标签：rate from create err turn 矩阵 EDA pre ini
import numpy as np import matplotlib.pyplot as plt import pandas as pd def CreateDataSet(): data = np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels = np.array([‘A‘,‘A‘,‘B‘,‘B‘]) return data,labels data,labels = CreateDataSet() print(data) print(labels)
plt.figure() plt.scatter(data[:,0],data[:,1],c="b") for i in range(data.shape[0]): plt.text(data[i,0]+0.02,data[i,1],labels[i]) plt.show()
def KNNClassfy(preData,dataSet,labels,k): distance = np.sum(np.power(dataSet - preData,2),1) #注意：这里我们不进行开方，可以少算一次 sortDistIdx = np.argsort(distance,0) #小到大排序，获取索引 labels_idx = {} for i in range(k): #获取分类 idx = sortDistIdx[i] #获取索引 label = labels[idx] #获取标签 labels_idx[label] = labels_idx.get(label,0)+1 labels_sort = sorted(labels_idx.items(),key=lambda x:x[1],reverse=True) return labels_sort[0][0] #获取最大可能分类
preData = np.array([0,0.3]) preLab = KNNClassfy(preData,data,labels,3) print(preLab)
40920 8.326976 0.953952 3 14488 7.153469 1.673904 2 26052 1.441871 0.805124 1 75136 13.147394 0.428964 1 38344 1.669788 0.134296 1 72993 10.141740 1.032955 1 35948 6.830792 1.213192 3 42666 13.276369 0.543880 3 67497 8.631577 0.749278 1 35483 12.273169 1.508053 3 50242 3.723498 0.831917 1 63275 8.385879 1.669485 1 5569 4.875435 0.728658 2 51052 4.680098 0.625224 1 77372 15.299570 0.331351 1 43673 1.889461 0.191283 1 61364 7.516754 1.269164 1 69673 14.239195 0.261333 1 15669 0.000000 1.250185 2 28488 10.528555 1.304844 3 6487 3.540265 0.822483 2 37708 2.991551 0.833920 1 22620 5.297865 0.638306 2 28782 6.593803 0.187108 3 19739 2.816760 1.686209 2 36788 12.458258 0.649617 3 5741 0.000000 1.656418 2 28567 9.968648 0.731232 3 6808 1.364838 0.640103 2 41611 0.230453 1.151996 1 36661 11.865402 0.882810 3 43605 0.120460 1.352013 1 15360 8.545204 1.340429 3 63796 5.856649 0.160006 1 10743 9.665618 0.778626 2 70808 9.778763 1.084103 1 72011 4.932976 0.632026 1 5914 2.216246 0.587095 2 14851 14.305636 0.632317 3 33553 12.591889 0.686581 3 44952 3.424649 1.004504 1 17934 0.000000 0.147573 2 27738 8.533823 0.205324 3 29290 9.829528 0.238620 3 42330 11.492186 0.263499 3 36429 3.570968 0.832254 1 39623 1.771228 0.207612 1 32404 3.513921 0.991854 1 27268 4.398172 0.975024 1 5477 4.276823 1.174874 2 14254 5.946014 1.614244 2 68613 13.798970 0.724375 1 41539 10.393591 1.663724 3 7917 3.007577 0.297302 2 21331 1.031938 0.486174 2 8338 4.751212 0.064693 2 5176 3.692269 1.655113 2 18983 10.448091 0.267652 3 68837 10.585786 0.329557 1 13438 1.604501 0.069064 2 48849 3.679497 0.961466 1 12285 3.795146 0.696694 2 7826 2.531885 1.659173 2 5565 9.733340 0.977746 2 10346 6.093067 1.413798 2 1823 7.712960 1.054927 2 9744 11.470364 0.760461 3 16857 2.886529 0.934416 2 39336 10.054373 1.138351 3 65230 9.972470 0.881876 1 2463 2.335785 1.366145 2 27353 11.375155 1.528626 3 16191 0.000000 0.605619 2 12258 4.126787 0.357501 2 42377 6.319522 