 0.0155203077087 0.984479692291 139 | 0.0181959075453 0.981804092455 140 | 0.388851193319 0.611148806681 141 | 0.0787672876313 0.921232712369 142 | 0.48304408325 0.51695591675 143 | 0.291450937767 0.708549062233 144 | 0.0391877633552 0.960812236645 145 | 0.309080973905 0.690919026095 146 | 0.440082842111 0.559917157889 147 | 0.0186381512745 0.981361848725 148 | 0.00210962332934 0.997890376671 149 | 0.174689975901 0.825310024099 150 | 0.0242325931897 0.97576740681 151 | 0.259645241018 0.740354758982 152 | 0.0504029424757 0.949597057524 153 | 0.00592705173926 0.994072948261 154 | 0.903290792015 0.0967092079846/ 155 | 0.234006253994 0.765993746006 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0.359725415552 0.640274584448 184 | 0.0305352028581 0.969464797142 185 | 0.654652539642 0.345347460358/ 186 | 0.037538179114 0.962461820886 187 | 0.388851193319 0.611148806681 188 | 0.160347722877 0.839652277123 189 | 0.20557172071 0.79442827929 190 | 0.388851193319 0.611148806681 191 | 0.617717810593 0.382282189407/ 192 | 0.0786120017089 0.921387998291 193 | 0.171995098032 0.828004901968 194 | 0.172729936708 0.827270063292 195 | 0.156594950498 0.843405049502 196 | 0.670985720066 0.329014279934/ 197 | 0.0543721479296 0.94562785207 198 | 0.0703061701395 0.929693829861 199 | 0.447592465574 0.552407534426 200 | 0.146372094791 0.853627905209 201 | 0.482608080768 0.517391919232 202 | 0.673137785203 0.326862214797/ 203 | 0.0340516626668 0.965948337333 204 | 0.0758294023511 0.924170597649 205 | 0.0141036191844 0.985896380816 206 | 0.243504051502 0.756495948498 207 | 0.204609045373 0.795390954627 208 | 0.000832233403867 0.999167766596 209 | 0.0590461146772 0.940953885323 210 | 0.247133117623 0.752866882377 211 | 0.363782415367 0.636217584633 212 | 0.725168080165 0.274831919835/ 213 | 0.0867564837983 0.913243516202 214 | 0.35753525556 0.64246474444 215 | 0.15413301062 0.84586698938 216 | 0.00755445479827 0.992445545202 217 | 0.111371191647 0.888628808353 218 | 0.190045612648 0.809954387352 219 | 0.0868234256104 0.91317657439 220 | 0.0635679226678 0.936432077332 221 | 0.634891049355 0.365108950645/ 222 | 0.847674783065 0.152325216935/ 223 | 0.359725415552 0.640274584448 224 | 0.0480366476254 0.951963352375 225 | 0.19725858185 0.80274141815 226 | 0.0970734759639 0.902926524036 227 | 0.354754556587 0.645245443413 228 | 0.0504410067728 0.949558993227 229 | 0.12897786004 0.87102213996 230 | 0.12509715818 0.87490284182 231 | 0.356091163167 0.643908836833 232 | 0.0035746079832 0.996425392017 233 | 0.0239835334881 0.976016466512 234 | 0.167530321757 0.832469678243 235 | 0.000741187935221 0.999258812065 236 | 0.407746468245 0.592253531755 237 | 0.0713016532412 0.928698346759 238 | 0.0693635838699 0.93063641613 239 | 0.0288675609912 0.971132439009 240 | 0.930173563061 0.0698264369394 241 | 0.280918552749 0.719081447251 242 | 0.0124333008793 0.987566699121 243 | 0.161846733995 0.838153266005 244 | 0.0825556364422 0.917444363558 245 | 0.560560068828 0.439439931172/ 246 | 0.781232981446 0.218767018554/ 247 | 0.125731766359 0.874268233641 248 | 0.226660122049 0.773339877951 249 | 0.290789532503 0.709210467497 250 | 0.238767894661 0.761232105339 251 | 0.21459999717 0.78540000283 252 | 0.1244023068 0.8755976932 253 | 0.444170679843 0.555829320157 254 | 0.000337664585664 0.999662335414 255 | 0.0609519344973 0.939048065503 256 | 0.327555108882 0.672444891118 257 | 0.00346854450756 0.996531455492 258 | 0.334529915645 0.665470084355 259 | 0.604256417276 0.395743582724/ 260 | 0.649248557107 0.350751442893/ 261 | 0.146825083489 0.853174916511 262 | 0.0235433842652 0.976456615735 263 | 0.834090958003 0.165909041997/ 264 | 0.241745131641 0.758254868359 265 | 0.000416198322029 0.999583801678 266 | 0.580203932836 0.419796067164/ 267 | 0.111371191647 0.888628808353 268 | 0.0806879373962 0.919312062604 269 | 0.380946823974 0.619053176026 270 | 0.388851193319 0.611148806681 271 | 0.0648210224868 0.935178977513 272 | 0.0356462063851 0.964353793615 273 | 0.413407930992 0.586592069008 274 | 0.0663904545925 0.933609545408 275 | 0.219593897848 0.780406102152 276 | 0.124781553534 0.875218446466 277 | 0.694180963463 0.305819036537/ 278 | 0.525255501087 0.474744498913/ 279 | 0.0430331598757 0.956966840124 280 | 0.124252557634 0.875747442366 281 | 0.140049738929 0.859950261071 282 | 0.525255501087 0.474744498913/ 283 | 0.0125712164472 0.987428783553 284 | 0.206583915613 0.793416084387 285 | 0.0480366476254 0.951963352375 286 | 0.0214299018059 0.978570098194 287 | 0.388851193319 0.611148806681 288 | 0.148599879763 0.851400120237 289 | 0.115504734962 0.884495265038 290 | 0.00669795395577 0.993302046044 291 | 0.263527237704 0.736472762296 292 | 0.701438007298 0.298561992702/ 293 | 0.348584789928 0.651415210072 294 | 0.415053868855 0.584946131145 295 | 0.141780179424 0.858219820576 296 | 0.016119319256 0.983880680744 297 | 0.00581493035585 0.994185069644 298 | 0.532752019571 0.467247980429/ 299 | 0.00234022218095 0.997659777819 300 | 0.145670395149 0.854329604851 301 | 0.195132838019 0.804867161981 302 | 0.00655785200486 0.993442147995 303 | 0.00598649264282 0.994013507357 304 | 0.738400453782 0.261599546218/ 305 | 0.0509408381292 0.949059161871 306 | 0.388851193319 0.611148806681 307 | 0.498065991288 0.501934008712 308 | 0.0359227172224 0.964077282778 309 | 0.772137190252 0.227862809748/ 310 | 0.404369855953 0.595630144047 311 | 0.145670395149 0.854329604851 312 | 0.363537769182 0.636462230818 313 | 0.413407930992 0.586592069008 314 | 0.633231735479 0.366768264521/ 315 | 0.0718083233317 0.928191676668 316 | 0.00310651733533 0.996893482665 317 | 0.00371274575697 0.996287254243 318 | 0.0156331156711 0.984366884329 319 | 0.240100628794 0.759899371206 320 | 0.0140480407441 0.985951959256 321 | 0.0527368100225 0.947263189977 322 | 0.0515388445806 0.948461155419 323 | 0.0610942087287 0.938905791271 324 | 0.780478097419 0.219521902581/ 325 | 0.00324587882379 0.996754121176 326 | 0.00496655579648 0.995033444204 327 | 0.046029272824 0.953970727176 328 | 0.244564175276 0.755435824724 329 | 0.813173886231 0.186826113769/ 330 | 0.250359170965 0.749640829035 331 | 0.0480366476254 0.951963352375 332 | 0.00380096893541 0.996199031065 333 | 0.182515802566 0.817484197434 334 | 0.0697248937076 0.930275106292 335 | 0.366273652391 0.633726347609 336 | 0.12694774543 0.87305225457 337 | 0.0945062418317 0.905493758168 338 | 0.359725415552 0.640274584448 339 | 0.0239856751559 0.976014324844 340 | 0.0039180803793 0.996081919621 341 | 0.13955850701 0.86044149299 342 | 0.185177361915 0.814822638085 343 | 0.029968831396 0.970031168604 344 | 0.0495718310775 0.950428168922 345 | 0.522131071201 0.477868928799/ 346 | 0.0308793124738 0.969120687526 347 | 0.0999931464329 0.900006853567 348 | 0.388851193319 0.611148806681 349 | 0.108488262996 0.891511737004 350 | 0.0959981263096 0.90400187369 351 | 0.225723983229 0.774276016771 352 | 0.540905944166 0.459094055834/ 353 | 0.197230741767 0.802769258233 354 | 0.0682560432842 0.931743956716 355 | 0.468012833232 0.531987166768 356 | 0.0193100133513 0.980689986649 357 | 0.0025358446437 0.997464155356 358 | 0.368833362651 0.631166637349 359 | 0.903367647324 0.0966323526759 360 | 0.0142165464517 0.985783453548 361 | 0.237854616938 0.762145383062 362 | 0.0232179290083 0.976782070992 363 | 0.447844694887 0.552155305113 364 | 0.487685207474 0.512314792526 365 | 0.459364281381 0.540635718619 366 | 0.256096434992 0.743903565008 367 | 0.260985080673 0.739014919327 368 | 0.00713650389065 0.992863496109 369 | 0.0291853922532 0.970814607747 370 | 0.240100628794 0.759899371206 371 | 0.283564395078 0.716435604922 372 | 0.0321499299616 0.967850070038 373 | 0.00142751950717 0.998572480493 374 | 0.334472321141 0.665527678859 375 | 0.0581098834983 0.941890116502 376 | 0.545230034279 0.454769965721/ 377 | 0.012852735805 0.987147264195 378 | 0.0124800556142 0.987519944386 379 | 0.252552399209 0.747447600791 380 | 0.186170017678 0.813829982322 381 | 0.0819787574691 0.918021242531 382 | 0.005354315865 0.994645684135 383 | 0.00235030815 0.99764969185 384 | 0.310765649634 0.689234350366 385 | 0.130659167569 0.869340832431 386 | 0.0392411063778 0.960758893622 387 | 0.286451046513 0.713548953487 388 | 0.373077800688 0.626922199312 389 | 0.0700255176203 0.92997448238 390 | 0.266339177654 0.733660822346 391 | 0.053080381689 0.946919618311 392 | 0.305886005568 0.694113994432 393 | 0.240802810497 0.759197189503 394 | 0.0231745238088 0.976825476191 395 | 0.231124874277 0.768875125723 396 | 0.388851193319 0.611148806681 397 | 0.246527322209 0.753472677791 398 | 0.0153644811649 0.984635518835 399 | 0.0563537168164 0.943646283184 400 | 0.503897212371 0.496102787629/ 401 | 0.388851193319 0.611148806681 402 | 0.124236836432 0.875763163568 403 | 0.0683922778592 0.931607722141 404 | 0.882321042474 0.117678957526/ 405 | 0.105187295877 0.894812704123 406 | 0.163087053208 0.836912946792 407 | 0.333731869855 0.666268130145 408 | 0.0609519344973 0.939048065503 409 | 0.000838075498173 0.999161924502 410 | 0.261769103586 0.738230896414 411 | 0.281792291884 0.718207708116 412 | 0.213960730652 0.786039269348 413 | 0.0369914700139 0.963008529986 414 | 0.581576984843 0.418423015157/ 415 | 0.00377128257202 0.996228717428 416 | 0.59246944714 0.40753055286/ 417 | 0.0120141892108 0.987985810789 418 | 0.0867105207413 0.913289479259 419 | 0.00210411573108 0.997895884269 420 | 0.243504051502 0.756495948498 421 | 0.0771194725238 0.922880527476 422 | 0.000671412001283 0.999328587999 423 | 0.431157961618 0.568842038382 424 | 0.00210411573108 0.997895884269 425 | 0.522016158475 0.477983841525/ 426 | 0.566230350967 0.433769649033 427 | 0.268216945455 0.731783054545/ 428 | 0.241736692298 0.758263307702/ 429 | 0.895368400842 0.104631599158 430 | 0.0461284647947 0.953871535205/ 431 | 0.997169767046 0.00283023295357 432 | 0.231980692415 0.768019307585/ 433 | 0.999925632869 7.43671307862e-05 434 | 0.986045872736 0.0139541272642 435 | 0.999492803736 0.000507196264425 436 | 0.974217474083 0.0257825259174 437 | 0.550906246946 0.449093753054 438 | 0.9937850908 0.00621490920034 439 | 0.999694462931 0.000305537068728 440 | 0.66334985162 0.33665014838 441 | 0.678407018491 0.321592981509 442 | 0.999885140642 0.000114859357541 443 | 0.701526688874 0.298473311126 444 | 0.993550455851 0.00644954414942 445 | 0.988710779281 0.0112892207192 446 | 0.994433792995 0.00556620700495 447 | 0.959053048246 0.0409469517541 448 | 0.999916731693 8.32683067393e-05 449 | 0.143347133736 0.856652866264/ 450 | 0.989796894944 0.0102031050558 451 | 0.232184676371 0.767815323629/ 452 | 0.998168062281 0.00183193771942 453 | 0.777830990615 0.222169009385 454 | 0.866342474335 0.133657525665 455 | 0.960544491099 0.0394555089011 456 | 0.970529266847 0.0294707331527 457 | 0.441631024772 0.558368975228/ 458 | 0.206416562189 0.793583437811/ 459 | 0.999952526161 4.74738393457e-05 460 | 0.315774723136 0.684225276864/ 461 | 0.91857092564 0.0814290743603 462 | 0.706215795334 0.293784204666 463 | 0.915910108462 0.0840898915379 464 | 0.831902006657 0.168097993343 465 | 0.951989190486 0.0480108095143 466 | 0.639435148471 0.360564851529 467 | 0.879752834612 0.120247165388 468 | 0.782665169775 0.217334830225 469 | 0.904813185397 0.0951868146026 470 | 0.997025195814 0.00297480418573 471 | 0.972667876304 0.027332123696 472 | 0.861221164737 0.138778835263 473 | 0.886675213708 0.113324786292 474 | 0.963678884228 0.0363211157724 475 | 0.733454295985 0.266545704015 476 | 0.999119929955 