在约会网站上使用k近邻算法
思路步骤：
1. 收集数据：提供文本文件。
2. 准备数据：使用Python解析文本文件。
3. 分析数据：使用Matplotlib画二维扩散图。
4. 训练算法：此步骤不适用于k近邻算法。
5. 测试算法：使用海伦提供的部分数据作为测试样本。
测试样本和非测试样本的区别在于：测试样本是已经完成分类的数据，如果预测分类与实际类别不同，则标记为一个错误。
6. 使用算法：产生简单的命令行程序，然后海伦可以输入一些特征数据以判断对方是否为自己喜欢的类型。
正式开始：
第1步.收集数据，提供文本文件datingTestSet.txt：
1 40920 8.326976 0.953952 largeDoses 2 14488 7.153469 1.673904 smallDoses 3 26052 1.441871 0.805124 didntLike 4 75136 13.147394 0.428964 didntLike 5 38344 1.669788 0.134296 didntLike 6 72993 10.141740 1.032955 didntLike 7 35948 6.830792 1.213192 largeDoses 8 42666 13.276369 0.543880 largeDoses 9 67497 8.631577 0.749278 didntLike 10 35483 12.273169 1.508053 largeDoses 11 50242 3.723498 0.831917 didntLike 12 63275 8.385879 1.669485 didntLike 13 5569 4.875435 0.728658 smallDoses 14 51052 4.680098 0.625224 didntLike 15 77372 15.299570 0.331351 didntLike 16 43673 1.889461 0.191283 didntLike 17 61364 7.516754 1.269164 didntLike 18 69673 14.239195 0.261333 didntLike 19 15669 0.000000 1.250185 smallDoses 20 28488 10.528555 1.304844 largeDoses 21 6487 3.540265 0.822483 smallDoses 22 37708 2.991551 0.833920 didntLike 23 22620 5.297865 0.638306 smallDoses 24 28782 6.593803 0.187108 largeDoses 25 19739 2.816760 1.686209 smallDoses 26 36788 12.458258 0.649617 largeDoses 27 5741 0.000000 1.656418 smallDoses 28 28567 9.968648 0.731232 largeDoses 29 6808 1.364838 0.640103 smallDoses 30 41611 0.230453 1.151996 didntLike 31 36661 11.865402 0.882810 largeDoses 32 43605 0.120460 1.352013 didntLike 33 15360 8.545204 1.340429 largeDoses 34 63796 5.856649 0.160006 didntLike 35 10743 9.665618 0.778626 smallDoses 36 70808 9.778763 1.084103 didntLike 37 72011 4.932976 0.632026 didntLike 38 5914 2.216246 0.587095 smallDoses 39 14851 14.305636 0.632317 largeDoses 40 33553 12.591889 0.686581 largeDoses 41 44952 3.424649 1.004504 didntLike 42 17934 0.000000 0.147573 smallDoses 43 27738 8.533823 0.205324 largeDoses 44 29290 9.829528 0.238620 largeDoses 45 42330 11.492186 0.263499 largeDoses 46 36429 3.570968 0.832254 didntLike 47 39623 1.771228 0.207612 didntLike 48 32404 3.513921 0.991854 didntLike 49 27268 4.398172 0.975024 didntLike 50 5477 4.276823 1.174874 smallDoses 51 14254 5.946014 1.614244 smallDoses 52 68613 13.798970 0.724375 didntLike 53 41539 10.393591 1.663724 largeDoses 54 7917 3.007577 0.297302 smallDoses 55 21331 1.031938 0.486174 smallDoses 56 8338 4.751212 0.064693 smallDoses 57 5176 3.692269 1.655113 smallDoses 58 18983 10.448091 0.267652 largeDoses 59 68837 10.585786 0.329557 didntLike 60 13438 1.604501 0.069064 smallDoses 61 48849 3.679497 0.961466 didntLike 62 12285 3.795146 0.696694 smallDoses 63 7826 2.531885 1.659173 smallDoses 64 5565 9.733340 0.977746 smallDoses 65 10346 6.093067 1.413798 smallDoses 66 1823 7.712960 1.054927 smallDoses 67 9744 11.470364 0.760461 largeDoses 68 16857 2.886529 0.934416 smallDoses 69 39336 10.054373 1.138351 largeDoses 70 65230 9.972470 0.881876 didntLike 71 2463 2.335785 1.366145 smallDoses 72 27353 11.375155 1.528626 largeDoses 73 16191 0.000000 0.605619 smallDoses 74 12258 4.126787 0.357501 smallDoses 75 42377 6.319522 1.058602 didntLike 76 25607 8.680527 0.086955 largeDoses 77 77450 14.856391 1.129823 didntLike 78 58732 2.454285 0.222380 didntLike 79 46426 7.292202 0.548607 largeDoses 80 32688 8.745137 0.857348 largeDoses 81 64890 8.579001 0.683048 didntLike 82 8554 2.507302 0.869177 smallDoses 83 28861 11.415476 1.505466 largeDoses 84 42050 4.838540 1.680892 didntLike 85 32193 10.339507 0.583646 largeDoses 86 64895 6.573742 1.151433 didntLike 87 2355 6.539397 0.462065 smallDoses 88 0 2.209159 0.723567 smallDoses 89 70406 11.196378 0.836326 didntLike 90 57399 4.229595 0.128253 didntLike 91 41732 9.505944 0.005273 largeDoses 92 11429 8.652725 1.348934 largeDoses 93 75270 17.101108 0.490712 didntLike 94 5459 7.871839 0.717662 smallDoses 95 73520 8.262131 1.361646 didntLike 96 40279 9.015635 1.658555 largeDoses 97 21540 9.215351 0.806762 largeDoses 98 17694 6.375007 0.033678 smallDoses 99 22329 2.262014 1.022169 didntLike 100 46570 5.677110 0.709469 didntLike 101 42403 11.293017 0.207976 largeDoses 102 33654 6.590043 1.353117 didntLike 103 9171 4.711960 0.194167 smallDoses 104 28122 8.768099 1.108041 largeDoses 105 34095 11.502519 0.545097 largeDoses 106 1774 4.682812 0.578112 smallDoses 107 40131 12.446578 0.300754 largeDoses 108 13994 12.908384 1.657722 largeDoses 109 77064 12.601108 0.974527 didntLike 110 11210 3.929456 0.025466 smallDoses 111 6122 9.751503 1.182050 largeDoses 112 15341 3.043767 0.888168 smallDoses 113 44373 4.391522 0.807100 didntLike 114 28454 11.695276 0.679015 largeDoses 115 63771 7.879742 0.154263 didntLike 116 9217 5.613163 0.933632 smallDoses 117 69076 9.140172 0.851300 didntLike 118 24489 4.258644 0.206892 didntLike 119 16871 6.799831 1.221171 smallDoses 120 39776 8.752758 0.484418 largeDoses 121 5901 1.123033 1.180352 smallDoses 122 40987 10.833248 1.585426 largeDoses 123 7479 3.051618 0.026781 smallDoses 124 38768 5.308409 0.030683 largeDoses 125 4933 1.841792 0.028099 smallDoses 126 32311 2.261978 1.605603 didntLike 127 26501 11.573696 1.061347 largeDoses 128 37433 8.038764 1.083910 largeDoses 129 23503 10.734007 0.103715 largeDoses 130 68607 9.661909 0.350772 didntLike 131 27742 9.005850 0.548737 largeDoses 132 11303 0.000000 0.539131 smallDoses 133 0 5.757140 1.062373 smallDoses 134 32729 9.164656 1.624565 largeDoses 135 24619 1.318340 1.436243 didntLike 136 42414 14.075597 0.695934 largeDoses 137 20210 10.107550 1.308398 largeDoses 138 33225 7.960293 1.219760 largeDoses 139 54483 6.317292 0.018209 didntLike 140 18475 12.664194 0.595653 largeDoses 141 33926 2.906644 0.581657 didntLike 142 43865 2.388241 0.913938 didntLike 143 26547 6.024471 0.486215 largeDoses 144 44404 7.226764 1.255329 largeDoses 145 16674 4.183997 1.275290 smallDoses 146 8123 11.850211 1.096981 largeDoses 147 42747 11.661797 1.167935 largeDoses 148 56054 3.574967 0.494666 didntLike 149 10933 0.000000 0.107475 smallDoses 150 18121 7.937657 0.904799 largeDoses 151 11272 3.365027 1.014085 smallDoses 152 16297 0.000000 0.367491 smallDoses 153 28168 13.860672 1.293270 largeDoses 154 40963 10.306714 1.211594 largeDoses 155 31685 7.228002 0.670670 largeDoses 156 55164 4.508740 1.036192 didntLike 157 17595 0.366328 0.163652 smallDoses 158 1862 3.299444 0.575152 smallDoses 159 57087 0.573287 0.607915 didntLike 160 63082 9.183738 0.012280 didntLike 161 51213 7.842646 1.060636 largeDoses 162 6487 4.750964 0.558240 smallDoses 163 4805 11.438702 1.556334 largeDoses 164 30302 8.243063 1.122768 largeDoses 165 68680 7.949017 0.271865 didntLike 166 17591 7.875477 0.227085 smallDoses 167 74391 9.569087 0.364856 didntLike 168 37217 7.750103 0.869094 largeDoses 169 42814 0.000000 1.515293 didntLike 170 14738 3.396030 0.633977 smallDoses 171 19896 11.916091 0.025294 largeDoses 172 14673 0.460758 0.689586 smallDoses 173 32011 13.087566 0.476002 largeDoses 174 58736 4.589016 1.672600 didntLike 175 54744 8.397217 1.534103 didntLike 176 29482 5.562772 1.689388 didntLike 177 27698 10.905159 0.619091 largeDoses 178 11443 1.311441 1.169887 smallDoses 179 56117 10.647170 0.980141 largeDoses 180 39514 0.000000 0.481918 didntLike 181 26627 8.503025 0.830861 largeDoses 182 16525 0.436880 1.395314 smallDoses 183 24368 6.127867 1.102179 didntLike 184 22160 12.112492 0.359680 largeDoses 185 6030 1.264968 1.141582 smallDoses 186 6468 6.067568 1.327047 smallDoses 187 22945 8.010964 1.681648 largeDoses 188 18520 3.791084 0.304072 smallDoses 189 34914 11.773195 1.262621 largeDoses 