1.058602 1 25607 8.680527 0.086955 3 77450 14.856391 1.129823 1 58732 2.454285 0.222380 1 46426 7.292202 0.548607 3 32688 8.745137 0.857348 3 64890 8.579001 0.683048 1 8554 2.507302 0.869177 2 28861 11.415476 1.505466 3 42050 4.838540 1.680892 1 32193 10.339507 0.583646 3 64895 6.573742 1.151433 1 2355 6.539397 0.462065 2 0 2.209159 0.723567 2 70406 11.196378 0.836326 1 57399 4.229595 0.128253 1 41732 9.505944 0.005273 3 11429 8.652725 1.348934 3 75270 17.101108 0.490712 1 5459 7.871839 0.717662 2 73520 8.262131 1.361646 1 40279 9.015635 1.658555 3 21540 9.215351 0.806762 3 17694 6.375007 0.033678 2 22329 2.262014 1.022169 1 46570 5.677110 0.709469 1 42403 11.293017 0.207976 3 33654 6.590043 1.353117 1 9171 4.711960 0.194167 2 28122 8.768099 1.108041 3 34095 11.502519 0.545097 3 1774 4.682812 0.578112 2 40131 12.446578 0.300754 3 13994 12.908384 1.657722 3 77064 12.601108 0.974527 1 11210 3.929456 0.025466 2 6122 9.751503 1.182050 3 15341 3.043767 0.888168 2 44373 4.391522 0.807100 1 28454 11.695276 0.679015 3 63771 7.879742 0.154263 1 9217 5.613163 0.933632 2 69076 9.140172 0.851300 1 24489 4.258644 0.206892 1 16871 6.799831 1.221171 2 39776 8.752758 0.484418 3 5901 1.123033 1.180352 2 40987 10.833248 1.585426 3 7479 3.051618 0.026781 2 38768 5.308409 0.030683 3 4933 1.841792 0.028099 2 32311 2.261978 1.605603 1 26501 11.573696 1.061347 3 37433 8.038764 1.083910 3 23503 10.734007 0.103715 3 68607 9.661909 0.350772 1 27742 9.005850 0.548737 3 11303 0.000000 0.539131 2 0 5.757140 1.062373 2 32729 9.164656 1.624565 3 24619 1.318340 1.436243 1 42414 14.075597 0.695934 3 20210 10.107550 1.308398 3 33225 7.960293 1.219760 3 54483 6.317292 0.018209 1 18475 12.664194 0.595653 3 33926 2.906644 0.581657 1 43865 2.388241 0.913938 1 26547 6.024471 0.486215 3 44404 7.226764 1.255329 3 16674 4.183997 1.275290 2 8123 11.850211 1.096981 3 42747 11.661797 1.167935 3 56054 3.574967 0.494666 1 10933 0.000000 0.107475 2 18121 7.937657 0.904799 3 11272 3.365027 1.014085 2 16297 0.000000 0.367491 2 28168 13.860672 1.293270 3 40963 10.306714 1.211594 3 31685 7.228002 0.670670 3 55164 4.508740 1.036192 1 17595 0.366328 0.163652 2 1862 3.299444 0.575152 2 57087 0.573287 0.607915 1 63082 9.183738 0.012280 1 51213 7.842646 1.060636 3 6487 4.750964 0.558240 2 4805 11.438702 1.556334 3 30302 8.243063 1.122768 3 68680 7.949017 0.271865 1 17591 7.875477 0.227085 2 74391 9.569087 0.364856 1 37217 7.750103 0.869094 3 42814 0.000000 1.515293 1 14738 3.396030 0.633977 2 19896 11.916091 0.025294 3 14673 0.460758 0.689586 2 32011 13.087566 0.476002 3 58736 4.589016 1.672600 1 54744 8.397217 1.534103 1 29482 5.562772 1.689388 1 27698 10.905159 0.619091 3 11443 1.311441 1.169887 2 56117 10.647170 0.980141 3 39514 0.000000 0.481918 1 26627 8.503025 0.830861 3 16525 0.436880 1.395314 2 24368 6.127867 1.102179 1 22160 12.112492 0.359680 3 6030 1.264968 1.141582 2 6468 