0.000880070045117 477 | 0.66787312986 0.33212687014 478 | 0.646636047293 0.353363952707 479 | 0.999815668301 0.000184331699179 480 | 0.372596830569 0.627403169431/ 481 | 0.97115553216 0.0288444678405 482 | 0.968942147447 0.0310578525528 483 | 0.99990014161 9.98583899151e-05 484 | 0.743993313368 0.256006686632 485 | 0.928489592522 0.071510407478 486 | 0.607572600566 0.392427399434 487 | 0.837045307592 0.162954692408 488 | 0.875878994817 0.124121005183 489 | 0.983822401381 0.0161775986192 490 | 0.388851193319 0.611148806681/ 491 | 0.388977003597 0.611022996403/ 492 | 0.364012241666 0.635987758334/ 493 | 0.990668085661 0.00933191433912 494 | 0.916650148759 0.0833498512415 495 | 0.896959485096 0.103040514904 496 | 0.731214274733 0.268785725267 497 | 0.793713032779 0.206286967221 498 | 0.435217781344 0.564782218656/ 499 | 0.777364956062 0.222635043938 500 | 0.451510665257 0.548489334743/ 501 | 0.909299340996 0.0907006590044 502 | 0.966985523538 0.0330144764619 503 | 0.995389080587 0.00461091941282 504 | 0.731166760156 0.268833239844 505 | 0.116829951259 0.883170048741/ 506 | 0.8659134888 0.1340865112 507 | 0.24546743255 0.75453256745/ 508 | 0.856090150042 0.143909849958 509 | 0.894589377357 0.105410622643 510 | 0.998788836172 0.00121116382786 511 | 0.667850448063 0.332149551937 512 | 0.957274394175 0.0427256058251 513 | 0.966303341929 0.0336966580713 514 | 0.96650153806 0.0334984619399 515 | 0.998896829496 0.00110317050428 516 | 0.718486211679 0.281513788321 517 | 0.192055447542 0.807944552458/ 518 | 0.998765343949 0.00123465605105 519 | 0.89830073672 0.10169926328 520 | 0.731719228972 0.268280771028 521 | 0.538403103103 0.461596896897 522 | 0.938070012602 0.0619299873977 523 | 0.909635160604 0.0903648393959 524 | 0.731214274733 0.268785725267 525 | 0.848947594562 0.151052405438 526 | 0.895046317319 0.104953682681 527 | 0.794381126106 0.205618873894 528 | 0.937186367702 0.0628136322982 529 | 0.967470861459 0.0325291385414 530 | 0.859110435846 0.140889564154 531 | 0.994833484555 0.00516651544537 532 | 0.919001878512 0.0809981214879 533 | 0.983736685961 0.0162633140387 534 | 0.988737795871 0.0112622041287 535 | 0.976863066326 0.0231369336741 536 | 0.87230187857 0.12769812143 537 | 0.638810592414 0.361189407586 538 | 0.821955365472 0.178044634528 539 | 0.368664116178 0.631335883822/ 540 | 0.999826667893 0.000173332106957 541 | 0.948593751864 0.0514062481356 542 | 0.941356872652 0.0586431273476 543 | 0.228539133235 0.771460866765/ 544 | 0.41362000783 0.58637999217/ 545 | 0.803876244466 0.196123755534 546 | 0.994057186302 0.00594281369807 547 | 0.950054022819 0.0499459771811 548 | 0.975948685256 0.0240513147445 549 | 0.958026528698 0.0419734713025 550 | 0.979065537112 0.0209344628884 551 | 0.593801090643 0.406198909357 552 | 0.80823623199 0.19176376801 553 | 0.549369305963 0.450630694037 554 | 0.510999268659 0.489000731341 555 | 0.988139683949 0.0118603160513 556 | 0.994952503084 0.00504749691645 557 | 0.999697724074 0.000302275925801 558 | 0.999998668892 1.3311083028e-06 559 | 0.618475895635 0.381524104365 560 | 0.995804580067 0.00419541993291 561 | 0.691959970103 0.308040029897 562 | 0.388851193319 0.611148806681/ 563 | 0.388851193319 0.611148806681/ 564 | 0.958573517722 0.0414264822782 565 | 0.900536702019 0.0994632979815 566 | 0.904819063842 0.0951809361583 567 | 0.982045407594 0.0179545924058 568 | 0.729325900989 0.270674099011 569 | 0.929511235889 0.0704887641105 570 | 0.620422871232 0.379577128768 571 | 0.388851193319 0.611148806681/ 572 | 0.998641624534 0.00135837546646 573 | 0.984771655861 0.0152283441394 574 | 0.990682538331 0.00931746166877 575 | 0.954876394198 0.0451236058022 576 | 0.435345341093 0.564654658907/ 577 | 0.988015389998 0.0119846100022 578 | 0.53364405579 0.46635594421 579 | 0.997336555064 0.00266344493586 580 | 0.304798835489 0.695201164511/ 581 | 0.911832212326 0.0881677876737 582 | 0.731214274733 0.268785725267 583 | 0.86436090827 0.13563909173 584 | 0.985361681065 0.0146383189348 585 | 0.999308136796 0.000691863203693 586 | 0.398139510126 0.601860489874/ 587 | 0.986038983045 0.0139610169547 588 | 0.956200729387 0.0437992706134 589 | 0.278609411557 0.721390588443/ 590 | 0.999957327937 4.26720633704e-05 591 | 0.996825636843 0.00317436315745 592 | 0.999835328953 0.000164671047268 593 | 0.250804000937 0.749195999063/ 594 | 0.995013121362 0.00498687863816 595 | 0.997054995549 0.0029450044511 596 | 0.767781952634 0.232218047366 597 | 0.965574372964 0.0344256270363 598 | 0.235816325038 0.764183674962/ 599 | 0.999996487538 3.51246221375e-06 600 | 0.959483464343 0.0405165356573 601 | 0.485269339459 0.514730660541/ 602 | 0.907550792625 0.092449207375 603 | 0.987012704453 0.0129872955473 604 | 0.17986865788 0.82013134212/ 605 | 0.578406503318 0.421593496682 606 | 0.879752834612 0.120247165388 607 | 0.973054883305 0.0269451166947 608 | 0.970356405454 0.0296435945459 609 | 0.904318048259 0.0956819517407 610 | 0.969700337018 0.0302996629821 611 | 0.544918455937 0.455081544063 612 | 0.993903417789 0.00609658221126 613 | 0.987631425839 0.0123685741606 614 | 0.992991614031 0.00700838596921 615 | 0.979138219969 0.0208617800312 616 | 0.940159403623 0.0598405963773 617 | 0.924021581301 0.0759784186991 618 | 0.983123950969 0.0168760490305 619 | 0.955570511613 0.0444294883871 620 | 0.0831861541885 0.916813845811/ 621 | 0.886675213708 0.113324786292 622 | 0.997144655574 0.002855344426 623 | 0.788750450707 0.211249549293 624 | 0.979193625018 0.0208063749817 625 | 0.574877977897 0.425122022103 626 | 0.994963132714 0.0050368672857 627 | 0.948056351943 0.051943648057 628 | 0.333075382318 0.666924617682/ 629 | 0.999562013783 0.000437986216872 630 | 0.707982981704 0.292017018296 631 | 0.993540434954 0.00645956504595 632 | 0.613689442508 0.386310557492 633 | 0.985854654267 0.0141453457329 634 | 0.892224728101 0.107775271899 635 | 0.686216489399 0.313783510601 636 | 0.388851193319 0.611148806681/ 637 | 0.53396530713 0.46603469287 638 | 0.879752834612 0.120247165388 639 | 0.999641318959 0.000358681041304 640 | 0.0194172903661 0.980582709634/ 641 | 0.996136331672 0.00386366832785 642 | 0.768258505811 0.231741494189 643 | 0.959791315476 0.0402086845241 644 | 0.682777100665 0.317222899335 645 | 0.938977870137 0.0610221298632 646 | 0.957103313214 0.0428966867858 647 | 0.377854579579 0.622145420421/ 648 | 0.961013627308 0.0389863726921 649 | 0.364012241666 0.635987758334/ 650 | 0.998882520745 0.00111747925461 651 | 0.81826754444 0.18173245556 652 | 0.544289576082 0.455710423918 653 | 0.499169922616 0.500830077384/ 654 | 0.878354584269 0.121645415731 655 | 0.806402017489 0.193597982511 656 | 0.958365773321 0.0416342266785 657 | 0.998147156425 0.0018528435747 658 | 0.702706251481 0.297293748519 659 | 0.495165708448 0.504834291552/ 660 | 0.388851193319 0.611148806681/ 661 | 0.940986845646 0.0590131543541 662 | 0.999910903869 8.90961309368e-05 663 | 0.897525390262 0.102474609738 664 | 0.956147734895 0.0438522651055 665 | 0.920936641404 0.0790633585961 666 | 0.979809429979 0.0201905700208 667 | 0.95003420245 0.0499657975497 668 | 0.819137629426 0.180862370574 669 | 0.855378270185 0.144621729815 670 | 0.930898816901 0.0691011830989 671 | 0.859137214295 0.140862785705 672 | 0.929572729721 0.0704272702793 673 | 0.859434787524 0.140565212476 674 | 0.988331774452 0.0116682255477 675 | 0.746501355579 0.253498644421 676 | 0.374084258575 0.625915741425/ 677 | 0.42405584402 0.57594415598/ 678 | 0.276086693366 0.723913306634/ 679 | 0.370204699685 0.629795300315/ 680 | 0.990230599476 0.00976940052368 681 | 0.86436090827 0.13563909173 682 | 0.886675213708 0.113324786292 683 | 0.969945987161 0.0300540128391 684 | 0.92939422834 0.0706057716604 685 | 0.644264505995 0.355735494005 686 | 0.674636710683 0.325363289317 687 | 0.974535041065 0.0254649589348 688 | 0.773110119483 0.226889880517 689 | 0.9983250596 0.00167494040043 690 | 0.954632067048 0.0453679329517 691 | 0.997789718552 0.0022102814479 692 | 0.799579008379 0.200420991621 693 | 0.987154842206 0.012845157794 694 | 0.943558397362 0.056441602638 695 | 0.388851193319 0.611148806681/ 696 | 0.999080446242 0.000919553757787 697 | 0.798105151312 0.201894848688 698 | 0.610717765828 0.389282234172 699 | 0.878238305799 0.121761694201 700 | 0.883342979148 0.116657020852 701 | 0.963115658207 0.0368843417925 702 | 0.383912672211 0.616087327789/ 703 | 0.937918120471 0.0620818795291 704 | 0.64897742352 0.35102257648 705 | 0.999672351682 0.000327648317744 706 | 0.704158129149 0.295841870851 707 | 0.999040810813 0.000959189186916 708 | 0.997831136666 0.00216886333359 709 | 0.984295855352 0.015704144648 710 | 0.572028737425 0.427971262575 711 | 0.999305723101 0.000694276899275 712 | 0.999082387629 0.000917612371201 713 | 0.668367362066 0.331632637934 714 | 0.985870897846 0.0141291021541 715 | 0.966821893125 0.0331781068747 716 | 0.998309467463 0.00169053253734 717 | 0.966978525955 0.0330214740453 718 | 0.847118858913 0.152881141087 719 | 0.834193571584 0.165806428416 720 | 0.999977010014 2.29899862993e-05 721 | 0.194201468556 0.805798531444/ 722 | 0.999106029297 0.000893970702534 723 | 0.588454034683 0.411545965317 724 | 0.985065682785 0.0149343172147 725 | 0.98763162972 0.0123683702799 726 | 0.679096998455 0.320903001545 727 | 0.993909009605 0.00609099039526 728 | 0.660029674327 0.339970325673 729 | 0.586682184837 0.413317815163 730 | 0.605464059392 0.394535940608 731 | 0.999053029345 0.000946970655289 732 | 0.776616720864 0.223383279136 733 | 0.315944287582 0.684055712418/ 734 | 0.562983286639 0.437016713361 735 | 0.988175588283 0.011824411717 736 | 0.201084387282 0.798915612718/ 737 | 0.353142026998 0.646857973002/ 738 | 0.566230350967 0.433769649033 739 | 0.514682498856 0.485317501144 740 | 0.574689525827 0.425310474173 741 | 0.874966971496 0.125033028504 742 | 0.412131704923 0.587868295077/ 743 | 0.999665966347 0.000334033652723 744 | 0.493985410918 0.506014589082/ 745 | 0.388851193319 0.611148806681/ 746 | 0.92780496173 0.0721950382702 747 | 0.388851193319 0.611148806681/ 748 | 0.362967435271 0.637032564729/ 749 | 0.909601092099 0.0903989079013 750 | 0.999732220783 0.000267779217331 751 | 0.937967346388 0.0620326536124 752 | 0.42912906657 0.57087093343/ 753 | 0.614025683458 0.385974316542 754 | 0.786611372896 0.213388627104 755 | 0.950542694137 0.049457305863 756 | 0.882755221489 0.117244778511 757 | 0.455946127455 0.54405387254/5 758 | 0.969412625093 0.0305873749065 759 | 0.940509856869 0.0594901431309 760 | 0.602340781533 0.397659218467 761 | 0.881125325644 0.118874674356 762 | 0.907716776779 0.0922832232207 763 | 0.995645756353 0.00435424364668 764 | 0.359725415552 0.640274584448/ 765 | 0.898449325317 0.101550674683 766 | 0.0111115666738 0.988888433326/ 767 | 0.549002715169 0.450997284831 768 | 0.731214274733 0.268785725267 769 | 0.999551165499 0.000448834500699 770 | 0.0933498531061 0.906650146894/ 771 | 0.708527992631 0.291472007369 772 | 0.997542942464 0.0024570575361 773 | 0.995974064061 0.00402593593895 774 | 0.981841354439 0.0181586455612 775 | 0.927807332975 0.0721926670255 776 | 0.71986958846 0.28013041154 777 | 0.886675213708 0.113324786292 778 | 0.586151253263 0.413848746737 779 | 0.54327112501 0.45672887499 780 | 0.527952730513 0.472047269487 781 | 0.991963139516 0.00803686048386 782 | 0.830514367484 0.169485632516 783 | 0.806157497641 0.193842502359 784 | 0.971462247889 0.0285377521115 785 | 0.997883778855 0.00211622114525 786 | 0.563345827795 0.436654172205 787 | 0.747413476572 0.252586523428 788 | 