190 6121 8.339588 1.443357 smallDoses 191 38063 2.563092 1.464013 didntLike 192 23410 5.954216 0.953782 didntLike 193 35073 9.288374 0.767318 largeDoses 194 52914 3.976796 1.043109 didntLike 195 16801 8.585227 1.455708 largeDoses 196 9533 1.271946 0.796506 smallDoses 197 16721 0.000000 0.242778 smallDoses 198 5832 0.000000 0.089749 smallDoses 199 44591 11.521298 0.300860 largeDoses 200 10143 1.139447 0.415373 smallDoses 201 21609 5.699090 1.391892 smallDoses 202 23817 2.449378 1.322560 didntLike 203 15640 0.000000 1.228380 smallDoses 204 8847 3.168365 0.053993 smallDoses 205 50939 10.428610 1.126257 largeDoses 206 28521 2.943070 1.446816 didntLike 207 32901 10.441348 0.975283 largeDoses 208 42850 12.478764 1.628726 largeDoses 209 13499 5.856902 0.363883 smallDoses 210 40345 2.476420 0.096075 didntLike 211 43547 1.826637 0.811457 didntLike 212 70758 4.324451 0.328235 didntLike 213 19780 1.376085 1.178359 smallDoses 214 44484 5.342462 0.394527 didntLike 215 54462 11.835521 0.693301 largeDoses 216 20085 12.423687 1.424264 largeDoses 217 42291 12.161273 0.071131 largeDoses 218 47550 8.148360 1.649194 largeDoses 219 11938 1.531067 1.549756 smallDoses 220 40699 3.200912 0.309679 didntLike 221 70908 8.862691 0.530506 didntLike 222 73989 6.370551 0.369350 didntLike 223 11872 2.468841 0.145060 smallDoses 224 48463 11.054212 0.141508 largeDoses 225 15987 2.037080 0.715243 smallDoses 226 70036 13.364030 0.549972 didntLike 227 32967 10.249135 0.192735 largeDoses 228 63249 10.464252 1.669767 didntLike 229 42795 9.424574 0.013725 largeDoses 230 14459 4.458902 0.268444 smallDoses 231 19973 0.000000 0.575976 smallDoses 232 5494 9.686082 1.029808 largeDoses 233 67902 13.649402 1.052618 didntLike 234 25621 13.181148 0.273014 largeDoses 235 27545 3.877472 0.401600 didntLike 236 58656 1.413952 0.451380 didntLike 237 7327 4.248986 1.430249 smallDoses 238 64555 8.779183 0.845947 didntLike 239 8998 4.156252 0.097109 smallDoses 240 11752 5.580018 0.158401 smallDoses 241 76319 15.040440 1.366898 didntLike 242 27665 12.793870 1.307323 largeDoses 243 67417 3.254877 0.669546 didntLike 244 21808 10.725607 0.588588 largeDoses 245 15326 8.256473 0.765891 smallDoses 246 20057 8.033892 1.618562 largeDoses 247 79341 10.702532 0.204792 didntLike 248 15636 5.062996 1.132555 smallDoses 249 35602 10.772286 0.668721 largeDoses 250 28544 1.892354 0.837028 didntLike 251 57663 1.019966 0.372320 didntLike 252 78727 15.546043 0.729742 didntLike 253 68255 11.638205 0.409125 didntLike 254 14964 3.427886 0.975616 smallDoses 255 21835 11.246174 1.475586 largeDoses 256 7487 0.000000 0.645045 smallDoses 257 8700 0.000000 1.424017 smallDoses 258 26226 8.242553 0.279069 largeDoses 259 65899 8.700060 0.101807 didntLike 260 6543 0.812344 0.260334 smallDoses 261 46556 2.448235 1.176829 didntLike 262 71038 13.230078 0.616147 didntLike 263 47657 0.236133 0.340840 didntLike 264 19600 11.155826 0.335131 largeDoses 265 37422 11.029636 0.505769 largeDoses 266 1363 2.901181 1.646633 smallDoses 267 26535 3.924594 1.143120 didntLike 268 47707 2.524806 1.292848 didntLike 269 38055 3.527474 1.449158 didntLike 270 6286 3.384281 0.889268 smallDoses 271 10747 0.000000 1.107592 smallDoses 272 44883 11.898890 0.406441 largeDoses 273 56823 3.529892 1.375844 didntLike 274 68086 11.442677 0.696919 didntLike 275 70242 10.308145 0.422722 didntLike 276 11409 8.540529 0.727373 smallDoses 277 67671 7.156949 1.691682 didntLike 278 61238 0.720675 0.847574 didntLike 279 17774 0.229405 1.038603 smallDoses 280 53376 3.399331 0.077501 didntLike 281 30930 6.157239 0.580133 didntLike 282 28987 1.239698 0.719989 didntLike 283 13655 6.036854 0.016548 smallDoses 284 7227 5.258665 0.933722 smallDoses 285 40409 12.393001 1.571281 largeDoses 286 13605 9.627613 0.935842 smallDoses 287 26400 11.130453 0.597610 largeDoses 288 13491 8.842595 0.349768 largeDoses 289 30232 10.690010 1.456595 largeDoses 290 43253 5.714718 1.674780 largeDoses 291 55536 3.052505 1.335804 didntLike 292 8807 0.000000 0.059025 smallDoses 293 25783 9.945307 1.287952 largeDoses 294 22812 2.719723 1.142148 didntLike 295 77826 11.154055 1.608486 didntLike 296 38172 2.687918 0.660836 didntLike 297 31676 10.037847 0.962245 largeDoses 298 74038 12.404762 1.112080 didntLike 299 44738 10.237305 0.633422 largeDoses 300 17410 4.745392 0.662520 smallDoses 301 5688 4.639461 1.569431 smallDoses 302 36642 3.149310 0.639669 didntLike 303 29956 13.406875 1.639194 largeDoses 304 60350 6.068668 0.881241 didntLike 305 23758 9.477022 0.899002 largeDoses 306 25780 3.897620 0.560201 smallDoses 307 11342 5.463615 1.203677 smallDoses 308 36109 3.369267 1.575043 didntLike 309 14292 5.234562 0.825954 smallDoses 310 11160 0.000000 0.722170 smallDoses 311 23762 12.979069 0.504068 largeDoses 312 39567 5.376564 0.557476 didntLike 313 25647 13.527910 1.586732 largeDoses 314 14814 2.196889 0.784587 smallDoses 315 73590 10.691748 0.007509 didntLike 316 35187 1.659242 0.447066 didntLike 317 49459 8.369667 0.656697 largeDoses 318 31657 13.157197 0.143248 largeDoses 319 6259 8.199667 0.908508 smallDoses 320 33101 4.441669 0.439381 largeDoses 321 27107 9.846492 0.644523 largeDoses 322 17824 0.019540 0.977949 smallDoses 323 43536 8.253774 0.748700 largeDoses 324 67705 6.038620 1.509646 didntLike 325 35283 6.091587 1.694641 largeDoses 326 71308 8.986820 1.225165 didntLike 327 31054 11.508473 1.624296 largeDoses 328 52387 8.807734 0.713922 largeDoses 329 40328 0.000000 0.816676 didntLike 330 34844 8.889202 1.665414 largeDoses 331 11607 3.178117 0.542752 smallDoses 332 64306 7.013795 0.139909 didntLike 333 32721 9.605014 0.065254 largeDoses 334 33170 1.230540 1.331674 didntLike 335 37192 10.412811 0.890803 largeDoses 336 13089 0.000000 0.567161 smallDoses 337 66491 9.699991 0.122011 didntLike 338 15941 0.000000 0.061191 smallDoses 339 4272 4.455293 0.272135 smallDoses 340 48812 3.020977 1.502803 didntLike 341 28818 8.099278 0.216317 largeDoses 342 35394 1.157764 1.603217 didntLike 343 71791 10.105396 0.121067 didntLike 344 40668 11.230148 0.408603 largeDoses 345 39580 9.070058 0.011379 largeDoses 346 11786 0.566460 0.478837 smallDoses 347 19251 0.000000 0.487300 smallDoses 348 56594 8.956369 1.193484 largeDoses 349 54495 1.523057 0.620528 didntLike 350 11844 2.749006 0.169855 smallDoses 351 45465 9.235393 0.188350 largeDoses 352 31033 10.555573 0.403927 largeDoses 353 16633 6.956372 1.519308 smallDoses 354 13887 0.636281 1.273984 smallDoses 355 52603 3.574737 0.075163 didntLike 356 72000 9.032486 1.461809 didntLike 357 68497 5.958993 0.023012 didntLike 358 35135 2.435300 1.211744 didntLike 359 26397 10.539731 1.638248 largeDoses 360 7313 7.646702 0.056513 smallDoses 361 91273 20.919349 0.644571 didntLike 362 24743 1.424726 0.838447 didntLike 363 31690 6.748663 0.890223 largeDoses 364 15432 2.289167 0.114881 smallDoses 365 58394 5.548377 0.402238 didntLike 366 33962 6.057227 0.432666 didntLike 367 31442 10.828595 0.559955 largeDoses 368 31044 11.318160 0.271094 largeDoses 369 29938 13.265311 0.633903 largeDoses 370 9875 0.000000 1.496715 smallDoses 371 51542 6.517133 0.402519 largeDoses 372 11878 4.934374 1.520028 smallDoses 373 69241 10.151738 0.896433 didntLike 374 37776 2.425781 1.559467 didntLike 375 68997 9.778962 1.195498 didntLike 376 67416 12.219950 0.657677 didntLike 377 59225 7.394151 0.954434 didntLike 378 29138 8.518535 0.742546 largeDoses 379 5962 2.798700 0.662632 smallDoses 380 10847 0.637930 0.617373 smallDoses 381 70527 10.750490 0.097415 didntLike 382 9610 0.625382 0.140969 smallDoses 383 