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1.78712965201534 0.243156456792515 1 3.07790563815491 1.88920490138202 1 9.43195911269671 0.268801081716839 1 6.34282338934720 0.234109280621259 1 4.82412559000009 1.99459231605986 1 8.56305479341248 4.72713371076877 1 3.29684212405129 2.07793293965495 1 7.90570542236263 3.88560220548909 1 9.07438180273557 2.97201368564586 1 4.03957906251732 1.88105978289717 1 1.78205895950614 0.293661208884570 1 2.68642960682293 1.36689620711673 1 7.50923375595256 4.39161107527268 1 9.22583275513275 6.75308704026472 1 5.93852172849132 0.0767525003841111 1 1.85217721700788 0.438665288257335 1 9.57234374408993 7.22701037061452 1 9.41511021370755 8.19137839466097 1 4.35273751059844 1.07589578304157 1 8.04923110762197 1.49599991038707 1 9.63236985254770 8.03011417611781 1 4.29156538673758 1.13530168347901 1 7.23413465556835 4.46740498567466 1 8.20309146793921 4.27626275005668 1 1.87457510864822 0.489332789968566 1 1.38780964339963 0.293816887566648 1 6.93682776203853 3.55888673905837 1 3.04295274833797 1.45808471523332 1 7.52052171782518 2.24448074758751 1 1.29475743274558 0.248662041648605 1 7.93270103161629 3.53322776478622 1 7.36266950886594 5.03397809680607 1 3.97602116402406 2.71911655949805 1 6.19034466536054 1.49434836516839 1 3.40178374234313 2.13801547822256 1 4.29491070615890 0.494287306651284 1 9.51694812792873 5.54313061426752 1 9.83993111985939 1.71347650375439 1 8.45868335411795 3.68311342408982 1 6.31700682494856 0.238683478937646 1 6.08012336783280 3.21848321902105 1 5.48289786541560 0.267286639106428 1 5.63646865791522 0.246636667516577 1 8.59268728497635 4.51516446747963 1 7.21916947499839 4.02521298309224 1 6.61095852079607 5.35205946687567 1 2.59249592920151 0.756284591960114 1 8.58163248547907 3.76779427699367 1 7.86164254606212 1.23425344739733 1 7.22459851512079 5.69192759004084 1 2.46391682231140 0.626697111247094 1 1.37666679614458 0.0850311192606557 1 6.99535329428774 5.22402041296117 1 4.10255509353404 0.0845494795236941 1 9.14754400713588 3.83800038341603 1 9.19417616894172 2.68752405465039 1 7.21993188858784 0.108525252131188 1 5.61112164928016 1.53003323224417 1 4.02501202704844 2.88388192100925 1 7.75454869096328 2.25550746659844 1 6.69549473855791 2.36673839888467 1 6.18849817990230 4.97836674652459 1 6.54263406218390 0.834598177291021 1 8.54443627754918 5.47850873429622 1 1.97961580331274 0.158062518938500 1 3.67309260569741 1.13227508530645 1 5.75572976460165 1.30828876313430 1 8.45160472860189 5.90751849062474 1 3.46373193769655 1.51690595784220 1 1.91395651414783 0.612317246772413 1 4.12656938065540 2.09322445393649 1 3.65772017039046 1.80838713249693 1 4.54395760030265 1.38858754767689 1 2.86266970800483 1.34954202841685 1 8.37374823952218 6.83156860507414 1 5.62707992632729 0.893689431466388 1 6.42610816769460 1.07534713660481 1 4.69353317658611 3.15086012845143 1 4.86173611501328 3.57464916591233 1 4.35769946480643 2.34450146003832 1 6.29762879066540 0.534351386077976 1 4.71155500188011 3.43210544532588 1 1.49995426186507 0.192006207383273 1 3.16576104712266 0.493379007110222 1 4.21408348419092 2.97014277918461 1 5.52248511695330 3.63263027130760 1 4.15244831176753 1.44597290703838 1 9.55986996363196 1.13832040773527 1 1.63276516895206 0.446783742774178 1 9.38532498107474 0.913169554364942 1
转自：
https://github.com/zhaozhiyong19890102/Python-Machine-Learning-Algorithm