0.489684469932 0.510315530068/ 789 | 0.97834894437 0.0216510556305 790 | 0.969676996711 0.0303230032892 791 | 0.986442626321 0.0135573736792 792 | 0.999277329202 0.000722670797919 793 | 0.598940680277 0.401059319723 794 | 0.858883395344 0.141116604656 795 | 0.9019973298 0.0980026701997 796 | 0.478609503816 0.521390496184/ 797 | 0.947000405771 0.0529995942288 798 | 0.932559476318 0.0674405236819 799 | 0.468012833232 0.531987166768/ 800 | 0.842963878572 0.157036121428 801 | 0.854122402848 0.145877597152 802 | 0.848534329374 0.151465670626 803 | 0.899876626573 0.100123373427 804 | 0.674703369259 0.325296630741 805 | 0.987399738106 0.0126002618935 806 | 0.71488727122 0.28511272878 807 | 0.843561602446 0.156438397554 808 | 0.80301449817 0.19698550183 809 | 0.335045375366 0.664954624634/ 810 | 0.548622482092 0.451377517908 811 | 0.502172259911 0.497827740089 812 | 0.824324155758 0.175675844242 813 | 0.854678623281 0.145321376719 814 | 0.949107506141 0.0508924938586 815 | 0.967599783101 0.0324002168991 816 | 0.886675213708 0.113324786292 817 | 0.857925436488 0.142074563512 818 | 0.585641440649 0.414358559351 819 | 0.388851193319 0.611148806681/ 820 | 0.359725415552 0.640274584448/ 821 | 0.999941266552 5.87334475153e-05 822 | 0.998161651622 0.00183834837767 823 | 0.946609593645 0.053390406355 824 | 0.660548580931 0.339451419069 825 | 0.595420525287 0.404579474713 826 | 0.388851193319 0.611148806681/ 827 | 0.022385779858 0.977614220142/ 828 | 0.998475380291 0.00152461970857 829 | 0.718164406778 0.281835593222 830 | 0.731214274733 0.268785725267 831 | 0.965714392298 0.0342856077018 832 | 0.084694117266 0.915305882734/ 833 | 0.997599461409 0.00240053859087 834 | 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0.903957436221 0.0960425637789 415 | 0.708793592668 0.291206407332 416 | 0.996998266927 0.00300173307297 417 | 0.777724391721 0.222275608279 418 | 0.979671934463 0.0203280655369 419 | 0.966564477727 0.0334355222726 420 | 0.713761068035 0.286238931965 421 | 0.989607650797 0.0103923492028 422 | 0.28791387556 0.71208612444/ 423 | 0.998981342906 0.00101865709437 424 | 0.29477956576 0.70522043424/ 425 | 0.985964231813 0.0140357681869 426 | 0.878325033186 0.121674966814 427 | 0.585142897156 0.414857102844 428 | 0.99822144915 0.00177855085031 429 | 0.959441800318 0.0405581996819 430 | 0.887812662636 0.112187337364 431 | 0.992802239551 0.00719776044917 432 | 0.97929644664 0.02070355336 433 | 0.109387042806 0.890612957194/ 434 | 0.555707906801 0.444292093199 435 | 0.78540305136 0.21459694864 436 | 0.835902887729 0.164097112271 437 | 0.998382457403 0.00161754259703 438 | 0.996928249268 0.00307175073164 439 | 0.202209258132 0.797790741868/ 440 | 0.999701508249 0.000298491751042 441 | 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0.926498483884 0.0735015161156 523 | 0.707440169377 0.292559830623 524 | 0.960018568329 0.0399814316712 525 | 0.931225540281 0.0687744597194 526 | 0.590830176899 0.409169823101 527 | 0.996698919444 0.00330108055643 528 | 0.961905794097 0.0380942059027 529 | 0.53199672833 0.46800327167 530 | 0.99885689901 0.00114310099021 531 | 0.98198557859 0.0180144214096 532 | 0.85943741068 0.14056258932 533 | 0.996643941286 0.00335605871444 534 | 0.852934365378 0.147065634622 535 | 0.413117588322 0.586882411678/ 536 | 0.730975775463 0.269024224537 537 | 0.997633112851 0.00236688714858 538 | 0.999969451459 3.05485405888e-05 539 | 0.98374655441 0.0162534455899 540 | 0.999173660739 0.000826339261022 541 | 0.841533097404 0.158466902596 542 | 0.735212994693 0.264787005307 543 | 0.999847688589 0.000152311411348 544 | 0.639572507152 0.360427492848 545 | 0.977218559105 0.0227814408947 546 | 0.545745836349 0.454254163651 547 | 0.853058074842 0.146941925158 548 | 0.90047717019 0.0995228298102 549 | 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/result/threemix_NBfinal.txt: -------------------------------------------------------------------------------- 1 | 0.0307304205376 0.969269579462/ 2 | 0.00140616199052 0.998593838009 3 | 3.65243426272e-08 0.999999963476 4 | 0.756536136075 0.243463863925/ 5 | 0.127145611022 0.872854388978 6 | 0.319367757782 0.680632242218 7 | 0.307493548818 0.692506451182 8 | 0.660390243058 0.339609756942/ 9 | 0.129355341623 0.870644658377 10 | 0.100049870998 0.899950129002 11 | 0.0178307892358 0.982169210764 12 | 0.00344434801388 0.996555651986 13 | 0.0897461884977 0.910253811502 14 | 1.478838816e-06 0.999998521161 15 | 0.14040423347 0.85959576653 16 | 0.126939686949 0.873060313051 17 | 0.083607895993 0.916392104007 18 | 0.418520911558 0.581479088442 19 | 2.90754990457e-07 0.999999709245 20 | 0.00072663649191 0.999273363508 21 | 0.0261337968512 0.973866203149 22 | 0.00834885645788 0.991651143542 23 | 0.0328363487513 0.967163651249 24 | 4.30691610049e-05 0.999956930839 25 | 0.00187788777383 0.998122112226 26 | 0.0537897223781 0.946210277622 27 | 0.0208598568136 0.979140143186 28 | 0.17819892203 0.82180107797 29 | 0.0664943315354 0.933505668465 30 | 0.297741046886 0.702258953114 31 | 0.413777689939 0.586222310061 32 | 0.785317846137 0.214682153863/ 33 | 0.806751509424 0.193248490576/ 34 | 0.551427969459 0.448572030541/ 35 | 0.0662307696989 0.933769230301 36 | 0.00713589157536 0.992864108425 37 | 9.44494664371e-06 0.999990555053 38 | 0.0935217285096 0.90647827149 39 | 0.0591803410237 0.940819658976 40 | 0.113540364674 0.886459635326 41 | 0.6539715306 0.3460284694/ 42 | 0.00909915525692 0.990900844743 43 | 0.0945405833449 0.905459416655 44 | 1.89866834467e-05 0.999981013317 45 | 0.000100249323345 0.999899750677 46 | 0.0562137525692 0.943786247431 47 | 0.00133205557116 0.998667944429 48 | 1.35375324379e-05 0.999986462468 49 | 0.00222946430822 0.997770535692 50 | 0.0816837869548 0.918316213045 51 | 0.00720142778976 0.99279857221 52 | 0.749206939483 0.250793060517/ 53 | 0.0849126760084 0.915087323992 54 | 0.255138628587 0.744861371413 55 | 0.000332439488158 0.999667560512 56 | 0.000908199688875 0.999091800311 57 | 0.0775956678104 0.92240433219 58 | 0.699326551098 0.300673448902/ 59 | 0.00863425907089 0.991365740929 60 | 1.99679314082e-06 0.999998003207 61 | 0.516782819171 0.483217180829/ 62 | 6.56488510634e-05 0.999934351149 63 | 0.45801549685 0.54198450315 64 | 0.0265139363334 0.973486063667 65 | 0.0667727743895 0.93322722561 66 | 0.000325949763356 0.999674050237 67 | 0.930791646872 0.0692083531278/ 68 | 0.148351113718 0.851648886282 69 | 0.0946258056951 0.905374194305 70 | 1.60611609908e-06 0.999998393884 71 | 0.00213414591616 0.997865854084 72 | 0.123785787145 0.876214212855 73 | 0.137029804105 0.862970195895 74 | 0.0763065910279 0.923693408972 75 | 0.0976632703113 0.902336729689 76 | 0.0496625838525 0.950337416147 77 | 0.0159607272 0.9840392728 78 | 0.00305664624287 0.996943353757 79 | 0.188842483259 0.811157516741 80 | 0.109802473869 0.890197526131 81 | 0.00224139392392 0.997758606076 82 | 0.172098170632 0.827901829368 83 | 0.694943502892 0.305056497108/ 84 | 0.21305287506 0.78694712494 85 | 0.013234921723 0.986765078277 86 | 1.53484544421e-07 0.999999846515 87 | 0.813622106177 0.186377893823/ 88 | 0.00293368198287 0.997066318017 89 | 0.147612483845 0.852387516155 90 | 0.173398079789 0.826601920211 91 | 0.0238890507876 0.976110949212 92 | 0.0225889573755 0.977411042625 93 | 0.0050687216396 0.99493127836 94 | 0.184762398817 0.815237601183 95 | 0.060471981938 0.939528018062 96 | 0.418520911558 0.581479088442 97 | 0.0118888662 0.9881111338 98 | 0.137130587306 0.862869412694 99 | 0.0189872629511 0.981012737049 100 | 3.52399097875e-05 0.99996476009 101 | 6.68101264507e-10 0.999999999332 102 | 0.000116137020623 0.999883862979 103 | 0.12698216285 0.87301783715 104 | 0.249685449043 0.750314550957 105 | 0.5 0.5 106 | 0.0219577836168 0.978042216383 107 | 0.00148925291782 0.998510747082 108 | 0.0863949195439 0.913605080456 109 | 0.0421140111619 0.957885988838 110 | 0.062579558027 0.937420441973 111 | 0.53099142274 0.46900857726/ 112 | 0.436193382775 0.563806617225 113 | 0.120405153103 0.879594846897 114 | 0.176189216324 0.823810783676 115 | 0.211400832903 0.788599167097 116 | 0.10407446649 0.89592553351 117 | 0.464051873922 0.535948126078 118 | 0.0172082785406 0.982791721459 119 | 0.33463026892 0.66536973108 120 | 0.979122229738 0.0208777702625/ 121 | 0.00182214864451 0.998177851355 122 | 0.110846271567 0.889153728433 123 | 0.000421734033317 0.999578265967 124 | 0.000174916263354 0.999825083737 125 | 0.0635996148386 0.936400385161 126 | 0.00465730867918 0.995342691321 127 | 0.699100622541 0.300899377459/ 128 | 0.0466463723366 0.953353627663 129 | 0.123572251488 0.876427748512 130 | 0.210159977517 0.789840022483 131 | 0.000402356846871 0.999597643153 132 | 0.00656675563005 0.99343324437 133 | 0.0458902967607 0.954109703239 134 | 0.112601804159 0.887398195841 135 | 3.69332293078e-05 0.999963066771 136 | 0.0730257408503 0.92697425915 137 | 0.462317968547 0.537682031453 138 | 0.000344538000449 0.999655462 139 | 0.00298425926767 0.997015740732 140 | 0.100925430143 0.899074569857 141 | 0.54934827264 0.45065172736/ 142 | 0.477254701543 0.522745298457 143 | 0.000579487356442 0.999420512644 144 | 4.43837819539e-05 0.999955616218 145 | 0.66708953373 0.33291046627/ 146 | 0.0251757187016 0.974824281298 147 | 0.268742345274 0.731257654726 148 | 0.153627802605 0.846372197395 149 | 0.00242951238956 0.99757048761 150 | 0.0722070684571 0.927792931543 151 | 0.753113438426 0.246886561574/ 152 | 0.0154243823752 0.984575617625 153 | 0.00176604592883 0.998233954071 154 | 0.145393930627 0.854606069373 155 | 0.132710647633 0.867289352367 156 | 0.280188620793 0.719811379207 157 | 0.0551252166336 0.944874783366 158 | 0.151503768517 0.848496231483 159 | 0.892947207294 0.107052792706/ 160 | 0.433715730642 0.566284269358 161 | 0.5 0.5 162 | 0.0446875583019 0.955312441698 163 | 0.0583457506026 0.941654249397 164 | 0.0319929015107 0.968007098489 165 | 0.254117716443 0.745882283557 166 | 0.487722354249 0.512277645751 167 | 2.96343559993e-07 0.999999703656 168 | 0.5 0.5 169 | 0.472006919497 0.527993080503 170 | 1.02002265668e-05 0.999989799773 171 | 0.0222717633465 0.977728236654 172 | 0.0576067109595 0.94239328904 173 | 0.106883679611 0.893116320389 174 | 0.00109578075559 0.998904219244 175 | 5.72666359741e-09 0.999999994273 176 | 0.889122243123 0.110877756877/ 177 | 0.0850702015303 0.91492979847 178 | 0.503401624824 0.496598375176/ 179 | 0.312139539234 0.687860460766 180 | 0.593430964648 0.406569035352/ 181 | 0.104585983555 0.895414016445 182 | 0.092773997577 0.907226002423 183 | 0.00362342155255 0.996376578447 184 | 0.119822883528 0.880177116472 185 | 0.00127950048566 0.998720499514 186 | 0.05406083122 0.94593916878 187 | 5.27778156613e-05 0.999947222184 188 | 0.111834882656 0.888165117344 189 | 0.30688977088 0.69311022912 190 | 0.0400495411916 0.959950458808 191 | 0.0779051678714 0.922094832129 192 | 0.433715730642 0.566284269358 193 | 0.449101855423 0.550898144577 194 | 0.023490716474 0.976509283526 195 | 0.00561322778042 0.99438677222 196 | 0.0674930185069 0.932506981493 197 | 0.0856593450295 0.91434065497 198 | 0.236269707641 0.763730292359 199 | 0.00580554721657 0.994194452783 200 | 0.567926824933 0.432073175067/ 201 | 0.168446882615 0.831553117385 202 | 0.425085123191 0.574914876809 203 | 0.922096376405 0.0779036235953/ 204 | 0.262942046991 0.737057953009 205 | 0.0354849349664 0.964515065034 206 | 0.0444961730468 0.955503826953 207 | 9.94448772411e-09 0.999999990056 208 | 1.67794277343e-07 0.999999832206 209 | 0.0453525603506 0.954647439649 