64734 10.027968 0.282787 didntLike 384 25941 9.817347 0.364197 largeDoses 385 2763 0.646828 1.266069 smallDoses 386 55601 3.347111 0.914294 didntLike 387 31128 11.816892 0.193798 largeDoses 388 5181 0.000000 1.480198 smallDoses 389 69982 10.945666 0.993219 didntLike 390 52440 10.244706 0.280539 largeDoses 391 57350 2.579801 1.149172 didntLike 392 57869 2.630410 0.098869 didntLike 393 56557 11.746200 1.695517 largeDoses 394 42342 8.104232 1.326277 largeDoses 395 15560 12.409743 0.790295 largeDoses 396 34826 12.167844 1.328086 largeDoses 397 8569 3.198408 0.299287 smallDoses 398 77623 16.055513 0.541052 didntLike 399 78184 7.138659 0.158481 didntLike 400 7036 4.831041 0.761419 smallDoses 401 69616 10.082890 1.373611 didntLike 402 21546 10.066867 0.788470 largeDoses 403 36715 8.129538 0.329913 largeDoses 404 20522 3.012463 1.138108 smallDoses 405 42349 3.720391 0.845974 didntLike 406 9037 0.773493 1.148256 smallDoses 407 26728 10.962941 1.037324 largeDoses 408 587 0.177621 0.162614 smallDoses 409 48915 3.085853 0.967899 didntLike 410 9824 8.426781 0.202558 smallDoses 411 4135 1.825927 1.128347 smallDoses 412 9666 2.185155 1.010173 smallDoses 413 59333 7.184595 1.261338 didntLike 414 36198 0.000000 0.116525 didntLike 415 34909 8.901752 1.033527 largeDoses 416 47516 2.451497 1.358795 didntLike 417 55807 3.213631 0.432044 didntLike 418 14036 3.974739 0.723929 smallDoses 419 42856 9.601306 0.619232 largeDoses 420 64007 8.363897 0.445341 didntLike 421 59428 6.381484 1.365019 didntLike 422 13730 0.000000 1.403914 smallDoses 423 41740 9.609836 1.438105 largeDoses 424 63546 9.904741 0.985862 didntLike 425 30417 7.185807 1.489102 largeDoses 426 69636 5.466703 1.216571 didntLike 427 64660 0.000000 0.915898 didntLike 428 14883 4.575443 0.535671 smallDoses 429 7965 3.277076 1.010868 smallDoses 430 68620 10.246623 1.239634 didntLike 431 8738 2.341735 1.060235 smallDoses 432 7544 3.201046 0.498843 smallDoses 433 6377 6.066013 0.120927 smallDoses 434 36842 8.829379 0.895657 largeDoses 435 81046 15.833048 1.568245 didntLike 436 67736 13.516711 1.220153 didntLike 437 32492 0.664284 1.116755 didntLike 438 39299 6.325139 0.605109 largeDoses 439 77289 8.677499 0.344373 didntLike 440 33835 8.188005 0.964896 largeDoses 441 71890 9.414263 0.384030 didntLike 442 32054 9.196547 1.138253 largeDoses 443 38579 10.202968 0.452363 largeDoses 444 55984 2.119439 1.481661 didntLike 445 72694 13.635078 0.858314 didntLike 446 42299 0.083443 0.701669 didntLike 447 26635 9.149096 1.051446 largeDoses 448 8579 1.933803 1.374388 smallDoses 449 37302 14.115544 0.676198 largeDoses 450 22878 8.933736 0.943352 largeDoses 451 4364 2.661254 0.946117 smallDoses 452 4985 0.988432 1.305027 smallDoses 453 37068 2.063741 1.125946 didntLike 454 41137 2.220590 0.690754 didntLike 455 67759 6.424849 0.806641 didntLike 456 11831 1.156153 1.613674 smallDoses 457 34502 3.032720 0.601847 didntLike 458 4088 3.076828 0.952089 smallDoses 459 15199 0.000000 0.318105 smallDoses 460 17309 7.750480 0.554015 largeDoses 461 42816 10.958135 1.482500 largeDoses 462 43751 10.222018 0.488678 largeDoses 463 58335 2.367988 0.435741 didntLike 464 75039 7.686054 1.381455 didntLike 465 42878 11.464879 1.481589 largeDoses 466 42770 11.075735 0.089726 largeDoses 467 8848 3.543989 0.345853 smallDoses 468 31340 8.123889 1.282880 largeDoses 469 41413 4.331769 0.754467 largeDoses 470 12731 0.120865 1.211961 smallDoses 471 22447 6.116109 0.701523 largeDoses 472 33564 7.474534 0.505790 largeDoses 473 48907 8.819454 0.649292 largeDoses 474 8762 6.802144 0.615284 smallDoses 475 46696 12.666325 0.931960 largeDoses 476 36851 8.636180 0.399333 largeDoses 477 67639 11.730991 1.289833 didntLike 478 171 8.132449 0.039062 smallDoses 479 26674 10.296589 1.496144 largeDoses 480 8739 7.583906 1.005764 smallDoses 481 66668 9.777806 0.496377 didntLike 482 68732 8.833546 0.513876 didntLike 483 69995 4.907899 1.518036 didntLike 484 82008 8.362736 1.285939 didntLike 485 25054 9.084726 1.606312 largeDoses 486 33085 14.164141 0.560970 largeDoses 487 41379 9.080683 0.989920 largeDoses 488 39417 6.522767 0.038548 largeDoses 489 12556 3.690342 0.462281 smallDoses 490 39432 3.563706 0.242019 didntLike 491 38010 1.065870 1.141569 didntLike 492 69306 6.683796 1.456317 didntLike 493 38000 1.712874 0.243945 didntLike 494 46321 13.109929 1.280111 largeDoses 495 66293 11.327910 0.780977 didntLike 496 22730 4.545711 1.233254 didntLike 497 5952 3.367889 0.468104 smallDoses 498 72308 8.326224 0.567347 didntLike 499 60338 8.978339 1.442034 didntLike 500 13301 5.655826 1.582159 smallDoses 501 27884 8.855312 0.570684 largeDoses 502 11188 6.649568 0.544233 smallDoses 503 56796 3.966325 0.850410 didntLike 504 8571 1.924045 1.664782 smallDoses 505 4914 6.004812 0.280369 smallDoses 506 10784 0.000000 0.375849 smallDoses 507 39296 9.923018 0.092192 largeDoses 508 13113 2.389084 0.119284 smallDoses 509 70204 13.663189 0.133251 didntLike 510 46813 11.434976 0.321216 largeDoses 511 11697 0.358270 1.292858 smallDoses 512 44183 9.598873 0.223524 largeDoses 513 2225 6.375275 0.608040 smallDoses 514 29066 11.580532 0.458401 largeDoses 515 4245 5.319324 1.598070 smallDoses 516 34379 4.324031 1.603481 didntLike 517 44441 2.358370 1.273204 didntLike 518 2022 0.000000 1.182708 smallDoses 519 26866 12.824376 0.890411 largeDoses 520 57070 1.587247 1.456982 didntLike 521 32932 8.510324 1.520683 largeDoses 522 51967 10.428884 1.187734 largeDoses 523 44432 8.346618 0.042318 largeDoses 524 67066 7.541444 0.809226 didntLike 525 17262 2.540946 1.583286 smallDoses 526 79728 9.473047 0.692513 didntLike 527 14259 0.352284 0.474080 smallDoses 528 6122 0.000000 0.589826 smallDoses 529 76879 12.405171 0.567201 didntLike 530 11426 4.126775 0.871452 smallDoses 531 2493 0.034087 0.335848 smallDoses 532 19910 1.177634 0.075106 smallDoses 533 10939 0.000000 0.479996 smallDoses 534 17716 0.994909 0.611135 smallDoses 535 31390 11.053664 1.180117 largeDoses 536 20375 0.000000 1.679729 smallDoses 537 26309 2.495011 1.459589 didntLike 538 33484 11.516831 0.001156 largeDoses 539 45944 9.213215 0.797743 largeDoses 540 4249 5.332865 0.109288 smallDoses 541 6089 0.000000 1.689771 smallDoses 542 7513 0.000000 1.126053 smallDoses 543 27862 12.640062 1.690903 largeDoses 544 39038 2.693142 1.317518 didntLike 545 19218 3.328969 0.268271 smallDoses 546 62911 7.193166 1.117456 didntLike 547 77758 6.615512 1.521012 didntLike 548 27940 8.000567 0.835341 largeDoses 549 2194 4.017541 0.512104 smallDoses 550 37072 13.245859 0.927465 largeDoses 551 15585 5.970616 0.813624 smallDoses 552 25577 11.668719 0.886902 largeDoses 553 8777 4.283237 1.272728 smallDoses 554 29016 10.742963 0.971401 largeDoses 555 21910 12.326672 1.592608 largeDoses 556 12916 0.000000 0.344622 smallDoses 557 10976 0.000000 0.922846 smallDoses 558 79065 10.602095 0.573686 didntLike 559 36759 10.861859 1.155054 largeDoses 560 50011 1.229094 1.638690 didntLike 561 1155 0.410392 1.313401 smallDoses 562 71600 14.552711 0.616162 didntLike 563 30817 14.178043 0.616313 largeDoses 564 54559 14.136260 0.362388 didntLike 565 29764 0.093534 1.207194 didntLike 566 69100 10.929021 0.403110 didntLike 567 47324 11.432919 0.825959 largeDoses 568 73199 9.134527 0.586846 didntLike 569 44461 5.071432 1.421420 didntLike 570 45617 11.460254 1.541749 largeDoses 571 28221 11.620039 1.103553 largeDoses 572 7091 4.022079 0.207307 smallDoses 573 6110 3.057842 1.631262 smallDoses 574 79016 7.782169 0.404385 didntLike 575 18289 7.981741 0.929789 largeDoses 576 43679 4.601363 0.268326 