210 | 0.388836256292 0.611163743708 211 | 0.0718560052078 0.928143994792 212 | 0.221815791704 0.778184208296 213 | 0.227447483891 0.772552516109 214 | 0.0591803410237 0.940819658976 215 | 0.309132893661 0.690867106339 216 | 0.000925098741778 0.999074901258 217 | 0.709736955963 0.290263044037/ 218 | 0.18298129509 0.81701870491 219 | 0.418520911558 0.581479088442 220 | 0.000631420812981 0.999368579187 221 | 0.0565175322538 0.943482467746 222 | 0.0106481564229 0.989351843577 223 | 0.28823540508 0.71176459492 224 | 0.0718560052078 0.928143994792 225 | 7.97917725975e-05 0.999920208227 226 | 0.0175307095119 0.982469290488 227 | 0.00133263274185 0.998667367258 228 | 0.209366622258 0.790633377742 229 | 0.00022692118523 0.999773078815 230 | 0.00252892204538 0.997471077955 231 | 0.148351113718 0.851648886282 232 | 0.115548782948 0.884451217052 233 | 0.00054184765059 0.999458152349 234 | 0.0218260320671 0.978173967933 235 | 0.385022636114 0.614977363886 236 | 0.873975023194 0.126024976806/ 237 | 0.732126702311 0.267873297689/ 238 | 0.0148309467832 0.985169053217 239 | 0.204069907062 0.795930092938 240 | 0.0313356775164 0.968664322484 241 | 0.060303091945 0.939696908055 242 | 0.457733645513 0.542266354487 243 | 0.000283020220874 0.999716979779 244 | 0.322529952045 0.677470047955 245 | 0.0225550698415 0.977444930158 246 | 0.00311907659094 0.996880923409 247 | 0.00465781261889 0.995342187381 248 | 0.85773800033 0.14226199967/ 249 | 0.000491820754168 0.999508179246 250 | 0.373387058071 0.626612941929 251 | 0.00785138709671 0.992148612903 252 | 2.13140596659e-05 0.99997868594 253 | 0.000323388429575 0.99967661157 254 | 0.670887326717 0.329112673283/ 255 | 0.614380065423 0.385619934577/ 256 | 0.108318416509 0.891681583491 257 | 0.646521267839 0.353478732161/ 258 | 0.301881344305 0.698118655695 259 | 2.65021199941e-05 0.99997349788 260 | 0.140769401772 0.859230598228 261 | 0.490171062552 0.509828937448 262 | 0.043184042264 0.956815957736 263 | 0.000174885526096 0.999825114474 264 | 0.0402005564175 0.959799443583 265 | 0.00340869950553 0.996591300494 266 | 0.0496625838525 0.950337416147 267 | 5.86498362699e-05 0.999941350164 268 | 0.791220897865 0.208779102135/ 269 | 0.062574673222 0.937425326778 270 | 8.77662259831e-07 0.999999122338 271 | 0.000946676049701 0.99905332395 272 | 0.00601977883634 0.993980221164 273 | 0.171787049036 0.828212950964 274 | 0.0281921069483 0.971807893052 275 | 0.0820038531139 0.917996146886 276 | 0.0764481206438 0.923551879356 277 | 0.157706971395 0.842293028605 278 | 0.740796107539 0.259203892461/ 279 | 3.12948597578e-05 0.99996870514 280 | 0.345055754247 0.654944245753 281 | 0.492722065279 0.507277934721 282 | 0.0971385701138 0.902861429886 283 | 0.008228621305 0.991771378695 284 | 0.542311799565 0.457688200435/ 285 | 0.0356198634402 0.96438013656 286 | 0.011818537651 0.988181462349 287 | 0.0453525603506 0.954647439649 288 | 0.626069129181 0.373930870819/ 289 | 0.311552465465 0.688447534535 290 | 0.0131168760322 0.986883123968 291 | 0.296077983818 0.703922016182 292 | 0.0420240963253 0.957975903675 293 | 0.0165779089078 0.983422091092 294 | 0.163148662621 0.836851337379 295 | 0.418520911558 0.581479088442 296 | 0.168322852617 0.831677147383 297 | 0.0767159065484 0.923284093452 298 | 0.0817621384012 0.918237861599 299 | 0.0729780767431 0.927021923257 300 | 0.218560663345 0.781439336655 301 | 0.999170232653 0.00082976734703 302 | 0.969161483763 0.0308385162368 303 | 0.973694113064 0.0263058869362 304 | 0.997131826713 0.00286817328665 305 | 0.999999032998 9.67002150375e-07 306 | 0.988572433828 0.0114275661723 307 | 0.999981354146 1.86458536545e-05 308 | 0.369708557503 0.630291442497/ 309 | 0.664657156322 0.335342843678 310 | 0.997536808332 0.00246319166844 311 | 0.76521919312 0.23478080688 312 | 0.960466740144 0.0395332598556 313 | 0.999998996115 1.00388532342e-06 314 | 0.999981414084 1.85859158047e-05 315 | 0.994399889671 0.005600110329 316 | 0.957037488612 0.0429625113884 317 | 0.997789155196 0.00221084480382 318 | 0.943775351346 0.0562246486537 319 | 0.967746893221 0.0322531067788 320 | 0.828496369902 0.171503630098 321 | 0.972247422778 0.0277525772217 322 | 0.99942307218 0.000576927820186 323 | 0.998156803278 0.00184319672212 324 | 0.976655571357 0.0233444286434 325 | 0.998788842477 0.0012111575232 326 | 0.796992636037 0.203007363963 327 | 0.99970343612 0.000296563880367 328 | 0.990275537648 0.0097244623518 329 | 0.0702388854843 0.929761114516/ 330 | 0.990602532149 0.00939746785063 331 | 0.738191359459 0.261808640541 332 | 0.0028196688062 0.997180331194/ 333 | 0.999996415415 3.58458512394e-06 334 | 0.983460194517 0.0165398054826 335 | 0.684833711266 0.315166288734 336 | 0.999990886891 9.11310938048e-06 337 | 0.99962175726 0.000378242739503 338 | 0.738191359459 0.261808640541 339 | 0.402909284156 0.597090715844/ 340 | 0.691578250525 0.308421749475 341 | 0.845671886868 0.154328113132 342 | 0.87091250302 0.12908749698 343 | 0.999002616189 0.000997383810597 344 | 0.977197551379 0.0228024486207 345 | 0.894349780353 0.105650219647 346 | 0.998998109166 0.00100189083421 347 | 0.999306207914 0.000693792086326 348 | 0.999999979976 2.00236973254e-08 349 | 0.822360037148 0.177639962852 350 | 0.94764774164 0.0523522583602 351 | 0.977931636048 0.0220683639522 352 | 0.990350512244 0.00964948775647 353 | 0.905470627985 0.0945293720153 354 | 0.619248395469 0.380751604531 355 | 0.556301131601 0.443698868399 356 | 0.999972558201 2.74417992026e-05 357 | 0.993063636279 0.00693636372057 358 | 0.999979277955 2.07220447828e-05 359 | 0.986475339928 0.0135246600719 360 | 0.826955232251 0.173044767749 361 | 0.968788314295 0.031211685705 362 | 0.495073351896 0.504926648104/ 363 | 0.406818232415 0.593181767585/ 364 | 0.979183489742 0.0208165102582 365 | 0.917215626985 0.0827843730149 366 | 0.855099769525 0.144900230475 367 | 0.999870455201 0.000129544799147 368 | 0.999999199724 8.00275981129e-07 369 | 0.99998999185 1.00081497541e-05 370 | 0.991684363161 0.00831563683904 371 | 0.997529064063 0.00247093593729 372 | 0.936989242136 0.0630107578644 373 | 0.9882028268 0.0117971732004 374 | 0.60347901826 0.39652098174 375 | 0.999937282394 6.27176061449e-05 376 | 0.999959622664 4.03773362712e-05 377 | 0.975431764357 0.0245682356434 378 | 0.998932578592 0.00106742140826 379 | 0.994783884867 0.00521611513316 380 | 0.937647722319 0.0623522776809 381 | 0.599376342237 0.400623657763 382 | 0.881764994976 0.118235005024 383 | 0.970385150963 0.0296148490372 384 | 0.980218124086 0.0197818759141 385 | 0.99638694159 0.00361305841014 386 | 0.999999054705 9.45294820566e-07 387 | 0.966530991044 0.0334690089557 388 | 0.817637099871 0.182362900129 389 | 0.999977531339 2.24686608887e-05 390 | 0.207525626174 0.792474373826/ 391 | 0.999999986644 1.33559502527e-08 392 | 0.936238202635 0.0637617973654 393 | 0.766245471843 0.233754528157 394 | 0.999004135525 0.000995864475123 395 | 0.748622784184 0.251377215816 396 | 0.000612810891157 0.999387189109/ 397 | 0.967965752397 0.0320342476027 398 | 0.999882907765 0.000117092234788 399 | 0.997588689685 0.00241131031463 400 | 0.978116030118 0.0218839698824 401 | 0.999999996581 3.41885011822e-09 402 | 0.989199529889 0.0108004701108 403 | 0.999828202379 0.000171797620612 404 | 0.997255776278 0.00274422372204 405 | 0.999958099471 4.19005293577e-05 406 | 0.999999018423 9.81577133883e-07 407 | 0.99999976286 2.37140370339e-07 408 | 0.998662028679 0.0013379713213 409 | 0.974466887994 0.0255331120059 410 | 0.617851290367 0.382148709633 411 | 0.999900313872 9.96861281535e-05 412 | 0.9999998561 1.43899546535e-07 413 | 0.999991249275 8.7507246009e-06 414 | 0.997190852028 0.00280914797232 415 | 0.996354691078 0.00364530892198 416 | 0.999969037655 3.09623447981e-05 417 | 0.888660707771 0.111339292229 418 | 0.925086476777 0.0749135232234 419 | 0.974156758017 0.0258432419833 420 | 0.978809851228 0.0211901487715 421 | 0.995079426271 0.00492057372935 422 | 0.748736854569 0.251263145431 423 | 0.999999822071 1.77929275434e-07 424 | 0.5 0.5 425 | 0.935577015795 0.0644229842054 426 | 0.994784760151 0.00521523984903 427 | 0.778542612568 0.221457387432 428 | 0.999999396112 6.0388786483e-07 429 | 0.999747068751 0.000252931249156 430 | 0.915777061731 0.0842229382685 431 | 0.999996356263 3.64373665811e-06 432 | 0.998947105245 0.00105289475479 433 | 0.0389177040795 0.961082295921/ 434 | 0.918688133412 0.0813118665881 435 | 0.853761231384 0.146238768616 436 | 0.999877978828 0.000122021172065 437 | 0.99990666506 9.33349398756e-05 438 | 0.999953907159 4.60928412863e-05 439 | 0.416604472319 0.583395527681/ 440 | 0.999955948909 4.40510911411e-05 441 | 0.999086415197 0.000913584803058 442 | 0.999938171907 6.18280928776e-05 443 | 0.999999999412 5.8848364781e-10 444 | 0.342295223399 0.657704776601/ 445 | 0.953023487656 0.0469765123445 446 | 0.267726606902 0.732273393098/ 447 | 0.999711791192 0.000288208808346 448 | 0.85036377919 0.14963622081 449 | 0.999625817669 0.000374182330779 450 | 0.988652823073 0.0113471769266 451 | 0.999995212124 4.78787609638e-06 452 | 0.968827449187 0.0311725508132 453 | 0.99966661082 0.000333389179542 454 | 0.999999961634 3.83664281226e-08 455 | 0.995874220425 0.00412577957472 456 | 0.999996813787 3.18621304224e-06 457 | 0.918900926695 0.0810990733054 458 | 0.999999000305 9.99694523892e-07 459 | 0.99965233523 0.000347664770207 460 | 0.999655699461 0.000344300539501 461 | 0.968048086866 0.0319519131339 462 | 0.997568924863 0.00243107513666 463 | 0.99539856366 0.0046014363398 464 | 0.999950809191 4.91908086189e-05 465 | 0.992894508371 0.00710549162921 466 | 0.5 0.5 467 | 0.999997027047 2.97295291427e-06 468 | 0.981999679453 0.0180003205465 469 | 0.838785132921 0.161214867079 470 | 0.736993457043 0.263006542957 471 | 0.74459200222 0.25540799778 472 | 0.876963196387 0.123036803613 473 | 0.282450247299 0.717549752701/ 474 | 0.999883085738 0.000116914261663 475 | 0.999986712194 1.32878063186e-05 476 | 0.972734976935 0.0272650230648 477 | 0.861674512407 0.138325487593 478 | 0.0130608312695 0.986939168731/ 479 | 0.997023589715 0.00297641028508 480 | 0.982058534654 0.0179414653464 481 | 0.89784844431 0.10215155569 482 | 0.993030928482 0.00696907151843 483 | 0.22959454935 0.77040545065/ 484 | 0.957258647682 0.0427413523177 485 | 0.993478250119 0.0065217498811 486 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0.35919813834 0.64080186166 123 | 0.0139762978235 0.986023702176 124 | 0.0645728553064 0.935427144694 125 | 0.227172605639 0.772827394361 126 | 0.029784539084 0.970215460916 127 | 0.282030787603 0.717969212397 128 | 0.0638398295664 0.936160170434 129 | 0.196852732325 0.803147267675 130 | 0.934718311678 0.0652816883222/ 131 | 0.282647476436 0.717352523564 132 | 0.116019549059 0.883980450941 133 | 0.00273545788739 0.997264542113 134 | 0.218969228461 0.781030771539 135 | 0.0780229457366 0.921977054263 136 | 0.433138810802 0.566861189198 137 | 0.101365394002 0.898634605998 138 | 0.258046430961 0.741953569039 139 | 0.00680804595514 0.993191954045 140 | 0.235064013238 0.764935986762 141 | 0.374723781159 0.625276218841 142 | 0.57453102868 0.42546897132/ 143 | 0.346169002807 0.653830997193 144 | 0.118666522527 0.881333477473 145 | 0.138289718898 0.861710281102 146 | 0.0464506151421 0.953549384858 147 | 0.349494370978 0.650505629022 148 | 0.0525355504863 0.947464449514 149 | 0.119563248372 0.880436751628 150 | 0.432261527573 0.567738472427 151 | 0.821792589823 0.178207410177/ 152 | 0.0851903280867 0.914809671913 153 | 0.000657131812069 0.999342868188 154 | 0.139059941539 0.860940058461 155 | 0.563907537563 0.436092462437/ 156 | 0.0740272228014 0.925972777199 157 | 0.0254963219674 0.974503678033 158 | 0.167211142767 0.832788857233 159 | 0.00019658284944 0.999803417151 160 | 0.59882113618 0.40117886382/ 161 | 0.116532660986 0.883467339014 162 | 4.94823891146e-05 0.999950517611 163 | 0.000310143378635 0.999689856621 164 | 0.105025055617 0.894974944383 165 | 0.0740074919265 0.925992508074 166 | 0.00265469781023 0.99734530219 167 | 0.845628127241 0.154371872759/ 168 | 0.349494370978 0.650505629022 169 | 0.236416388983 0.763583611017 170 | 0.888038812237 0.111961187763/ 171 | 0.000664195575682 0.999335804424 172 | 0.384433343063 0.615566656937 173 | 0.182476854431 0.817523145569 174 | 0.0374090475382 0.962590952462 175 | 0.00156575751446 0.998434242486 176 | 0.0598270742112 0.940172925789 177 | 0.0160892567501 0.98391074325 178 | 0.00978521649168 0.990214783508 179 | 0.284303031489 0.715696968511 180 | 0.00829496328036 0.99170503672 181 | 0.0918805389539 0.908119461046 182 | 0.423377987897 0.576622012103 183 | 0.286456486591 0.713543513409 184 | 0.0184467517158 0.981553248284 185 | 0.349494370978 0.650505629022 186 | 0.336043951665 0.663956048335 187 | 0.0180306225861 0.981969377414 188 | 0.0885469036767 0.911453096323 189 | 0.124353765778 0.875646234222 190 | 0.241612156809 0.758387843191 191 | 0.0960561856946 0.903943814305 192 | 0.0258984537405 0.97410154626 193 | 0.0239684706669 0.976031529333 194 | 0.441976749724 0.558023250276 195 | 0.280419337957 0.719580662043 196 | 0.0712960202726 0.928703979727 197 | 0.203071769983 0.796928230017 198 | 0.0138000815912 0.986199918409 199 | 0.117139515925 0.882860484075 200 | 0.00477357159205 0.995226428408 201 | 0.0212555872865 0.978744412713 202 | 0.0927608650567 0.907239134943 203 | 0.00159818085532 0.998401819145 204 | 0.0733935363954 0.926606463605 205 | 0.000137916477089 0.999862083523 206 | 0.445495255267 0.554504744733 207 | 0.0599393589296 0.94006064107 208 | 0.509159428391 0.490840571609/ 209 | 0.013132625884 0.986867374116 210 | 0.522717883546 0.477282116454/ 211 | 0.114794785581 0.885205214419 212 | 0.00828512685041 0.99171487315 213 | 0.408197423407 0.591802576593 214 | 0.407745721534 0.592254278466 215 | 0.0349928912814 0.965007108719 216 | 0.109472458054 0.890527541946 217 | 0.00362407516992 0.99637592483 218 | 0.561270578122 0.438729421878/ 219 | 0.0540955150926 0.945904484907 220 | 0.243648481723 0.756351518277 221 | 0.00392411746776 0.996075882532 222 | 0.18409878133 0.81590121867 223 | 0.0705862573705 0.92941374263 224 | 0.074886590078 0.925113409922 225 | 0.0284302560548 0.971569743945 226 | 0.186654761744 0.813345238256 227 | 0.7713946774 0.2286053226/ 228 | 0.192715489007 0.807284510993 229 | 0.0392289517634 0.960771048237 230 | 0.471726952881 0.528273047119 231 | 0.05838803618 0.94161196382 232 | 0.00854481578952 0.99145518421 233 | 0.00366913072651 0.996330869273 234 | 0.000705519740275 0.99929448026 235 | 0.149308341927 0.850691658073 236 | 0.251058678057 0.748941321943 237 | 0.0801417333495 0.919858266651 238 | 0.014741327136 0.985258672864 239 | 0.00241460002996 0.99758539997 240 | 0.980974872596 0.0190251274039/ 241 | 0.00757410091092 0.992425899089 242 | 0.476300568796 0.523699431204 243 | 0.352915626339 0.647084373661 244 | 0.000222928243023 0.999777071757 245 | 0.00790126175292 0.992098738247 246 | 0.167202726541 0.832797273459 247 | 0.00679107663163 0.993208923368 248 | 0.030314094311 0.969685905689 249 | 0.0213719787876 0.978628021212 250 | 0.683569352579 0.316430647421/ 251 | 0.200693463093 0.799306536907 252 | 0.536917921272 0.463082078728/ 253 | 0.0809975359762 0.919002464024 254 | 0.997943372721 0.00205662727885/ 255 | 0.858373301584 0.141626698416/ 256 | 4.26184940339e-07 0.999999573815 257 | 0.620402392931 0.379597607069/ 258 | 0.563116275163 0.436883724837/ 259 | 0.0142271581642 0.985772841836 260 | 0.0494522971202 0.95054770288 261 | 0.349494370978 0.650505629022 262 | 0.199487339248 0.800512660752 263 | 0.0976600975086 0.902339902491 264 | 0.243648481723 0.756351518277 265 | 0.329976913383 0.670023086617 266 | 0.127281095853 0.872718904147 267 | 0.113782942286 0.886217057714 268 | 0.557095834809 0.442904165191/ 269 | 0.476164920698 0.523835079302 270 | 0.0719125920029 0.928087407997 271 | 0.0785397229038 0.921460277096 272 | 0.737482016852 0.262517983148/ 273 | 0.13594488245 0.86405511755 274 | 0.0791039411386 0.920896058861 275 | 0.174807900112 0.825192099888 276 | 0.190722735641 0.809277264359 277 | 0.26270123849 0.73729876151 278 | 0.165401956647 0.834598043353 279 | 0.180302770423 0.819697229577 280 | 0.0917557983543 0.908244201646 281 | 0.34755668196 0.65244331804 282 | 0.00813360630495 0.991866393695 283 | 0.252206360028 0.747793639972 284 | 0.201908623993 0.798091376007 285 | 0.408330835877 0.591669164123 286 | 0.18409635727 0.81590364273 287 | 0.110608302844 0.889391697156 288 | 0.360632145055 0.639367854945 289 | 0.276900469268 0.723099530732 290 | 0.333720363921 0.666279636079 291 | 0.020873062889 0.979126937111 292 | 0.24323237626 0.75676762374 293 | 0.0113197225435 0.988680277457 294 | 0.227740611157 0.772259388843 295 | 0.226528882703 0.773471117297 296 | 0.0575113147273 0.942488685273 297 | 0.154708564106 0.845291435894 298 | 0.393896010325 0.606103989675 299 | 0.131524470472 0.868475529528 300 | 0.00234540378653 0.997654596213 301 | 0.0685374640421 0.931462535958 302 | 0.251058678057 0.748941321943 303 | 0.507599311733 0.492400688267/ 304 | 0.164813031408 0.835186968592 305 | 0.115693748489 0.884306251511 306 | 0.243138143723 0.756861856277 307 | 0.172466204762 0.827533795238 308 | 0.0108583789701 0.98914162103 309 | 0.156547756893 0.843452243107 310 | 0.349494370978 0.650505629022 311 | 0.372533244187 0.627466755813 312 | 0.197873912029 0.802126087971 313 | 0.00187448786605 0.998125512134 314 | 7.87382854369e-06 0.999992126171 315 | 0.00848400698503 0.991515993015 316 | 0.119352013721 0.880647986279 317 | 0.0172530801101 0.98274691989 318 | 0.349494370978 0.650505629022 319 | 0.0043404650617 0.995659534938 320 | 0.199000011501 0.800999988499 321 | 0.022379122553 0.977620877447 322 | 0.277059508142 0.722940491858 323 | 0.123668278828 0.876331721172 324 | 0.0551248970378 0.944875102962 325 | 0.157339226158 0.842660773842 326 | 0.398553129507 0.601446870493 327 | 5.09264151732e-05 0.999949073585 328 | 0.171784287772 0.828215712228 329 | 0.00668869334623 0.993311306654 330 | 0.0759452728955 0.924054727104 331 | 0.101626318544 0.898373681456 332 | 0.0650660316065 0.934933968393 333 | 0.442466256908 0.557533743092 334 | 0.956084182189 0.0439158178111/ 335 | 0.0836891666287 0.916310833371 336 | 0.184314511636 0.815685488364 337 | 0.236908374985 0.763091625015 338 | 0.0895642407727 0.910435759227 339 | 0.0555300899114 0.944469910089 340 | 0.176649662351 0.823350337649 341 | 0.00611496441961 0.99388503558 342 | 0.013290617221 0.986709382779 343 | 0.105722494553 0.894277505447 344 | 0.00020228504335 0.999797714957 345 | 0.290308262366 0.709691737634 346 | 0.0400584798903 0.95994152011 347 | 0.543964716185 0.456035283815/ 348 | 0.284892345203 0.715107654797 349 | 0.342585553639 0.657414446361 350 | 0.0516839743032 0.948316025697 351 | 0.324761056618 0.675238943382 352 | 0.19793010424 0.80206989576 353 | 0.235813131552 0.764186868448 354 | 0.453844730938 0.546155269062 355 | 0.00575210002242 0.994247899978 356 | 1.83879124135e-06 0.999998161209 357 | 0.0982637649262 0.901736235074 358 | 0.73527009756 0.26472990244/ 359 | 0.299896445911 0.700103554089 360 | 0.100244038665 0.899755961335 361 | 0.101365394002 0.898634605998 362 | 0.396800364108 0.603199635892 363 | 0.349494370978 0.650505629022 364 | 0.0964188478294 0.903581152171 365 | 0.0247718125904 0.97522818741 366 | 0.0733935363954 0.926606463605 367 | 0.0499545826762 0.950045417324 368 | 0.139291020616 0.860708979384 369 | 0.115693748489 0.884306251511 370 | 9.71298598401e-05 0.99990287014 371 | 0.0426867481717 0.957313251828 372 | 0.00127566999266 0.998724330007 373 | 0.0678121527623 0.932187847238 374 | 0.0123295620043 0.987670437996 375 | 0.157441851707 0.842558148293 376 | 0.191482872658 0.808517127342 377 | 0.364572663156 0.635427336844 378 | 0.0147595580027 0.985240441997 379 | 0.112802760211 0.887197239789 380 | 0.127378589857 0.872621410143 381 | 0.017246156673 0.982753843327 382 | 0.813187492104 0.186812507896/ 383 | 0.237939461194 0.762060538806 384 | 0.00798670243384 0.992013297566 385 | 0.386829431347 0.613170568653 386 | 0.00348761352715 0.996512386473 387 | 0.0593106654111 0.940689334589 388 | 0.094217890964 0.905782109036 389 | 0.0348095011678 0.965190498832 390 | 0.349494370978 0.650505629022 391 | 0.0133241948004 0.9866758052 392 | 0.0791039411386 0.920896058861 393 | 0.230220793246 0.769779206754 394 | 0.418593784097 0.581406215903 395 | 0.730170641748 0.269829358252/ 396 | 0.14967468356 0.85032531644 397 | 0.906928303013 0.0930716969872 398 | 0.00358414187899 0.996415858121 399 | 0.0226681152364 0.977331884764 400 | 0.503349862021 0.496650137979/ 401 | 0.0759513155656 0.924048684434 402 | 0.253382327202 0.746617672798 403 | 0.316583467761 0.683416532239 404 | 0.0469406253202 0.95305937468 405 | 0.633696120375 0.366303879625/ 406 | 0.263656338667 0.736343661333 407 | 0.2941769666 0.7058230334 408 | 0.391143777868 0.608856222132 409 | 0.002592816822 0.997407183178 410 | 0.518476452117 0.481523547883/ 411 | 0.00490387089404 0.995096129106 412 | 0.349494370978 0.650505629022 413 | 0.107530908151 0.892469091849 414 | 0.00149765890872 0.998502341091 415 | 0.156795639032 0.843204360968 416 | 0.100259652339 0.899740347661 417 | 0.27730208518 0.72269791482 418 | 0.0067158380819 0.993284161918 419 | 0.00082073094075 0.999179269059 420 | 0.439822239349 0.560177760651 421 | 0.810573666475 0.189426333525/ 422 | 0.0895642407727 0.910435759227 423 | 0.428357836972 0.571642163028 424 | 0.0648830383258 0.935116961674 425 | 0.406744666914 0.593255333086 426 | 0.960019180729 0.039980819271 427 | 0.935113120576 0.0648868794241 428 | 0.801766049762 0.198233950238 429 | 0.507702676018 0.492297323982 430 | 0.983831545927 0.0161684540727 431 | 0.999785438475 0.000214561524613 432 | 0.349494370978 0.650505629022/ 433 | 0.405437528428 0.594562471572/ 434 | 0.997340672359 0.00265932764075 435 | 0.997854731093 0.00214526890664 436 | 0.999542189031 0.00045781096897 437 | 0.815125430926 0.184874569074 438 | 0.272946662008 0.727053337992/ 439 | 0.676888679946 0.323111320054 440 | 0.905141217386 0.0948587826139 441 | 0.792276516365 0.207723483635 442 | 0.404463238866 0.595536761134/ 443 | 0.708647087231 0.291352912769 444 | 0.770410920751 0.229589079249 445 | 0.488726101135 0.511273898865/ 446 | 0.992918667187 0.00708133281271 447 | 0.791252003279 0.208747996721 448 | 0.940814549532 0.0591854504685 449 | 0.999943868646 5.61313537679e-05 450 | 0.694343402053 0.305656597947 451 | 0.407428723282 0.592571276718/ 452 | 0.998895197496 0.00110480250407 453 | 0.669299685553 0.330700314447 454 | 0.287878654355 0.712121345645/ 455 | 0.997563758265 0.00243624173532 456 | 0.461009437919 0.538990562081/ 457 | 0.856366117253 0.143633882747 458 | 0.9938703114 0.00612968859959 459 | 0.962480944146 0.0375190558538 460 | 0.999948210201 5.17897988501e-05 461 | 0.994705903265 0.00529409673498 462 | 0.997626470759 0.00237352924055 463 | 0.972565680158 0.027434319842 464 | 0.881419607697 0.118580392303 465 | 0.845780343769 0.154219656231 466 | 0.87765440023 