didntLike 577 22075 2.595564 1.115375 didntLike 578 23535 10.049077 0.391045 largeDoses 579 25301 3.265444 1.572970 smallDoses 580 32256 11.780282 1.511014 largeDoses 581 36951 3.075975 0.286284 didntLike 582 31290 1.795307 0.194343 didntLike 583 38953 11.106979 0.202415 largeDoses 584 35257 5.994413 0.800021 didntLike 585 25847 9.706062 1.012182 largeDoses 586 32680 10.582992 0.836025 largeDoses 587 62018 7.038266 1.458979 didntLike 588 9074 0.023771 0.015314 smallDoses 589 33004 12.823982 0.676371 largeDoses 590 44588 3.617770 0.493483 didntLike 591 32565 8.346684 0.253317 largeDoses 592 38563 6.104317 0.099207 didntLike 593 75668 16.207776 0.584973 didntLike 594 9069 6.401969 1.691873 smallDoses 595 53395 2.298696 0.559757 didntLike 596 28631 7.661515 0.055981 largeDoses 597 71036 6.353608 1.645301 didntLike 598 71142 10.442780 0.335870 didntLike 599 37653 3.834509 1.346121 didntLike 600 76839 10.998587 0.584555 didntLike 601 9916 2.695935 1.512111 smallDoses 602 38889 3.356646 0.324230 didntLike 603 39075 14.677836 0.793183 largeDoses 604 48071 1.551934 0.130902 didntLike 605 7275 2.464739 0.223502 smallDoses 606 41804 1.533216 1.007481 didntLike 607 35665 12.473921 0.162910 largeDoses 608 67956 6.491596 0.032576 didntLike 609 41892 10.506276 1.510747 largeDoses 610 38844 4.380388 0.748506 didntLike 611 74197 13.670988 1.687944 didntLike 612 14201 8.317599 0.390409 smallDoses 613 3908 0.000000 0.556245 smallDoses 614 2459 0.000000 0.290218 smallDoses 615 32027 10.095799 1.188148 largeDoses 616 12870 0.860695 1.482632 smallDoses 617 9880 1.557564 0.711278 smallDoses 618 72784 10.072779 0.756030 didntLike 619 17521 0.000000 0.431468 smallDoses 620 50283 7.140817 0.883813 largeDoses 621 33536 11.384548 1.438307 largeDoses 622 9452 3.214568 1.083536 smallDoses 623 37457 11.720655 0.301636 largeDoses 624 17724 6.374475 1.475925 largeDoses 625 43869 5.749684 0.198875 largeDoses 626 264 3.871808 0.552602 smallDoses 627 25736 8.336309 0.636238 largeDoses 628 39584 9.710442 1.503735 largeDoses 629 31246 1.532611 1.433898 didntLike 630 49567 9.785785 0.984614 largeDoses 631 7052 2.633627 1.097866 smallDoses 632 35493 9.238935 0.494701 largeDoses 633 10986 1.205656 1.398803 smallDoses 634 49508 3.124909 1.670121 didntLike 635 5734 7.935489 1.585044 smallDoses 636 65479 12.746636 1.560352 didntLike 637 77268 10.732563 0.545321 didntLike 638 28490 3.977403 0.766103 didntLike 639 13546 4.194426 0.450663 smallDoses 640 37166 9.610286 0.142912 largeDoses 641 16381 4.797555 1.260455 smallDoses 642 10848 1.615279 0.093002 smallDoses 643 35405 4.614771 1.027105 didntLike 644 15917 0.000000 1.369726 smallDoses 645 6131 0.608457 0.512220 smallDoses 646 67432 6.558239 0.667579 didntLike 647 30354 12.315116 0.197068 largeDoses 648 69696 7.014973 1.494616 didntLike 649 33481 8.822304 1.194177 largeDoses 650 43075 10.086796 0.570455 largeDoses 651 38343 7.241614 1.661627 largeDoses 652 14318 4.602395 1.511768 smallDoses 653 5367 7.434921 0.079792 smallDoses 654 37894 10.467570 1.595418 largeDoses 655 36172 9.948127 0.003663 largeDoses 656 40123 2.478529 1.568987 didntLike 657 10976 5.938545 0.878540 smallDoses 658 12705 0.000000 0.948004 smallDoses 659 12495 5.559181 1.357926 smallDoses 660 35681 9.776654 0.535966 largeDoses 661 46202 3.092056 0.490906 didntLike 662 11505 0.000000 1.623311 smallDoses 663 22834 4.459495 0.538867 didntLike 664 49901 8.334306 1.646600 largeDoses 665 71932 11.226654 0.384686 didntLike 666 13279 3.904737 1.597294 smallDoses 667 49112 7.038205 1.211329 largeDoses 668 77129 9.836120 1.054340 didntLike 669 37447 1.990976 0.378081 didntLike 670 62397 9.005302 0.485385 didntLike 671 0 1.772510 1.039873 smallDoses 672 15476 0.458674 0.819560 smallDoses 673 40625 10.003919 0.231658 largeDoses 674 36706 0.520807 1.476008 didntLike 675 28580 10.678214 1.431837 largeDoses 676 25862 4.425992 1.363842 didntLike 677 63488 12.035355 0.831222 didntLike 678 33944 10.606732 1.253858 largeDoses 679 30099 1.568653 0.684264 didntLike 680 13725 2.545434 0.024271 smallDoses 681 36768 10.264062 0.982593 largeDoses 682 64656 9.866276 0.685218 didntLike 683 14927 0.142704 0.057455 smallDoses 684 43231 9.853270 1.521432 largeDoses 685 66087 6.596604 1.653574 didntLike 686 19806 2.602287 1.321481 smallDoses 687 41081 10.411776 0.664168 largeDoses 688 10277 7.083449 0.622589 smallDoses 689 7014 2.080068 1.254441 smallDoses 690 17275 0.522844 1.622458 smallDoses 691 31600 10.362000 1.544827 largeDoses 692 59956 3.412967 1.035410 didntLike 693 42181 6.796548 1.112153 largeDoses 694 51743 4.092035 0.075804 didntLike 695 5194 2.763811 1.564325 smallDoses 696 30832 12.547439 1.402443 largeDoses 697 7976 5.708052 1.596152 smallDoses 698 14602 4.558025 0.375806 smallDoses 699 41571 11.642307 0.438553 largeDoses 700 55028 3.222443 0.121399 didntLike 701 5837 4.736156 0.029871 smallDoses 702 39808 10.839526 0.836323 largeDoses 703 20944 4.194791 0.235483 smallDoses 704 22146 14.936259 0.888582 largeDoses 705 42169 3.310699 1.521855 didntLike 706 7010 2.971931 0.034321 smallDoses 707 3807 9.261667 0.537807 smallDoses 708 29241 7.791833 1.111416 largeDoses 709 52696 1.480470 1.028750 didntLike 710 42545 3.677287 0.244167 didntLike 711 24437 2.202967 1.370399 didntLike 712 16037 5.796735 0.935893 smallDoses 713 8493 3.063333 0.144089 smallDoses 714 68080 11.233094 0.492487 didntLike 715 59016 1.965570 0.005697 didntLike 716 11810 8.616719 0.137419 smallDoses 717 68630 6.609989 1.083505 didntLike 718 7629 1.712639 1.086297 smallDoses 719 71992 10.117445 1.299319 didntLike 720 13398 0.000000 1.104178 smallDoses 721 26241 9.824777 1.346821 largeDoses 722 11160 1.653089 0.980949 smallDoses 723 76701 18.178822 1.473671 didntLike 724 32174 6.781126 0.885340 largeDoses 725 45043 8.206750 1.549223 largeDoses 726 42173 10.081853 1.376745 largeDoses 727 69801 6.288742 0.112799 didntLike 728 41737 3.695937 1.543589 didntLike 729 46979 6.726151 1.069380 largeDoses 730 79267 12.969999 1.568223 didntLike 731 4615 2.661390 1.531933 smallDoses 732 32907 7.072764 1.117386 largeDoses 733 37444 9.123366 1.318988 largeDoses 734 569 3.743946 1.039546 smallDoses 735 8723 2.341300 0.219361 smallDoses 736 6024 0.541913 0.592348 smallDoses 737 52252 2.310828 1.436753 didntLike 738 8358 6.226597 1.427316 smallDoses 739 26166 7.277876 0.489252 largeDoses 740 18471 0.000000 0.389459 smallDoses 741 3386 7.218221 1.098828 smallDoses 742 41544 8.777129 1.111464 largeDoses 743 10480 2.813428 0.819419 smallDoses 744 5894 2.268766 1.412130 smallDoses 745 7273 6.283627 0.571292 smallDoses 746 22272 7.520081 1.626868 largeDoses 747 31369 11.739225 0.027138 largeDoses 748 10708 3.746883 0.877350 smallDoses 749 69364 12.089835 0.521631 didntLike 750 37760 12.310404 0.259339 largeDoses 751 13004 0.000000 0.671355 smallDoses 752 37885 2.728800 0.331502 didntLike 753 52555 10.814342 0.607652 largeDoses 754 38997 12.170268 0.844205 largeDoses 755 69698 6.698371 0.240084 didntLike 756 11783 3.632672 1.643479 smallDoses 757 47636 10.059991 0.892361 largeDoses 758 15744 1.887674 0.756162 smallDoses 759 69058 8.229125 0.195886 didntLike 760 33057 7.817082 0.476102 largeDoses 761 28681 12.277230 0.076805 largeDoses 762 34042 10.055337 1.115778 largeDoses 763 29928 3.596002 1.485952 didntLike 764 9734 2.755530 1.420655 smallDoses 765 7344 7.780991 0.513048 smallDoses 766 7387 0.093705 0.391834 smallDoses 767 33957 8.481567 0.520078 largeDoses 768 9936 3.865584 0.110062 smallDoses 769 36094 9.683709 0.779984 largeDoses 770 39835 10.617255 1.359970 largeDoses 771 64486 7.203216 1.624762 didntLike 772 0 7.601414 1.215605 smallDoses 773 39539 1.386107 1.417070 didntLike 774 66972 9.129253 0.594089 didntLike 775 15029 1.363447 0.620841 smallDoses 776 44909 3.181399 0.359329 didntLike 777 38183 13.365414 0.217011 largeDoses 778 37372 4.207717 1.289767 didntLike 779 0 4.088395 0.870075 smallDoses 780 17786 3.327371 1.142505 smallDoses 781 39055 1.303323 1.235650 didntLike 782 37045 7.999279 1.581763 largeDoses 783 6435 2.217488 0.864536 smallDoses 784 72265 7.751808 0.192451 didntLike 785 28152 14.149305 1.591532 largeDoses 786 25931 8.765721 0.152808 largeDoses 787 7538 3.408996 0.184896 smallDoses 788 1315 1.251021 0.112340 smallDoses 789 12292 6.160619 1.537165 smallDoses 790 49248 1.034538 1.585162 didntLike 791 9025 0.000000 1.034635 smallDoses 792 13438 2.355051 0.542603 smallDoses 793 69683 6.614543 0.153771 didntLike 794 25374 10.245062 1.450903 largeDoses 795 55264 3.467074 1.231019 didntLike 796 38324 7.487678 1.572293 largeDoses 797 69643 4.624115 1.185192 didntLike 798 44058 8.995957 1.436479 largeDoses 799 41316 11.564476 0.007195 largeDoses 800 29119 3.440948 0.078331 didntLike 801 51656 1.673603 0.732746 didntLike 802 3030 4.719341 0.699755 smallDoses 803 35695 10.304798 1.576488 largeDoses 804 1537 2.086915 1.199312 smallDoses 805 9083 6.338220 1.131305 smallDoses 806 47744 8.254926 0.710694 largeDoses 807 71372 16.067108 0.974142 didntLike 808 37980 1.723201 0.310488 didntLike 809 42385 3.785045 0.876904 didntLike 810 22687 2.557561 0.123738 didntLike 811 39512 9.852220 1.095171 largeDoses 812 11885 3.679147 1.557205 smallDoses 813 4944 9.789681 0.852971 smallDoses 814 73230 14.958998 0.526707 didntLike 815 17585 11.182148 1.288459 largeDoses 816 68737 7.528533 1.657487 didntLike 817 13818 5.253802 1.378603 smallDoses 818 31662 13.946752 1.426657 largeDoses 819 86686 15.557263 1.430029 didntLike 820 43214 12.483550 0.688513 largeDoses 821 24091 2.317302 1.411137 didntLike 822 52544 10.069724 0.766119 largeDoses 823 61861 5.792231 1.615483 didntLike 824 47903 4.138435 0.475994 didntLike 825 37190 12.929517 0.304378 largeDoses 826 6013 9.378238 0.307392 smallDoses 827 27223 8.361362 1.643204 largeDoses 828 69027 7.939406 1.325042 didntLike 829 78642 10.735384 0.705788 didntLike 830 30254 11.592723 0.286188 largeDoses 831 21704 10.098356 0.704748 largeDoses 832 34985 9.299025 0.545337 largeDoses 833 31316 11.158297 0.218067 largeDoses 834 76368 16.143900 0.558388 didntLike 835 27953 10.971700 1.221787 largeDoses 836 152 0.000000 0.681478 smallDoses 837 9146 3.178961 1.292692 smallDoses 838 75346 17.625350 0.339926 didntLike 839 26376 1.995833 0.267826 didntLike 840 35255 10.640467 0.416181 largeDoses 841 19198 9.628339 0.985462 largeDoses 842 12518 4.662664 0.495403 smallDoses 843 25453 5.754047 1.382742 smallDoses 844 12530 0.000000 0.037146 smallDoses 845 62230 9.334332 0.198118 didntLike 846 9517 3.846162 0.619968 smallDoses 847 71161 10.685084 0.678179 didntLike 848 1593 4.752134 0.359205 smallDoses 849 33794 0.697630 0.966786 didntLike 850 39710 10.365836 0.505898 largeDoses 851 16941 0.461478 0.352865 smallDoses 852 69209 11.339537 1.068740 didntLike 853 4446 5.420280 0.127310 smallDoses 854 9347 3.469955 1.619947 smallDoses 855 55635 8.517067 0.994858 largeDoses 856 65889 8.306512 0.413690 didntLike 857 10753 2.628690 0.444320 smallDoses 858 7055 0.000000 0.802985 smallDoses 859 7905 0.000000 1.170397 smallDoses 860 53447 7.298767 1.582346 largeDoses 861 9194 7.331319 1.277988 smallDoses 862 61914 9.392269 0.151617 didntLike 863 15630 5.541201 1.180596 smallDoses 864 79194 15.149460 0.537540 didntLike 865 12268 5.515189 0.250562 smallDoses 866 33682 7.728898 0.920494 largeDoses 867 26080 11.318785 1.510979 largeDoses 868 19119 3.574709 1.531514 smallDoses 869 30902 7.350965 0.026332 largeDoses 870 63039 7.122363 1.630177 didntLike 871 51136 1.828412 1.013702 didntLike 872 35262 10.117989 1.156862 largeDoses 873 42776 11.309897 0.086291 largeDoses 874 64191 8.342034 1.388569 didntLike 875 15436 0.241714 0.715577 smallDoses 876 14402 10.482619 1.694972 smallDoses 877 6341 9.289510 1.428879 smallDoses 878 14113 4.269419 0.134181 smallDoses 879 6390 0.000000 0.189456 smallDoses 880 8794 0.817119 0.143668 smallDoses 881 43432 1.508394 0.652651 didntLike 882 38334 9.359918 0.052262 largeDoses 883 34068 10.052333 0.550423 largeDoses 884 30819 11.111660 0.989159 largeDoses 885 22239 11.265971 0.724054 largeDoses 886 28725 10.383830 0.254836 largeDoses 887 57071 3.878569 1.377983 didntLike 888 72420 13.679237 0.025346 didntLike 889 28294 10.526846 0.781569 largeDoses 890 9896 0.000000 0.924198 smallDoses 891 65821 4.106727 1.085669 didntLike 892 7645 8.118856 1.470686 smallDoses 893 71289 7.796874 0.052336 didntLike 894 5128 2.789669 1.093070 smallDoses 895 13711 6.226962 0.287251 smallDoses 896 22240 10.169548 1.660104 largeDoses 897 15092 0.000000 1.370549 smallDoses 898 5017 7.513353 0.137348 smallDoses 899 10141 8.240793 0.099735 smallDoses 900 35570 14.612797 1.247390 largeDoses 901 46893 3.562976 0.445386 didntLike 902 8178 3.230482 1.331698 smallDoses 903 55783 3.612548 1.551911 didntLike 904 1148 0.000000 0.332365 smallDoses 905 10062 3.931299 0.487577 smallDoses 906 74124 14.752342 1.155160 didntLike 907 66603 10.261887 1.628085 didntLike 908 11893 2.787266 1.570402 smallDoses 909 50908 15.112319 1.324132 largeDoses 910 39891 5.184553 0.223382 largeDoses 911 65915 3.868359 0.128078 didntLike 912 65678 3.507965 0.028904 didntLike 913 62996 11.019254 0.427554 didntLike 914 36851 3.812387 0.655245 didntLike 915 36669 11.056784 0.378725 largeDoses 916 38876 8.826880 1.002328 largeDoses 917 26878 11.173861 1.478244 largeDoses 918 46246 11.506465 0.421993 largeDoses 919 12761 7.798138 0.147917 largeDoses 920 35282 10.155081 1.370039 largeDoses 921 68306 10.645275 0.693453 didntLike 922 31262 9.663200 1.521541 largeDoses 923 34754 10.790404 1.312679 largeDoses 924 13408 2.810534 0.219962 smallDoses 925 30365 9.825999 1.388500 largeDoses 926 10709 1.421316 0.677603 smallDoses 927 24332 11.123219 0.809107 largeDoses 928 45517 13.402206 0.661524 largeDoses 929 6178 1.212255 0.836807 smallDoses 930 10639 1.568446 1.297469 smallDoses 931 29613 3.343473 1.312266 didntLike 932 22392 5.400155 0.193494 didntLike 933 51126 3.818754 0.590905 didntLike 934 53644 7.973845 0.307364 largeDoses 935 51417 9.078824 0.734876 largeDoses 936 24859 0.153467 0.766619 didntLike 937 61732 8.325167 0.028479 didntLike 938 71128 7.092089 1.216733 didntLike 939 27276 5.192485 1.094409 largeDoses 940 30453 10.340791 1.087721 largeDoses 941 18670 2.077169 1.019775 smallDoses 942 70600 10.151966 0.993105 didntLike 943 12683 0.046826 0.809614 smallDoses 944 81597 11.221874 1.395015 didntLike 945 69959 14.497963 1.019254 didntLike 946 8124 3.554508 0.533462 smallDoses 947 18867 3.522673 0.086725 smallDoses 948 80886 14.531655 0.380172 didntLike 949 55895 3.027528 0.885457 didntLike 950 31587 1.845967 0.488985 didntLike 951 10591 10.226164 0.804403 largeDoses 952 70096 10.965926 1.212328 didntLike 953 53151 2.129921 1.477378 didntLike 954 11992 0.000000 1.606849 smallDoses 955 33114 9.489005 0.827814 largeDoses 956 7413 0.000000 1.020797 smallDoses 957 10583 0.000000 1.270167 smallDoses 958 58668 6.556676 0.055183 didntLike 959 35018 9.959588 0.060020 largeDoses 960 70843 7.436056 1.479856 didntLike 961 14011 0.404888 0.459517 smallDoses 962 35015 9.952942 1.650279 largeDoses 963 70839 15.600252 0.021935 didntLike 964 3024 2.723846 0.387455 smallDoses 965 5526 0.513866 1.323448 smallDoses 966 5113 0.000000 0.861859 smallDoses 967 20851 7.280602 1.438470 smallDoses 968 40999 9.161978 1.110180 largeDoses 969 15823 0.991725 0.730979 smallDoses 970 35432 7.398380 0.684218 largeDoses 971 53711 12.149747 1.389088 largeDoses 972 64371 9.149678 0.874905 didntLike 973 9289 9.666576 1.370330 smallDoses 974 60613 3.620110 0.287767 didntLike 975 18338 5.238800 1.253646 smallDoses 976 22845 14.715782 1.503758 largeDoses 977 74676 14.445740 1.211160 didntLike 978 34143 13.609528 0.364240 largeDoses 979 14153 3.141585 0.424280 smallDoses 980 9327 0.000000 0.120947 smallDoses 981 18991 0.454750 1.033280 smallDoses 982 9193 0.510310 0.016395 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1.2把文本文件初步处理，分类换成数字datingTestSet2.txt：