0.12234559977 467 | 0.865884197651 0.134115802349 468 | 0.919980827867 0.0800191721332 469 | 0.830030469071 0.169969530929 470 | 0.994609690735 0.00539030926487 471 | 0.999137387996 0.00086261200357 472 | 0.9533144432 0.0466855567996 473 | 0.999791113668 0.00020888633168 474 | 0.995694069881 0.0043059301191 475 | 0.902090300782 0.0979096992178 476 | 0.882708411973 0.117291588027 477 | 0.828267475985 0.171732524015 478 | 0.999899114379 0.000100885621093 479 | 0.770812665224 0.229187334776 480 | 0.810381901114 0.189618098886 481 | 0.992196736706 0.00780326329388 482 | 0.742453699734 0.257546300266 483 | 0.997765393918 0.00223460608152 484 | 0.95621854994 0.0437814500604 485 | 0.364572663156 0.635427336844/ 486 | 0.57354486792 0.42645513208 487 | 0.610381637069 0.389618362931 488 | 0.994140687207 0.00585931279276 489 | 0.962986603292 0.037013396708 490 | 0.94249165492 0.0575083450796 491 | 0.983895056283 0.0161049437166 492 | 0.961125704132 0.038874295868 493 | 0.902729020497 0.0972709795025 494 | 0.942082480583 0.0579175194175 495 | 0.887575912205 0.112424087795 496 | 0.985877462375 0.0141225376249 497 | 0.956125330918 0.0438746690823 498 | 0.994381670422 0.00561832957838 499 | 0.530991126633 0.469008873367 500 | 0.998978758284 0.00102124171614 501 | 0.934783883662 0.0652161163378 502 | 0.999130834881 0.000869165119302 503 | 0.999942762191 5.72378085466e-05 504 | 0.591676283005 0.408323716995 505 | 0.999763867628 0.000236132371506 506 | 0.999403198386 0.000596801614455 507 | 0.917870301011 0.0821296989892 508 | 0.99776023761 0.00223976238989 509 | 0.9439436793 0.0560563207 510 | 0.980050922477 0.0199490775229 511 | 0.840073264569 0.159926735431 512 | 0.905141217386 0.0948587826139 513 | 0.999769060262 0.000230939737528 514 | 0.721182257385 0.278817742615 515 | 0.349494370978 0.650505629022/ 516 | 0.999998846164 1.15383550114e-06 517 | 0.946079320657 0.0539206793431 518 | 0.875439579607 0.124560420393 519 | 0.92870376244 0.0712962375598 520 | 0.447213886143 0.552786113857/ 521 | 0.927914299691 0.0720857003087 522 | 0.997955763317 0.0020442366825 523 | 0.180702242212 0.819297757788/ 524 | 0.980476031644 0.0195239683564 525 | 0.989499509055 0.0105004909449 526 | 0.980184444113 0.0198155558867 527 | 0.995799989106 0.00420001089389 528 | 0.546775438631 0.453224561369 529 | 0.999944971946 5.50280537509e-05 530 | 0.667911623154 0.332088376846 531 | 0.980722092569 0.0192779074309 532 | 0.980967658929 0.0190323410714 533 | 0.995708806336 0.00429119366416 534 | 0.971588314549 0.0284116854509 535 | 0.999982874592 1.71254079061e-05 536 | 0.99939063005 0.000609369949839 537 | 0.970389388804 0.0296106111961 538 | 0.990886083511 0.00911391648887 539 | 0.968212510789 0.0317874892113 540 | 0.52160086445 0.47839913555 541 | 0.98994628927 0.0100537107304 542 | 0.95726201095 0.0427379890505 543 | 0.685958444053 0.314041555947 544 | 0.995814823756 0.00418517624399 545 | 0.972782529332 0.0272174706675 546 | 0.989628139739 0.010371860261 547 | 0.862488772965 0.137511227035 548 | 0.985286621882 0.0147133781179 549 | 0.974116131603 0.0258838683974 550 | 0.995123217816 0.0048767821842 551 | 0.998945470519 0.00105452948086 552 | 0.978414532144 0.0215854678564 553 | 0.953232728939 0.0467672710612 554 | 0.985102166448 0.0148978335519 555 | 0.980674586321 0.019325413679 556 | 0.917081700579 0.0829182994212 557 | 0.957326675727 0.042673324273 558 | 0.424506298078 0.575493701922/ 559 | 0.942680573731 0.0573194262691 560 | 0.84783013731 0.15216986269 561 | 0.916676037067 0.0833239629327 562 | 0.22679851584 0.77320148416/ 563 | 0.928109490518 0.0718905094821 564 | 0.976619159007 0.023380840993 565 | 0.999683035302 0.000316964698365 566 | 0.995188125485 0.00481187451507 567 | 0.821501774397 0.178498225603 568 | 0.763986499716 0.236013500284 569 | 0.995976822412 0.00402317758751 570 | 0.988460736622 0.011539263378 571 | 0.897737950929 0.102262049071 572 | 0.999956987318 4.30126823079e-05 573 | 0.291501923925 0.708498076075/ 574 | 0.994507058673 0.00549294132739 575 | 0.774246636879 0.225753363121 576 | 0.999737641709 0.000262358290876 577 | 0.349494370978 0.650505629022/ 578 | 0.0966305476701 0.90336945233/ 579 | 0.919440352115 0.0805596478852 580 | 0.89927651324 0.10072348676 581 | 0.795994152204 0.204005847796 582 | 0.875316586319 0.124683413681 583 | 0.821816243006 0.178183756994 584 | 0.919956049044 0.0800439509562 585 | 0.184592233913 0.815407766087/ 586 | 0.936750605461 0.0632493945388 587 | 0.838255729331 0.161744270669 588 | 0.970635361595 0.0293646384051 589 | 0.595506326781 0.404493673219 590 | 0.999950279385 4.97206151492e-05 591 | 0.979614765307 0.0203852346929 592 | 0.710752546654 0.289247453346 593 | 0.974205321624 0.0257946783759 594 | 0.407499721754 0.592500278246/ 595 | 0.456343454049 0.543656545951/ 596 | 0.974601698209 0.0253983017909 597 | 0.612856128003 0.387143871997 598 | 0.328631539842 0.671368460158/ 599 | 0.990392297409 0.00960770259112 600 | 0.742655122051 0.257344877949 601 | 0.9697616599 0.0302383400997 602 | 0.875679852566 0.124320147434 603 | 0.997533130901 0.00246686909913 604 | 0.96982406271 0.0301759372899 605 | 0.980237773716 0.0197622262835 606 | 0.906918665882 0.0930813341181 607 | 0.997423525172 0.00257647482807 608 | 0.989720300333 0.0102796996665 609 | 0.980216657603 0.0197833423972 610 | 0.236544302144 0.763455697856/ 611 | 0.926540570763 0.0734594292374 612 | 0.501710693257 0.498289306743 613 | 0.969537135138 0.0304628648619 614 | 0.991635750738 0.00836424926217 615 | 0.586683651034 0.413316348966 616 | 0.722647362933 0.277352637067 617 | 0.999671545987 0.000328454013257 618 | 0.987550254434 0.0124497455661 619 | 0.993845789149 0.00615421085099 620 | 0.0610456312987 0.938954368701/ 621 | 0.329964706376 0.670035293624/ 622 | 0.996771455426 0.00322854457379 623 | 0.999938997426 6.10025739978e-05 624 | 0.999951990128 4.80098723868e-05 625 | 0.848952567663 0.151047432337 626 | 0.976242923921 0.0237570760787 627 | 0.941412558769 0.0585874412313 628 | 0.993075255756 0.00692474424402 629 | 0.999914527515 8.54724848907e-05 630 | 0.302192051068 0.697807948932/ 631 | 0.67146702927 0.32853297073 632 | 0.97589898324 0.02410101676 633 | 0.98060064703 0.0193993529698 634 | 0.349494370978 0.650505629022/ 635 | 0.968644189694 0.0313558103056 636 | 0.908937036858 0.091062963142 637 | 0.998367721346 0.00163227865399 638 | 0.999436593643 0.000563406357264 639 | 0.934277072306 0.065722927694 640 | 0.999969318109 3.06818907264e-05 641 | 0.00580306624862 0.99419693375/1 642 | 0.912537623788 0.0874623762118 643 | 0.901515404916 0.0984845950842 644 | 0.759248594286 0.240751405714 645 | 0.99838815136 0.00161184864003 646 | 0.999763136201 0.000236863798551 647 | 0.999048576708 0.000951423292331 648 | 0.655533246033 0.344466753967 649 | 0.452072549217 0.547927450783/ 650 | 0.970140388599 0.0298596114011 651 | 0.152658449324 0.847341550676/ 652 | 0.886588315893 0.113411684107 653 | 0.430964877655 0.569035122345/ 654 | 0.978453741964 0.0215462580362 655 | 0.990445691376 0.00955430862448 656 | 0.982282901094 0.0177170989058 657 | 0.875679852566 0.124320147434 658 | 0.906884724079 0.0931152759207 659 | 0.999448759805 0.000551240195241 660 | 0.991881781404 0.00811821859642 661 | 0.920409916376 0.079590083624 662 | 0.966761504832 0.0332384951683 663 | 0.914351278419 0.0856487215806 664 | 0.99799611895 0.00200388104973 665 | 0.981521567053 0.0184784329472 666 | 0.957325565366 0.0426744346336 667 | 0.988232274273 0.0117677257272 668 | 0.277482844613 0.722517155387/ 669 | 0.989103262438 0.0108967375616 670 | 0.299561203275 0.700438796725/ 671 | 0.646492294427 0.353507705573 672 | 0.990071595808 0.00992840419197 673 | 0.718558363036 0.281441636964 674 | 0.517273570681 0.482726429319 675 | 0.473623599847 0.526376400153/ 676 | 0.691128449071 0.308871550929 677 | 0.855428834328 0.144571165672 678 | 0.987238664461 0.0127613355395 679 | 0.999753396913 0.000246603086899 680 | 0.995036869428 0.00496313057196 681 | 0.806002057687 0.193997942313 682 | 0.526703641806 0.473296358194 683 | 0.865808255745 0.134191744255 684 | 0.263723102858 0.736276897142/ 685 | 0.86863970491 0.13136029509 686 | 0.712985680289 0.287014319711 687 | 0.986341986021 0.0136580139792 688 | 0.994349642484 0.00565035751591 689 | 0.949330646874 0.0506693531264 690 | 0.978257238927 0.0217427610725 691 | 0.757208751417 0.242791248583 692 | 0.958973926 0.0410260739997 693 | 0.998356334961 0.00164366503938 694 | 0.488301952914 0.511698047086/ 695 | 0.879256620565 0.120743379435 696 | 0.999962383892 3.76161084125e-05 697 | 0.95956718884 0.0404328111604 698 | 0.844941429605 0.155058570395 699 | 0.719847604258 0.280152395742 700 | 0.848216297132 0.151783702868 701 | 0.959936147631 0.0400638523694 702 | 0.336977314404 0.663022685596/ 703 | 0.8578908576 0.1421091424 704 | 0.750956524503 0.249043475497 705 | 0.91492492263 0.08507507737 706 | 0.875803582058 0.124196417942 707 | 0.926986553079 0.0730134469208 708 | 0.97504255752 0.0249574424804 709 | 0.32247581446 0.67752418554/ 710 | 0.8700528481 0.1299471519 711 | 0.884771350121 0.115228649879 712 | 0.97756845878 0.0224315412196 713 | 0.9994879988 0.000512001199714 714 | 0.999996279394 3.72060638476e-06 715 | 0.998176192066 0.0018238079344 716 | 0.941192107307 0.0588078926928 717 | 0.730037926882 0.269962073118 718 | 0.999097472399 0.000902527600614 719 | 0.544872538562 0.455127461438 720 | 0.161080824093 0.838919175907/ 721 | 0.855455355374 0.144544644626 722 | 0.158265379683 0.841734620317/ 723 | 0.999951431382 4.85686184445e-05 724 | 0.392654874374 0.607345125626/ 725 | 0.401017854513 0.598982145487/ 726 | 0.832291995838 0.167708004162 727 | 0.93464934682 0.0653506531802 728 | 0.387248190693 0.612751809307/ 729 | 0.992249461126 0.00775053887438 730 | 0.48201442421 0.51798557579/ 731 | 0.997723453846 0.00227654615412 732 | 0.993170099798 0.00682990020172 733 | 0.842388781667 0.157611218333 734 | 0.896208932615 0.103791067385 735 | 0.869126857323 0.130873142677 736 | 0.89292005722 0.10707994278 737 | 0.995289517417 0.004710482583 738 | 0.383356144746 0.616643855254/ 739 | 0.999896868672 0.000103131328022 740 | 0.745937011655 0.254062988345 741 | 0.135824263061 0.864175736939/ 742 | 0.715955401307 0.284044598693 743 | 0.971462118565 0.028537881435 744 | 0.927641162463 0.0723588375369 745 | 0.877632710199 0.122367289801 746 | 0.669299685553 0.330700314447 747 | 0.994329417445 0.0056705825548 748 | 0.501798148883 0.498201851117 749 | 0.85611856012 0.14388143988 750 | 0.867904838041 0.132095161959 751 | 0.997190390464 0.00280960953575 752 | 0.892441635085 0.107558364915 753 | 0.763303658464 0.236696341536 754 | 0.8666268834 0.1333731166 755 | 0.993967752668 0.006032247332 756 | 0.75387032159 0.24612967841 757 | 0.915578823395 0.0844211766051 758 | 0.99992541442 7.45855795158e-05 759 | 0.998931259033 0.00106874096689 760 | 0.999995117137 4.88286342071e-06 761 | 0.745937011655 0.254062988345 762 | 0.999658132312 0.000341867687601 763 | 0.989994727249 0.0100052727514 764 | 0.957422349226 0.0425776507737 765 | 0.989600482351 0.0103995176486 766 | 0.999335091108 0.000664908892415 767 | 0.995710126897 0.00428987310316 768 | 0.688961373927 0.311038626073 769 | 0.997144466364 0.00285553363626 770 | 0.960526871159 0.0394731288406 771 | 0.96250972721 0.0374902727896 772 | 0.780668046362 0.219331953638 773 | 0.987217551851 0.0127824481494 774 | 0.945817121539 0.0541828784609 775 | 0.999065593413 0.000934406586611 776 | 0.980351167066 0.0196488329343 777 | 0.954848739051 