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2 72308 8.326224 0.567347 1 60338 8.978339 1.442034 1 13301 5.655826 1.582159 2 27884 8.855312 0.570684 3 11188 6.649568 0.544233 2 56796 3.966325 0.850410 1 8571 1.924045 1.664782 2 4914 6.004812 0.280369 2 10784 0.000000 0.375849 2 39296 9.923018 0.092192 3 13113 2.389084 0.119284 2 70204 13.663189 0.133251 1 46813 11.434976 0.321216 3 11697 0.358270 1.292858 2 44183 9.598873 0.223524 3 2225 6.375275 0.608040 2 29066 11.580532 0.458401 3 4245 5.319324 1.598070 2 34379 4.324031 1.603481 1 44441 2.358370 1.273204 1 2022 0.000000 1.182708 2 26866 12.824376 0.890411 3 57070 1.587247 1.456982 1 32932 8.510324 1.520683 3 51967 10.428884 1.187734 3 44432 8.346618 0.042318 3 67066 7.541444 0.809226 1 17262 2.540946 1.583286 2 79728 9.473047 0.692513 1 14259 0.352284 0.474080 2 6122 0.000000 0.589826 2 76879 12.405171 0.567201 1 11426 4.126775 0.871452 2 2493 0.034087 0.335848 2 19910 1.177634 0.075106 2 10939 0.000000 0.479996 2 17716 0.994909 0.611135 2 31390 11.053664 1.180117 3 20375 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7.782169 0.404385 1 18289 7.981741 0.929789 3 43679 4.601363 0.268326 1 22075 2.595564 1.115375 1 23535 10.049077 0.391045 3 25301 3.265444 1.572970 2 32256 11.780282 1.511014 3 36951 3.075975 0.286284 1 31290 1.795307 0.194343 1 38953 11.106979 0.202415 3 35257 5.994413 0.800021 1 25847 9.706062 1.012182 3 32680 10.582992 0.836025 3 62018 7.038266 1.458979 1 9074 0.023771 0.015314 2 33004 12.823982 0.676371 3 44588 3.617770 0.493483 1 32565 8.346684 0.253317 3 38563 6.104317 0.099207 1 75668 16.207776 0.584973 1 9069 6.401969 1.691873 2 53395 2.298696 0.559757 1 28631 7.661515 0.055981 3 71036 6.353608 1.645301 1 71142 10.442780 0.335870 1 37653 3.834509 1.346121 1 76839 10.998587 0.584555 1 9916 2.695935 1.512111 2 38889 3.356646 0.324230 1 39075 14.677836 0.793183 3 48071 1.551934 0.130902 1 7275 2.464739 0.223502 2 41804 1.533216 1.007481 1 35665 12.473921 0.162910 3 67956 6.491596 0.032576 1 41892 10.506276 1.510747 3 38844 4.380388 0.748506 1 74197 13.670988 1.687944 1 14201 8.317599 0.390409 2 3908 0.000000 0.556245 2 2459 0.000000 0.290218 2 32027 10.095799 1.188148 3 12870 0.860695 1.482632 2 9880 1.557564 0.711278 2 72784 10.072779 0.756030 1 17521 0.000000 0.431468 2 50283 7.140817 0.883813 3 33536 11.384548 1.438307 3 9452 3.214568 1.083536 2 37457 11.720655 0.301636 3 17724 6.374475 1.475925 3 43869 5.749684 0.198875 3 264 3.871808 0.552602 2 25736 8.336309 0.636238 3 39584 9.710442 1.503735 3 31246 1.532611 1.433898 1 49567 9.785785 0.984614 3 7052 2.633627 1.097866 2 35493 9.238935 0.494701 3 10986 1.205656 1.398803 2 49508 3.124909 1.670121 1 5734 7.935489 1.585044 2 65479 12.746636 1.560352 1 77268 10.732563 0.545321 1 28490 3.977403 0.766103 1 13546 4.194426 0.450663 2 37166 9.610286 0.142912 3 16381 4.797555 1.260455 2 10848 1.615279 0.093002 2 35405 4.614771 1.027105 1 15917 0.000000 1.369726 2 6131 0.608457 0.512220 2 67432 6.558239 0.667579 1 30354 12.315116 0.197068 3 69696 7.014973 1.494616 1 33481 8.822304 1.194177 3 43075 10.086796 0.570455 3 38343 7.241614 1.661627 3 14318 4.602395 1.511768 2 5367 7.434921 0.079792 2 37894 10.467570 1.595418 3 36172 9.948127 0.003663 3 40123 2.478529 1.568987 1 10976 5.938545 0.878540 2 12705 0.000000 0.948004 2 12495 5.559181 1.357926 2 35681 9.776654 0.535966 3 46202 3.092056 0.490906 1 11505 0.000000 1.623311 2 22834 4.459495 0.538867 1 49901 8.334306 1.646600 3 71932 11.226654 0.384686 1 13279 3.904737 1.597294 2 49112 7.038205 1.211329 3 77129 9.836120 1.054340 1 37447 1.990976 0.378081 1 62397 9.005302 0.485385 1 0 1.772510 1.039873 2 15476 0.458674 0.819560 2 40625 10.003919 0.231658 3 36706 0.520807 1.476008 1 28580 10.678214 1.431837 3 25862 4.425992 1.363842 1 63488 12.035355 0.831222 1 33944 10.606732 1.253858 3 30099 1.568653 0.684264 1 13725 2.545434 0.024271 2 36768 10.264062 0.982593 3 64656 9.866276 0.685218 1 14927 0.142704 0.057455 2 43231 9.853270 1.521432 3 66087 6.596604 1.653574 1 19806 2.602287 1.321481 2 41081 10.411776 0.664168 3 10277 7.083449 0.622589 2 7014 2.080068 1.254441 2 17275 0.522844 1.622458 2 31600 10.362000 1.544827 3 59956 3.412967 1.035410 1 42181 6.796548 1.112153 3 51743 4.092035 0.075804 1 5194 2.763811 1.564325 2 30832 12.547439 1.402443 3 7976 5.708052 1.596152 2 14602 4.558025 0.375806 2 41571 11.642307 0.438553 3 55028 3.222443 0.121399 1 5837 4.736156 0.029871 2 39808 10.839526 0.836323 3 20944 4.194791 0.235483 2 22146 14.936259 0.888582 3 42169 3.310699 1.521855 1 7010 2.971931 0.034321 2 3807 9.261667 0.537807 2 29241 7.791833 1.111416 3 52696 1.480470 1.028750 1 42545 3.677287 0.244167 1 24437 2.202967 1.370399 1 16037 5.796735 0.935893 2 8493 3.063333 0.144089 2 68080 11.233094 0.492487 1 59016 1.965570 0.005697 1 11810 8.616719 0.137419 2 68630 6.609989 1.083505 1 7629 1.712639 1.086297 2 71992 10.117445 1.299319 1 13398 0.000000 1.104178 2 26241 9.824777 1.346821 3 11160 1.653089 0.980949 2 76701 18.178822 1.473671 1 32174 6.781126 0.885340 3 45043 8.206750 1.549223 3 42173 10.081853 1.376745 3 69801 6.288742 0.112799 1 41737 3.695937 1.543589 1 46979 6.726151 1.069380 3 79267 12.969999 1.568223 1 4615 2.661390 1.531933 2 32907 7.072764 1.117386 3 37444 9.123366 1.318988 3 569 3.743946 1.039546 2 8723 2.341300 0.219361 2 6024 0.541913 0.592348 2 52252 2.310828 1.436753 1 8358 6.226597 1.427316 2 26166 7.277876 0.489252 3 18471 0.000000 0.389459 2 3386 7.218221 1.098828 2 41544 8.777129 1.111464 3 10480 2.813428 0.819419 2 5894 2.268766 1.412130 2 7273 6.283627 0.571292 2 22272 7.520081 1.626868 3 31369 11.739225 0.027138 3 10708 3.746883 0.877350 2 69364 12.089835 0.521631 1 37760 12.310404 0.259339 3 13004 0.000000 0.671355 2 37885 2.728800 0.331502 1 52555 10.814342 0.607652 3 38997 12.170268 0.844205 3 69698 6.698371 0.240084 1 11783 3.632672 1.643479 2 47636 10.059991 0.892361 3 15744 1.887674 0.756162 2 69058 8.229125 0.195886 1 33057 7.817082 0.476102 3 28681 12.277230 0.076805 3 34042 10.055337 1.115778 3 29928 3.596002 1.485952 1 9734 2.755530 1.420655 2 7344 7.780991 0.513048 2 7387 0.093705 0.391834 2 33957 8.481567 0.520078 3 9936 3.865584 0.110062 2 36094 9.683709 0.779984 3 39835 10.617255 1.359970 3 64486 7.203216 1.624762 1 0 7.601414 1.215605 2 39539 1.386107 1.417070 1 66972 9.129253 0.594089 1 15029 1.363447 0.620841 2 44909 3.181399 0.359329 1 38183 13.365414 0.217011 3 37372 4.207717 1.289767 1 0 4.088395 0.870075 2 17786 3.327371 1.142505 2 39055 1.303323 1.235650 1 37045 7.999279 1.581763 3 6435 2.217488 0.864536 2 72265 7.751808 0.192451 1 28152 14.149305 1.591532 3 25931 8.765721 0.152808 3 7538 3.408996 0.184896 