0.0451512609488 778 | 0.954668292161 0.0453317078388 779 | 0.982874052751 0.0171259472487 780 | 0.963064658839 0.0369353411613 781 | 0.828267475985 0.171732524015 782 | 0.935963251555 0.0640367484449 783 | 0.828267475985 0.171732524015 784 | 0.999624225139 0.000375774861078 785 | 0.952252005776 0.0477479942239 786 | 0.925067173943 0.0749328260571 787 | 0.934783883662 0.0652161163378 788 | 0.583584997264 0.416415002736 789 | 0.846787978031 0.153212021969 790 | 0.882289555797 0.117710444203 791 | 0.591676283005 0.408323716995 792 | 0.747985059153 0.252014940847 793 | 0.503527386111 0.496472613889 794 | 0.799090578898 0.200909421102 795 | 0.631537103625 0.368462896375 796 | 0.999997557614 2.44238643687e-06 797 | 0.984170046349 0.0158299536508 798 | 0.829387266665 0.170612733335 799 | 0.952765521482 0.0472344785179 800 | 0.484758290102 0.515241709898/ 801 | 0.975208087739 0.0247919122606 802 | 0.461469780252 0.538530219748/ 803 | 0.520442858133 0.479557141867 804 | 0.829218806831 0.170781193169 805 | 0.846208010831 0.153791989169 806 | 0.994413482619 0.00558651738141 807 | 0.967497204936 0.0325027950637 808 | 0.999643679118 0.000356320881648 809 | 0.91275091542 0.0872490845803 810 | 0.999442961822 0.00055703817802 811 | 0.804256411124 0.195743588876 812 | 0.926443510489 0.0735564895111 813 | 0.400653301075 0.599346698925/ 814 | 0.993398237997 0.00660176200278 815 | 0.846263031074 0.153736968926 816 | 0.200988155544 0.799011844456/ 817 | 0.950354089949 0.0496459100507 818 | 0.95608414919 0.0439158508102 819 | 0.711741454272 0.288258545728 820 | 0.999554793838 0.000445206161838 821 | 0.858526391889 0.141473608111 822 | 0.770299580126 0.229700419874 823 | 0.941139341733 0.0588606582669 824 | 0.606659891496 0.393340108504 825 | 0.875679852566 0.124320147434 826 | 0.999598360207 0.000401639793434 827 | 0.997917666972 0.00208233302846 828 | 0.719922404206 0.280077595794 829 | 0.998953693957 0.00104630604318 830 | 0.833401667991 0.166598332009 831 | 0.763986499716 0.236013500284 832 | 0.484256321383 0.515743678617/ 833 | 0.996551648401 0.00344835159917 834 | 0.946539767061 0.0534602329394 835 | 0.616603528648 0.383396471352 836 | 0.997121713353 0.00287828664738 837 | 0.5495268766 0.4504731234 838 | 0.546913160968 0.453086839032 839 | 0.986952336347 0.0130476636528 840 | 0.992476125783 0.00752387421689 841 | 0.989519678655 0.0104803213454 842 | 0.148842462999 0.851157537001/ 843 | 0.746470009752 0.253529990248 844 | 0.999606794271 0.000393205729221 845 | 0.349494370978 0.650505629022/ 846 | 0.745937011655 0.254062988345 847 | 0.701544602828 0.298455397172 848 | 0.746698860698 0.253301139302 849 | 0.952310808119 0.0476891918808 850 | 0.993449309211 0.0065506907887 851 | 0.934891734008 0.0651082659923 852 | 0.997392021407 0.00260797859312 853 | 0.998975768033 0.00102423196682 854 | 0.916024026435 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/storesentimentclassifier.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | import pickle 3 | import itertools 4 | import nltk 5 | from nltk.collocations import BigramCollocationFinder 6 | from nltk.metrics import BigramAssocMeasures 7 | from nltk.probability import FreqDist, ConditionalFreqDist 8 | import sklearn 9 | from sklearn.svm import SVC, LinearSVC, NuSVC 10 | from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB 11 | from sklearn.linear_model import LogisticRegression 12 | from nltk.classify.scikitlearn import SklearnClassifier 13 | from sklearn.metrics import accuracy_score 14 | 15 | # Feature extraction function 16 | # 1 Use all words as features 17 | def bag_of_words(words): 18 | return dict([(word, True) for word in words]) 19 | 20 | # 2 Use bigrams as features (use chi square chose top 200 bigrams) 21 | def bigrams(words, score_fn=BigramAssocMeasures.chi_sq, n=200): 22 | bigram_finder = BigramCollocationFinder.from_words(words) 23 | bigrams = bigram_finder.nbest(score_fn, n) 24 | return bag_of_words(bigrams) 25 | 26 | # 3 Use words and bigrams as features (use chi square chose top 200 bigrams) 27 | def bigram_words(words, score_fn=BigramAssocMeasures.chi_sq, n=200): 28 | bigram_finder = BigramCollocationFinder.from_words(words) 29 | bigrams = bigram_finder.nbest(score_fn, n) 30 | return bag_of_words(words + bigrams) 31 | 32 | 33 | # 4 Use chi_sq to find most informative features of the review 34 | # 4.1 First we should compute words or bigrams information score 35 | def create_word_scores(posdata,negdata): 36 | 37 | posWords = list(itertools.chain(*posdata)) 38 | negWords = list(itertools.chain(*negdata)) 39 | 40 | word_fd = FreqDist() 41 | cond_word_fd = ConditionalFreqDist() 42 | for word in posWords: 43 | word_fd[word] += 1 44 | cond_word_fd['pos'][word] += 1 45 | for word in negWords: 46 | word_fd[word] += 1 47 | cond_word_fd['neg'][word] += 1 48 | 49 | pos_word_count = cond_word_fd['pos'].N() 50 | neg_word_count = cond_word_fd['neg'].N() 51 | total_word_count = pos_word_count + neg_word_count 52 | 53 | word_scores = {} 54 | for word, freq in word_fd.iteritems(): 55 | pos_score = BigramAssocMeasures.chi_sq(cond_word_fd['pos'][word], (freq, pos_word_count), total_word_count) 56 | neg_score = BigramAssocMeasures.chi_sq(cond_word_fd['neg'][word], (freq, neg_word_count), total_word_count) 57 | word_scores[word] = pos_score + neg_score 58 | 59 | return word_scores 60 | #4.2 61 | def create_bigram_scores(posdata,negdata): 62 | 63 | posWords = list(itertools.chain(*posdata)) 64 | negWords = list(itertools.chain(*negdata)) 65 | 66 | bigram_finder_pos = BigramCollocationFinder.from_words(posWords) 67 | bigram_finder_neg = BigramCollocationFinder.from_words(negWords) 68 | posBigrams = bigram_finder_pos.nbest(BigramAssocMeasures.chi_sq, 8000) 69 | negBigrams = bigram_finder_neg.nbest(BigramAssocMeasures.chi_sq, 8000) 70 | 71 | pos = posBigrams 72 | neg = negBigrams 73 | 74 | word_fd = FreqDist() 75 | cond_word_fd = ConditionalFreqDist() 76 | for word in pos: 77 | word_fd[word] += 1 78 | cond_word_fd['pos'][word] += 1 79 | 80 | for word in neg: 81 | word_fd[word] += 1 82 | cond_word_fd['neg'][word] += 1 83 | 84 | pos_word_count = cond_word_fd['pos'].N() 85 | neg_word_count = cond_word_fd['neg'].N() 86 | total_word_count = pos_word_count + neg_word_count 87 | 88 | word_scores = {} 89 | for word, freq in word_fd.iteritems(): 90 | pos_score = BigramAssocMeasures.chi_sq(cond_word_fd['pos'][word], (freq, pos_word_count), total_word_count) 91 | neg_score = BigramAssocMeasures.chi_sq(cond_word_fd['neg'][word], (freq, neg_word_count), total_word_count) 92 | word_scores[word] = pos_score + neg_score 93 | 94 | return word_scores 95 | 96 | # 4.3Combine words and bigrams and compute words and bigrams information scores 97 | def create_word_bigram_scores(posdata,negdata): 98 | 99 | posWords = list(itertools.chain(*posdata)) 100 | negWords = list(itertools.chain(*negdata)) 101 | 102 | bigram_finder_pos = BigramCollocationFinder.from_words(posWords) 103 | bigram_finder_neg = BigramCollocationFinder.from_words(negWords) 104 | posBigrams = bigram_finder_pos.nbest(BigramAssocMeasures.chi_sq, 5000) 105 | negBigrams = bigram_finder_neg.nbest(BigramAssocMeasures.chi_sq, 5000) 106 | 107 | pos = posWords + posBigrams 108 | neg = negWords + negBigrams 109 | 110 | word_fd = FreqDist() 111 | cond_word_fd = ConditionalFreqDist() 112 | for word in pos: 113 | word_fd[word] += 1 114 | cond_word_fd['pos'][word] += 1 115 | for word in neg: 116 | word_fd[word] += 1 117 | cond_word_fd['neg'][word] += 1 118 | 119 | 120 | pos_word_count = cond_word_fd['pos'].N() 121 | neg_word_count = cond_word_fd['neg'].N() 122 | total_word_count = pos_word_count + neg_word_count 123 | 124 | word_scores = {} 125 | for word, freq in word_fd.iteritems(): 126 | pos_score = BigramAssocMeasures.chi_sq(cond_word_fd['pos'][word], (freq, pos_word_count), total_word_count) 127 | neg_score = BigramAssocMeasures.chi_sq(cond_word_fd['neg'][word], (freq, neg_word_count), total_word_count) 128 | word_scores[word] = pos_score + neg_score 129 | 130 | return word_scores 131 | 132 | # 5 Second we should extact the most informative words or bigrams based on the information score 133 | def find_best_words(word_scores, number): 134 | best_vals = sorted(word_scores.iteritems(), key=lambda (w, s): s, reverse=True)[:number] 135 | best_words = set([w for w, s in best_vals]) 136 | return best_words 137 | 138 | # 6 Third we could use the most informative words and bigrams as machine learning features 139 | # Use chi_sq to find most informative words of the review 140 | def best_word_features(words,best_words): 141 | return dict([(word, True) for word in words if word in best_words]) 142 | 143 | # Use chi_sq to find most informative bigrams of the review 144 | def best_word_features_bi(words,best_words): 145 | return dict([(word, True) for word in nltk.bigrams(words) if word in best_words]) 146 | 147 | # Use chi_sq to find most informative words and bigrams of the review 148 | def best_word_features_com(words,best_words): 149 | d1 = dict([(word, True) for word in words if word in best_words]) 150 | d2 = dict([(word, True) for word in nltk.bigrams(words) if word in best_words]) 151 | d3 = dict(d1, **d2) 152 | return d3 153 | 154 | #7 Transform review to features by setting labels to words in review 155 | def pos_features(pos,feature_extraction_method,best_words): 156 | posFeatures = [] 157 | for i in pos: 158 | posWords = [feature_extraction_method(i,best_words),'pos'] 159 | posFeatures.append(posWords) 160 | return posFeatures 161 | 162 | def neg_features(neg,feature_extraction_method,best_words): 163 | negFeatures = [] 164 | for j in neg: 165 | negWords = [feature_extraction_method(j,best_words),'neg'] 166 | negFeatures.append(negWords) 167 | return negFeatures 168 | 169 | def clf_score(classifier,train_set,test,tag_test): 170 | classifier = SklearnClassifier(classifier) 171 | classifier.train(train_set) 172 | predict = classifier.classify_many(test) 173 | return accuracy_score(tag_test, predict) 174 | 175 | 176 | def cal_classifier_accuracy(train_set,test,tag_test): 177 | classifierlist = [] 178 | print '各个分类器准度：' 179 | print 'BernoulliNB`s accuracy is %f' %clf_score(BernoulliNB(),train_set,test,tag_test) 180 | print 'MultinomiaNB`s accuracy is %f' %clf_score(MultinomialNB(),train_set,test,tag_test) 181 | print 'LogisticRegression`s accuracy is %f' %clf_score(LogisticRegression(),train_set,test,tag_test) 182 | print 'SVC`s accuracy is %f' %clf_score(SVC(gamma=0.001, C=100., kernel='linear'),train_set,test,tag_test) 183 | print 'LinearSVC`s accuracy is %f' %clf_score(LinearSVC(),train_set,test,tag_test) 184 | print 'NuSVC`s accuracy is %f' %clf_score(NuSVC(),train_set,test,tag_test) 185 | # print 'GaussianNB`s accuracy is %f' %clf_score(GaussianNB()) 186 | classifierlist.append([BernoulliNB(),clf_score(BernoulliNB(),train_set,test,tag_test)]) 187 | classifierlist.append([MultinomialNB(),clf_score(MultinomialNB(),train_set,test,tag_test)]) 188 | classifierlist.append([LogisticRegression(),clf_score(LogisticRegression(),train_set,test,tag_test)]) 189 | classifierlist.append([SVC(gamma=0.001, C=100., kernel='linear'),clf_score(SVC(gamma=0.001, C=100., kernel='linear'),train_set,test,tag_test)]) 190 | classifierlist.append([LinearSVC(),clf_score(LinearSVC(),train_set,test,tag_test)]) 191 | classifierlist.append([NuSVC(),clf_score(NuSVC(),train_set,test,tag_test)]) 192 | return classifierlist 193 | 194 | def find_score_max(classifier): 195 | max = 0 196 | for cla in classifier: 197 | if cla[1] > max: 198 | max = cla[1] 199 | object = cla[0] 200 | return object 201 | 202 | def store_classifier(object,train_set,path): 203 | object_classifier = SklearnClassifier(object) 204 | object_classifier.train(train_set) 205 | pickle.dump(object_classifier, open(path+'/classifier.pkl','w')) 206 | -------------------------------------------------------------------------------- /storesentimentclassifier.