2 1315 1.251021 0.112340 2 12292 6.160619 1.537165 2 49248 1.034538 1.585162 1 9025 0.000000 1.034635 2 13438 2.355051 0.542603 2 69683 6.614543 0.153771 1 25374 10.245062 1.450903 3 55264 3.467074 1.231019 1 38324 7.487678 1.572293 3 69643 4.624115 1.185192 1 44058 8.995957 1.436479 3 41316 11.564476 0.007195 3 29119 3.440948 0.078331 1 51656 1.673603 0.732746 1 3030 4.719341 0.699755 2 35695 10.304798 1.576488 3 1537 2.086915 1.199312 2 9083 6.338220 1.131305 2 47744 8.254926 0.710694 3 71372 16.067108 0.974142 1 37980 1.723201 0.310488 1 42385 3.785045 0.876904 1 22687 2.557561 0.123738 1 39512 9.852220 1.095171 3 11885 3.679147 1.557205 2 4944 9.789681 0.852971 2 73230 14.958998 0.526707 1 17585 11.182148 1.288459 3 68737 7.528533 1.657487 1 13818 5.253802 1.378603 2 31662 13.946752 1.426657 3 86686 15.557263 1.430029 1 43214 12.483550 0.688513 3 24091 2.317302 1.411137 1 52544 10.069724 0.766119 3 61861 5.792231 1.615483 1 47903 4.138435 0.475994 1 37190 12.929517 0.304378 3 6013 9.378238 0.307392 2 27223 8.361362 1.643204 3 69027 7.939406 1.325042 1 78642 10.735384 0.705788 1 30254 11.592723 0.286188 3 21704 10.098356 0.704748 3 34985 9.299025 0.545337 3 31316 11.158297 0.218067 3 76368 16.143900 0.558388 1 27953 10.971700 1.221787 3 152 0.000000 0.681478 2 9146 3.178961 1.292692 2 75346 17.625350 0.339926 1 26376 1.995833 0.267826 1 35255 10.640467 0.416181 3 19198 9.628339 0.985462 3 12518 4.662664 0.495403 2 25453 5.754047 1.382742 2 12530 0.000000 0.037146 2 62230 9.334332 0.198118 1 9517 3.846162 0.619968 2 71161 10.685084 0.678179 1 1593 4.752134 0.359205 2 33794 0.697630 0.966786 1 39710 10.365836 0.505898 3 16941 0.461478 0.352865 2 69209 11.339537 1.068740 1 4446 5.420280 0.127310 2 9347 3.469955 1.619947 2 55635 8.517067 0.994858 3 65889 8.306512 0.413690 1 10753 2.628690 0.444320 2 7055 0.000000 0.802985 2 7905 0.000000 1.170397 2 53447 7.298767 1.582346 3 9194 7.331319 1.277988 2 61914 9.392269 0.151617 1 15630 5.541201 1.180596 2 79194 15.149460 0.537540 1 12268 5.515189 0.250562 2 33682 7.728898 0.920494 3 26080 11.318785 1.510979 3 19119 3.574709 1.531514 2 30902 7.350965 0.026332 3 63039 7.122363 1.630177 1 51136 1.828412 1.013702 1 35262 10.117989 1.156862 3 42776 11.309897 0.086291 3 64191 8.342034 1.388569 1 15436 0.241714 0.715577 2 14402 10.482619 1.694972 2 6341 9.289510 1.428879 2 14113 4.269419 0.134181 2 6390 0.000000 0.189456 2 8794 0.817119 0.143668 2 43432 1.508394 0.652651 1 38334 9.359918 0.052262 3 34068 10.052333 0.550423 3 30819 11.111660 0.989159 3 22239 11.265971 0.724054 3 28725 10.383830 0.254836 3 57071 3.878569 1.377983 1 72420 13.679237 0.025346 1 28294 10.526846 0.781569 3 9896 0.000000 0.924198 2 65821 4.106727 1.085669 1 7645 8.118856 1.470686 2 71289 7.796874 0.052336 1 5128 2.789669 1.093070 2 13711 6.226962 0.287251 2 22240 10.169548 1.660104 3 15092 0.000000 1.370549 2 5017 7.513353 0.137348 2 10141 8.240793 0.099735 2 35570 14.612797 1.247390 3 46893 3.562976 0.445386 1 8178 3.230482 1.331698 2 55783 3.612548 1.551911 1 1148 0.000000 0.332365 2 10062 3.931299 0.487577 2 74124 14.752342 1.155160 1 66603 10.261887 1.628085 1 11893 2.787266 1.570402 2 50908 15.112319 1.324132 3 39891 5.184553 0.223382 3 65915 3.868359 0.128078 1 65678 3.507965 0.028904 1 62996 11.019254 0.427554 1 36851 3.812387 0.655245 1 36669 11.056784 0.378725 3 38876 8.826880 1.002328 3 26878 11.173861 1.478244 3 46246 11.506465 0.421993 3 12761 7.798138 0.147917 3 35282 10.155081 1.370039 3 68306 10.645275 0.693453 1 31262 9.663200 1.521541 3 34754 10.790404 1.312679 3 13408 2.810534 0.219962 2 30365 9.825999 1.388500 3 10709 1.421316 0.677603 2 24332 11.123219 0.809107 3 45517 13.402206 0.661524 3 6178 1.212255 0.836807 2 10639 1.568446 1.297469 2 29613 3.343473 1.312266 1 22392 5.400155 0.193494 1 51126 3.818754 0.590905 1 53644 7.973845 0.307364 3 51417 9.078824 0.734876 3 24859 0.153467 0.766619 1 61732 8.325167 0.028479 1 71128 7.092089 1.216733 1 27276 5.192485 1.094409 3 30453 10.340791 1.087721 3 18670 2.077169 1.019775 2 70600 10.151966 0.993105 1 12683 0.046826 0.809614 2 81597 11.221874 1.395015 1 69959 14.497963 1.019254 1 8124 3.554508 0.533462 2 18867 3.522673 0.086725 2 80886 14.531655 0.380172 1 55895 3.027528 0.885457 1 31587 1.845967 0.488985 1 10591 10.226164 0.804403 3 70096 10.965926 1.212328 1 53151 2.129921 1.477378 1 11992 0.000000 1.606849 2 33114 9.489005 0.827814 3 7413 0.000000 1.020797 2 10583 0.000000 1.270167 2 58668 6.556676 0.055183 1 35018 9.959588 0.060020 3 70843 7.436056 1.479856 1 14011 0.404888 0.459517 2 35015 9.952942 1.650279 3 70839 15.600252 0.021935 1 3024 2.723846 0.387455 2 5526 0.513866 1.323448 2 5113 0.000000 0.861859 2 20851 7.280602 1.438470 2 40999 9.161978 1.110180 3 15823 0.991725 0.730979 2 35432 7.398380 0.684218 3 53711 12.149747 1.389088 3 64371 9.149678 0.874905 1 9289 9.666576 1.370330 2 60613 3.620110 0.287767 1 18338 5.238800 1.253646 2 22845 14.715782 1.503758 3 74676 14.445740 1.211160 1 34143 13.609528 0.364240 3 14153 3.141585 0.424280 2 9327 0.000000 0.120947 2 18991 0.454750 1.033280 2 9193 0.510310 0.016395 2 2285 3.864171 0.616349 2 9493 6.724021 0.563044 2 2371 4.289375 0.012563 2 13963 0.000000 1.437030 2 2299 3.733617 0.698269 2 5262 2.002589 1.380184 2 4659 2.502627 0.184223 2 17582 6.382129 0.876581 2 27750 8.546741 0.128706 3 9868 2.694977 0.432818 2 18333 3.951256 0.333300 2 3780 9.856183 0.329181 2 18190 2.068962 0.429927 2 11145 3.410627 0.631838 2 68846 9.974715 0.669787 1 26575 10.650102 0.866627 3 48111 9.134528 0.728045 3 43757 7.882601 1.332446 3
第2步.准备数据：使用Python解析文本文件,文件名kNN.py
from numpy import * #导入科学计算包 import operator #运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数 #文件转矩阵函数开始 def file2matrix(filename): fr=open(filename) #读取文档 arraylines=fr.readlines() #读取每行，结果为列表格式 lengthlines=len(arraylines) #获取列表长度，相当于文档行数 mats=zeros((lengthlines,3)) #创建一个以0填充的矩阵：（lengthlines行，3列） classLabelVector = [] #创建分类标签列表 i=0 for line in arraylines: #处理列表 line=line.strip() #去除每行列表两边空格、回车等 listFromLine=line.split(' ') #用Tab键 分割列表为：[40920 8.326976 0.953952 largeDoses]（ 千万别写错/t，否则报错：“could not convert string to float:”） mats[i,:]=listFromLine[0:3] #把列表前3个数字填入mats的矩阵：[40920 8.326976 0.953952] classLabelVector.append(int(listFromLine[-1])) #把列表最后一项添加入classLabelVector列表，顺序为先进排在前面，后进排后面（如果用文件datingTestSet.txt，则原int(listFromLine[-1])去除int，否则报错） i+=1 return mats,classLabelVector #返回训练矩阵，和对应的分类标签
运行：可以再最下面加入：
if __name__=='__main__': #groups,labels=createDataSet() #print(classify0([1,0], groups, labels, 2)) a,b=file2matrix('datingTestSet2.txt') print(a) print(b)
运行2：或在命令窗输入：import kNN
>>> import kNN
>>> reload(kNN) #kNN有变化时，重加载 >>> datingDataMat, datingLabels = kNN.file2matrix('datingTestSet2.txt')
第3步. 分析数据：使用Matplotlib画二维扩散图
3.1 在KNN.py，和datingTestSet.txt 文件夹内，运行命令窗口：
>>> import kNN >>> datingDataMat, datingLabels = kNN.file2matrix('datingTestSet.txt') >>> import matplotlib >>> import matplotlib.pyplot as plt >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.scatter(datingDataMat[:,1], datingDataMat[:,2]) >>> plt.show()
得到下图：（x：玩视频游戏所耗时间百分比，y：每周所消费的冰淇淋公升数）
3.2 由于没有使用样本分类的特征值，我们很难从图2-3中看到任何有用的数据模式信息。一般来说，我们会采用色彩或其他的记号来标记不同样本分类，以便更好地理解数据信息。
在文件夹下新建一个名为test.py的文件，输入如下代码：