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/life-is-good/MoiveDataAnalysisByML/e80ea309a85086aae40aa2ac8c1f109193000db3/storesentimentclassifier.pyc -------------------------------------------------------------------------------- /svmmain.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | import storesentimentclassifier as ssc # 分类器类 3 | import posnegmlfeature as pnf # 预测类 4 | import textprocessing as tp # 处理excel或者txt类 5 | import pickle 6 | from random import shuffle 7 | from nltk.classify.scikitlearn import SklearnClassifier 8 | import os 9 | import time 10 | 11 | if __name__ == "__main__": 12 | #三部电影《长城》《乘风破浪》《西游伏魔篇》，分别跑一遍，然后将三部电影合起来再跑一遍 13 | #使用MutiNB,LogisticRegression,SVM三种分类器 14 | 15 | print '开始训练分类器' 16 | # 1. Load positive and negative review data 17 | path = os.getcwd() 18 | print '当前路径'+path 19 | start_time = time.time() 20 | pos_review = tp.seg_fil_senti_excel(path+"\\seniment review set\\THREEMIXPOS.xls", 1, 1) 21 | neg_review = tp.seg_fil_senti_excel(path+"\\seniment review set\\THREEMIXNEG.xls", 1, 1) 22 | test_review = test_review = tp.seg_fil_senti_excel(path+"\\seniment review set\\THREEMIXTEST.xls", 1, 1) 23 | 24 | pos = pos_review 25 | neg = neg_review 26 | 27 | # 2. Feature extraction function 28 | # Choose word_scores extaction methods 29 | #word_scores = create_word_scores() 30 | #word_scores = create_bigram_scores() 31 | word_scores = ssc.create_word_bigram_scores(pos,neg) 32 | 33 | # 3. Transform review to features by setting labels to words in review 34 | best_words = ssc.find_best_words(word_scores, 1500) # Set dimension and initiallize most informative words 35 | 36 | # posFeatures = ssc.pos_features(ssc.bigrams) 37 | # negFeatures = ssc.neg_features(ssc.bigrams) 38 | 39 | # posFeatures = ssc.pos_features(ssc.bigram_words) 40 | # negFeatures = ssc.neg_features(ssc.bigram_words) 41 | 42 | # posFeatures = ssc.pos_features(ssc.best_word_features) 43 | # negFeatures = ssc.neg_features(ssc.best_word_features) 44 | 45 | posFeatures = ssc.pos_features(pos,ssc.best_word_features_com,best_words) 46 | negFeatures = ssc.neg_features(neg,ssc.best_word_features_com,best_words) 47 | 48 | # 4. Train classifier and examing classify accuracy 49 | # Make the feature set ramdon 50 | shuffle(posFeatures) 51 | shuffle(negFeatures) 52 | 53 | # 5. After finding the best classifier,store it and then check different dimension classification accuracy 54 | # 75% of features used as training set (in fact, it have a better way by using cross validation function) 55 | size_pos = int(len(pos_review) * 0.75) 56 | size_neg = int(len(neg_review) * 0.75) 57 | 58 | train_set = posFeatures[:size_pos] + negFeatures[:size_neg] 59 | test_set = posFeatures[size_pos:] + negFeatures[size_neg:] 60 | 61 | test, tag_test = zip(*test_set) 62 | 63 | classifier = [] 64 | classifier = ssc.cal_classifier_accuracy(train_set,test,tag_test) 65 | #选择svm分类器 66 | object = classifier[4][0] #svm 67 | print '选择的分类器是：' 68 | print object 69 | print '存储分类器' 70 | object_classifier = SklearnClassifier(object) 71 | object_classifier.train(train_set) 72 | print "开始预测" 73 | predict = object_classifier.classify_many(pnf.extract_features(test_review,best_words)) 74 | print "存储预测结果" 75 | p_file = open(path+'/result/great_SVMfinal.txt', 'w') 76 | for pre in predict: 77 | p_file.write(pre + '\n') 78 | p_file.close() 79 | print '结束预测' 80 | end_time = time.time() 81 | print end_time - start_time 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | -------------------------------------------------------------------------------- /textprocessing.py: -------------------------------------------------------------------------------- 1 | #coding=utf-8 2 | 3 | """ 4 | Read data from excel file and txt file. 5 | Chinese word segmentation, postagger, sentence cutting and stopwords filtering function. 6 | 7 | """ 8 | 9 | import xlrd 10 | import jieba 11 | import jieba.posseg 12 | import os 13 | 14 | 15 | path = os.getcwd() 16 | jieba.load_userdict(path+'/dict/userdict.txt') #使用自定义词典，自己抓的所有的电影明星和电影名称 17 | 18 | """ 19 | input: An excel file with product review 20 | 手机很好，很喜欢。 21 | 三防出色，操作系统垃圾！ 22 | Defy用过3年感受。。。 23 | 刚买很兴奋。当时还流行，机还很贵 24 | …… 25 | output: 26 | parameter_1: Every cell is a value of the data list. (unicode) 27 | parameter_2: Excel row number. (int) 28 | """ 29 | def get_excel_data(filepath, sheetnum, colnum, para): 30 | table = xlrd.open_workbook(filepath) 31 | sheet = table.sheets()[sheetnum-1] 32 | data = sheet.col_values(colnum-1) 33 | rownum = sheet.nrows 34 | if para == 'data': 35 | return data 36 | elif para == 'rownum': 37 | return rownum 38 | 39 | 40 | """ 41 | input: 42 | parameter_1: A txt file with many lines 43 | parameter_2: A txt file with only one line of data 44 | output: 45 | parameter_1: Every line is a value of the txt_data list. (unicode) 46 | parameter_2: Txt data is a string. (str) 47 | """ 48 | 49 | def get_txt_data(filepath, para): 50 | if para == 'lines': 51 | txt_file1 = open(filepath, 'r') 52 | txt_tmp1 = txt_file1.readlines() 53 | txt_tmp2 = ''.join(txt_tmp1) 54 | txt_data1 = txt_tmp2.decode('utf8').split('\n') 55 | txt_file1.close() 56 | return txt_data1 57 | elif para == 'line': 58 | txt_file2 = open(filepath, 'r') 59 | txt_tmp = txt_file2.readline() 60 | txt_data2 = txt_tmp.decode('utf8') 61 | txt_file2.close() 62 | return txt_data2 63 | 64 | 65 | """ 66 | input: 这款手机大小合适。 67 | output: 68 | parameter_1: 这 款 手机 大小 合适 。(unicode) 69 | parameter_2: [u'\u8fd9', u'\u6b3e', u'\u624b\u673a', u'\u5927\u5c0f', u'\u5408\u9002', u'\uff0c'] 70 | """ 71 | 72 | def segmentation(sentence, para): 73 | if para == 'str': 74 | seg_list = jieba.cut(sentence) 75 | seg_result = ' '.join(seg_list) 76 | return seg_result 77 | elif para == 'list': 78 | seg_list2 = jieba.cut(sentence) 79 | seg_result2 = [] 80 | for w in seg_list2: 81 | seg_result2.append(w) 82 | return seg_result2 83 | 84 | 85 | """ 86 | input: '这款手机大小合适。' 87 | output: 88 | parameter_1: 这 r 款 m 手机 n 大小 b 合适 a 。 x 89 | parameter_2: [(u'\u8fd9', ['r']), (u'\u6b3e', ['m']), 90 | (u'\u624b\u673a', ['n']), (u'\u5927\u5c0f', ['b']), 91 | (u'\u5408\u9002', ['a']), (u'\u3002', ['x'])] 92 | """ 93 | 94 | def postagger(sentence, para): 95 | if para == 'list': 96 | pos_data1 = jieba.posseg.cut(sentence) 97 | pos_list = [] 98 | for w in pos_data1: 99 | pos_list.append((w.word, w.flag)) #make every word and tag as a tuple and add them to a list 100 | return pos_list 101 | elif para == 'str': 102 | pos_data2 = jieba.posseg.cut(sentence) 103 | pos_list2 = [] 104 | for w2 in pos_data2: 105 | pos_list2.extend([w2.word.encode('utf8'), w2.flag]) 106 | pos_str = ' '.join(pos_list2) 107 | return pos_str 108 | 109 | 110 | """ 111 | input: A review like this 112 | '这款手机大小合适，配置也还可以，很好用，只是屏幕有点小。。。总之，戴妃+是一款值得购买的智能手机。' 113 | output: A multidimentional list 114 | [u'\u8fd9\u6b3e\u624b\u673a\u5927\u5c0f\u5408\u9002\uff0c', 115 | u'\u914d\u7f6e\u4e5f\u8fd8\u53ef\u4ee5\uff0c', u'\u5f88\u597d\u7528\uff0c', 116 | u'\u53ea\u662f\u5c4f\u5e55\u6709\u70b9\u5c0f\u3002', u'\u603b\u4e4b\uff0c', 117 | u'\u6234\u5983+\u662f\u4e00\u6b3e\u503c\u5f97\u8d2d\u4e70\u7684\u667a\u80fd\u624b\u673a\u3002'] 118 | """ 119 | 120 | """ Maybe this algorithm will have bugs in it """ 121 | # def cut_sentences_1(words): 122 | # #words = (words).decode('utf8') 123 | # start = 0 124 | # i = 0 #i is the position of words 125 | # sents = [] 126 | # punt_list = ',.!?:;~，。！？：；～ '.decode('utf8') # Sentence cutting punctuations 127 | # for word in words: 128 | # if word in punt_list and token not in punt_list: 129 | # sents.append(words[start:i+1]) 130 | # start = i+1 131 | # i += 1 132 | # else: 133 | # i += 1 134 | # token = list(words[start:i+2]).pop() 135 | # # if there is no punctuations in the end of a sentence, it can still be cutted 136 | # if start < len(words): 137 | # sents.append(words[start:]) 138 | # return sents 139 | 140 | """ Sentence cutting algorithm without bug, but a little difficult to explain why""" 141 | def cut_sentence_2(words): 142 | #words = (words).decode('utf8') 143 | start = 0 144 | i = 0 #i is the position of words 145 | token = 'meaningless' 146 | sents = [] 147 | punt_list = ',.!?;~，。！？；～… '.decode('utf8') 148 | for word in words: 149 | if word not in punt_list: 150 | i += 1 151 | token = list(words[start:i+2]).pop() 152 | #print token 153 | elif word in punt_list and token in punt_list: 154 | i += 1 155 | token = list(words[start:i+2]).pop() 156 | else: 157 | sents.append(words[start:i+1]) 158 | start = i+1 159 | i += 1 160 | if start < len(words): 161 | sents.append(words[start:]) 162 | return sents 163 | 164 | 165 | """ 166 | input: An excel file with product reviews 167 | 手机很好，很喜欢。 168 | 三防出色，操作系统垃圾！ 169 | Defy用过3年感受。。。 170 | 刚买很兴奋。当时还流行，机还很贵 171 | output: A multidimentional list of reviews 172 | 173 | """ 174 | 175 | def seg_fil_excel(filepath, sheetnum, colnum): 176 | # Read product review data from excel file and segment every review 177 | review_data = [] 178 | for cell in get_excel_data(filepath, sheetnum, colnum, 'data')[0:get_excel_data(filepath, sheetnum, colnum, 'rownum')]: 179 | review_data.append(segmentation(cell, 'list')) # Seg every reivew 180 | 181 | # Read txt file contain stopwords 182 | stopwords = get_txt_data(path+'/dict/stopword.txt', 'lines') 183 | 184 | # Filter stopwords from reviews 185 | seg_fil_result = [] 186 | for review in review_data: 187 | fil = [word for word in review if word not in stopwords and word != ' '] 188 | seg_fil_result.append(fil) 189 | fil = [] 190 | 191 | # Return filtered segment reviews 192 | return seg_fil_result 193 | 194 | 195 | """ 196 | input: An excel file with product reviews 197 | 手机很好，很喜欢。 198 | 三防出色，操作系统垃圾！ 199 | Defy用过3年感受。。。 200 | 刚买很兴奋。当时还流行，机还很贵 201 | output: A multidimentional list of reviews, use different stopword list, so it will remain sentiment tokens. 202 | 203 | """ 204 | 205 | def seg_fil_senti_excel(filepath, sheetnum, colnum): 206 | # Read product review data from excel file and segment every review 207 | review_data = [] 208 | for cell in get_excel_data(filepath, sheetnum, colnum, 'data')[0:get_excel_data(filepath, sheetnum, colnum, 'rownum')]: 209 | review_data.append(segmentation(cell, 'list')) # Seg every reivew 210 | 211 | # Read txt file contain sentiment stopwords 212 | sentiment_stopwords = get_txt_data(path+'/dict/sentiment_stopword.txt', 'lines') 213 | 214 | # Filter stopwords from reviews 215 | seg_fil_senti_result = [] 216 | for review in review_data: 217 | fil = [word for word in review if word not in sentiment_stopwords and word != ' '] 218 | seg_fil_senti_result.append(fil) 219 | fil = [] 220 | 221 | # Return filtered segment reviews 222 | return seg_fil_senti_result 223 | -------------------------------------------------------------------------------- /textprocessing.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/life-is-good/MoiveDataAnalysisByML/e80ea309a85086aae40aa2ac8c1f109193000db3/textprocessing.pyc --------------------------------------------------------------------------------