import knn,matplotlib import matplotlib.pyplot as plt from numpy import array datingDataMat, datingLabels = knn.file2matrix('datingTestSet.txt') fig = plt.figure() ax = fig.add_subplot(111) #ax.scatter(datingDataMat[:,1], datingDataMat[:,2])#没有分类信息的绘图 #以下for循环用于把分类字符串转换成数字：largeDoses=3,smallDoses=2,didntLike=1,（如果用文件datingTestSet2.txt则不用此循环） labels=[] for label in datingLabels: if label=='largeDoses': labels.append(3) elif label=='smallDoses': labels.append(2) else: labels.append(1) #以下for循环用于绘图，包含分类信息 ax.scatter(datingDataMat[:,1], datingDataMat[:,2],15.0*array(labels), 15.0*array(labels)) plt.show()
上代码将绘制如下图：
代码解释：上述代码利用变量datingLabels存储的类标签属性，在散点图上绘制了色彩不等、尺寸不同的点。
4.准备数据：归一化数值
表2-3】约会网站原始数据改进之后的样本数据：
【公式：计算两样本间距离公式】：
多个样本同样适用：
例】对于本例，计算样本3、4间距离是：
为什么要数值规一】：上面方程中数字差值最大的属性对计算结果的影响最大，也就是说，每年获取的飞行常客里程数对于计算结果的影响将远远大于表2-3中其他两个特征——玩视频游戏的和每周消费冰淇淋公升数——的影响。而产生这种现象的唯一原因，仅仅是因为飞行常客里程数远大于其他特征值。但海伦认为这三种特征是同等重要的，因此作为三个等权重的特征之一，飞行常客里程数并不应该如此严重地影响到计算结果。
常用规一方式】我们通常采用的方法是将数值归一化，如将取值范围处理为0到1或者-1到1之间。
【规一公式】：newValue = (oldValue-min)/(max-min)
规一公式解释：
min：数据集中的 每一列 的 最小特征值，
max：数据集中的 每一列 的 最大特征值
newValue：要求的最终规一值
oldvalue：要换成规一值的值
最终函数如下，也写在knn.py里：
from numpy import * #导入科学计算包 import operator #运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数 #规一化大数值函数：用于处理部分数值太大情形 def autoNorm(dataSet): minVals = dataSet.min(0) #返回每一列的最小值组成的列表，结果[0. 0. 0.00156] maxVals = dataSet.max(0) #返回每一列的最大值组成的列表，结果[9.1273000e+04 2.0919349e+01 1.6955170e+00](e+04表示：9.1273*1000) ranges = maxVals - minVals #返回最大-最小差值，结果[9.1273000e+04 2.0919349e+01 1.6943610e+00] normDataSet = zeros(shape(dataSet)) #=zero(shape(1000,3))=1000行3列以0填充的矩阵 m = dataSet.shape[0] #获取维度的第一个数据即行数，m=1000 normDataSet = dataSet - tile(minVals, (m, 1)) #原数矩阵-最小数集矩阵（把最小列表转换成1000行1列与输入集一样的矩阵后才能运算） normDataSet = normDataSet / tile(ranges, (m, 1)) # ❶ 特征值相除 （完成规一运算(oldValue-min)/(max-min)） return normDataSet, ranges, minVals #返回：规一值，(max-min)，最小值 #运行函数部分 if __name__=='__main__': datingDataMat, datingLabels = file2matrix('datingTestSet.txt') normMat, ranges, minVals = autoNorm(datingDataMat)
代码解释：也可以只返回normMat矩阵，但是下一节我们将需要取值范围和最小值归一化测试数据
运行2：或在命令窗口进入knn.py所在目录后运行（记得注释掉if __name__..：以下的内容）：
>>> reload(kNN) >>> normMat, ranges, minVals = kNN.autoNorm(datingDataMat) >>> normMat array([[ 0.33060119, 0.58918886, 0.69043973], [ 0.49199139, 0.50262471, 0.13468257], [ 0.34858782, 0.68886842, 0.59540619], ..., [ 0.93077422, 0.52696233, 0.58885466], [ 0.76626481, 0.44109859, 0.88192528], [ 0.0975718 , 0.02096883, 0.02443895]]) >>> ranges array([ 8.78430000e+04, 2.02823930e+01, 1.69197100e+00]) >>> minVals array([ 0. , 0. , 0.001818])
5.测试算法：作为完整程序验证分类器
程序如下：
#以下函数：找一些数据进行算法测试，算出错误率 def datingClassTest(): hoRatio = 0.1 #取出 10% datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #调用之前写的函数将数据转矩阵 normMat, ranges, minVals = autoNorm(datingDataMat) #将数值规一化 m = normMat.shape[0] #求出数值行数，1000 numTestVecs = int(m*hoRatio) #取出百分之十用于测试，1000*0.1=100 errorCount = 0.0 #用于计算错误总数 for i in range(numTestVecs): #循环100个 classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3) #k值函数（测试集0-99行所有列，学习集100-最后一行所有列，标签从100行开始，k=3 print ("the classifier came back with: %s, the real answer is: %s" % (classifierResult, datingLabels[i])) #括号内（函数算出的结果，标签实际结果） if (classifierResult != datingLabels[i]): errorCount += 1.0 #如果学习结果!= 实际结果，错误+1 print ("the total error rate is: %f" % (errorCount/float(numTestVecs))) #错误比率=错误数/总测试数 print (errorCount) #输出错误数
运行方法1：在最后加入如下代码：
if __name__=='__main__': datingClassTest()
运行方法2：在文件夹内打开命令窗口：
import knn >>> knn.datingClassTest()
6.使用算法：构建完整可用系统
最后总结综合一下函数：
from numpy import * #导入科学计算包 import operator #运算符模块，k近邻算法执行排序操作时将使用这个模块提供的函数 def createDataSet(): group=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels=['A','A','B','B'] return group,labels #k近邻分类函数 def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0]#求数据集的维度 diffMat = tile(inX, (dataSetSize,1)) - dataSet sqDiffMat = diffMat**2 sqDistances = sqDiffMat.sum(axis=1) #axis=0是按照行求和，axis=1是按照列进行求和 distances = sqDistances**0.5 #开根号 sortedDistIndicies = distances.argsort()#把向量中每个元素进行排序，结果是元素的索引形成的向量 classCount={} #❷ （以下两行）选择距离最小的k个点 for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #❸ 排序。 3.5以上版本，原classCount.iteritems()变为classCount.items() sortedClassCount = sorted(classCount.items(),key=operator.itemgetter(1), reverse=True) return sortedClassCount[0][0] #文件转矩阵函数开始 def file2matrix(filename): fr=open(filename) #读取文档 arraylines=fr.readlines() #读取每行，结果为列表格式 lengthlines=len(arraylines) #获取列表长度，相当于文档行数 mats=zeros((lengthlines,3)) #创建一个以0填充的矩阵：（lengthlines行，3列） classLabelVector = [] #创建分类标签列表 i=0 for line in arraylines: #处理列表 line=line.strip() #去除每行列表两边空格、回车等 listFromLine=line.split(' ') #用Tab键 分割列表为：[40920 8.326976 0.953952 largeDoses]（ 千万别写错/t，否则报错：“could not convert string to float:”） mats[i,:]=listFromLine[0:3] #把列表前3个数字填入mats的矩阵：[40920 8.326976 0.953952] #if listFromLine[-1]=='largeDoses': classLabelVector.append(int(listFromLine[-1])) #把列表最后一项添加入classLabelVector列表，顺序为先进排在前面，后进排后面（原int()去除，否则报错） i+=1 # # 以下for循环用于把分类字符串转换成数字：largeDoses=3,smallDoses=2,didntLike=1（用于兼容py3.x以后版本字符不能直接int为数字问题） # labels = [] # for label in classLabelVector: # if label == 'largeDoses': # labels.append(3) # elif label == 'smallDoses': # labels.append(2) # else: # labels.append(1) return mats,classLabelVector #返回训练矩阵，和对应的分类标签 #规一化大数值函数：用于处理部分数值太大情形 def autoNorm(dataSet): minVals = dataSet.min(0) #返回每一列的最小值组成的列表，结果[0. 0. 0.00156] maxVals = dataSet.max(0) #返回每一列的最大值组成的列表，结果[9.1273000e+04 2.0919349e+01 1.6955170e+00](e+04表示：9.1273*1000) ranges = maxVals - minVals #返回最大-最小差值，结果[9.1273000e+04 2.0919349e+01 1.6943610e+00] normDataSet = zeros(shape(dataSet)) #=zero(shape(1000,3))=1000行3列以0填充的矩阵 m = dataSet.shape[0] #获取维度的第一个数据即行数，m=1000 normDataSet = dataSet - tile(minVals, (m, 1)) #原数矩阵-最小数集矩阵（把最小列表转换成1000行1列与输入集一样的矩阵后才能运算） normDataSet = normDataSet / tile(ranges, (m, 1)) # ❶ 特征值相除 （完成规一运算(oldValue-min)/(max-min)） return normDataSet, ranges, minVals #返回：规一值，(max-min)，最小值 #以下函数：找一些数据进行算法测试，算出错误率 def datingClassTest(): hoRatio = 0.1 #取出 10% datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #调用之前写的函数将数据转矩阵 normMat, ranges, minVals = autoNorm(datingDataMat) #将数值规一化 m = normMat.shape[0] #求出数值行数，1000 numTestVecs = int(m*hoRatio) #取出百分之十用于测试，1000*0.1=100 errorCount = 0.0 #用于计算错误总数 for i in range(numTestVecs): #循环100个 classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3) #k值函数（测试集0-99行所有列，学习集100-最后一行所有列，标签从100行开始，k=3 print ("the classifier came back with: %s, the real answer is: %s" % (classifierResult, datingLabels[i])) #括号内（函数算出的结果，标签实际结果） if (classifierResult != datingLabels[i]): errorCount += 1.0 #如果学习结果!= 实际结果，错误+1 print ("the total error rate is: %f" % (errorCount/float(numTestVecs))) #错误比率=错误数/总测试数 print (errorCount) #输出错误数 #面向个人的函数 def classifyPerson(): resultList = ['一点也不喜欢','有一点点啦', '非常喜欢'] percentTats = float(input("玩电子游戏的时间百分比?")) ffMiles = float(input("每年获得的飞行常客里程?")) iceCream = float(input("每年消耗的冰淇淋升数?")) datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') normMat, ranges, minVals = autoNorm(datingDataMat) inArr = array([ffMiles, percentTats, iceCream]) classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels,3) print ("你可能喜欢这个人: ",resultList[classifierResult - 1]) if __name__=='__main__': classifyPerson() # matss,labelss=file2matrix('datingTestSet.txt') # print(matss,labelss) #datingClassTest() # datingDataMat, datingLabels = file2matrix('datingTestSet.txt') # normMat, ranges, minVals = autoNorm(datingDataMat) # #groups,labels=createDataSet() # #print(classify0([1,0], groups, labels, 2)) # # a,b=file2matrix('datingTestSet.txt') # print(a) # print(b)
运行：
runfile('C:/Users/Administrator/Desktop/机学-pdf/机器学习实战/knn.py', wdir='C:/Users/Administrator/Desktop/机学-pdf/机器学习实战') 玩电子游戏的时间百分比?>? 50 每年获得的飞行常客里程?>? 90000 每年消耗的冰淇淋升数?>? 800 你可能喜欢这个人: 非常喜欢
目录内容：