6.067568 1.327047 2 22945 8.010964 1.681648 3 18520 3.791084 0.304072 2 34914 11.773195 1.262621 3 6121 8.339588 1.443357 2 38063 2.563092 1.464013 1 23410 5.954216 0.953782 1 35073 9.288374 0.767318 3 52914 3.976796 1.043109 1 16801 8.585227 1.455708 3 9533 1.271946 0.796506 2 16721 0.000000 0.242778 2 5832 0.000000 0.089749 2 44591 11.521298 0.300860 3 10143 1.139447 0.415373 2 21609 5.699090 1.391892 2 23817 2.449378 1.322560 1 15640 0.000000 1.228380 2 8847 3.168365 0.053993 2 50939 10.428610 1.126257 3 28521 2.943070 1.446816 1 32901 10.441348 0.975283 3 42850 12.478764 1.628726 3 13499 5.856902 0.363883 2 40345 2.476420 0.096075 1 43547 1.826637 0.811457 1 70758 4.324451 0.328235 1 19780 1.376085 1.178359 2 44484 5.342462 0.394527 1 54462 11.835521 0.693301 3 20085 12.423687 1.424264 3 42291 12.161273 0.071131 3 47550 8.148360 1.649194 3 11938 1.531067 1.549756 2 40699 3.200912 0.309679 1 70908 8.862691 0.530506 1 73989 6.370551 0.369350 1 11872 2.468841 0.145060 2 48463 11.054212 0.141508 3 15987 2.037080 0.715243 2 70036 13.364030 0.549972 1 32967 10.249135 0.192735 3 63249 10.464252 1.669767 1 42795 9.424574 0.013725 3 14459 4.458902 0.268444 2 19973 0.000000 0.575976 2 5494 9.686082 1.029808 3 67902 13.649402 1.052618 1 25621 13.181148 0.273014 3 27545 3.877472 0.401600 1 58656 1.413952 0.451380 1 7327 4.248986 1.430249 2 64555 8.779183 0.845947 1 8998 4.156252 0.097109 2 11752 5.580018 0.158401 2 76319 15.040440 1.366898 1 27665 12.793870 1.307323 3 67417 3.254877 0.669546 1 21808 10.725607 0.588588 3 15326 8.256473 0.765891 2 20057 8.033892 1.618562 3 79341 10.702532 0.204792 1 15636 5.062996 1.132555 2 35602 10.772286 0.668721 3 28544 1.892354 0.837028 1 57663 1.019966 0.372320 1 78727 15.546043 0.729742 1 68255 11.638205 0.409125 1 14964 3.427886 0.975616 2 21835 11.246174 1.475586 3 7487 0.000000 0.645045 2 8700 0.000000 1.424017 2 26226 8.242553 0.279069 3 65899 8.700060 0.101807 1 6543 0.812344 0.260334 2 46556 2.448235 1.176829 1 71038 13.230078 0.616147 1 47657 0.236133 0.340840 1 19600 11.155826 0.335131 3 37422 11.029636 0.505769 3 1363 2.901181 1.646633 2 26535 3.924594 1.143120 1 47707 2.524806 1.292848 1 38055 3.527474 1.449158 1 6286 3.384281 0.889268 2 10747 0.000000 1.107592 2 44883 11.898890 0.406441 3 56823 3.529892 1.375844 1 68086 11.442677 0.696919 1 70242 10.308145 0.422722 1 11409 8.540529 0.727373 2 67671 7.156949 1.691682 1 61238 0.720675 0.847574 1 17774 0.229405 1.038603 2 53376 3.399331 0.077501 1 30930 6.157239 0.580133 1 28987 1.239698 0.719989 1 13655 6.036854 0.016548 2 7227 5.258665 0.933722 2 40409 12.393001 1.571281 3 13605 9.627613 0.935842 2 26400 11.130453 0.597610 3 13491 8.842595 0.349768 3 30232 10.690010 1.456595 3 43253 5.714718 1.674780 3 55536 3.052505 1.335804 1 8807 0.000000 0.059025 2 25783 9.945307 1.287952 3 22812 2.719723 1.142148 1 77826 11.154055 1.608486 1 38172 2.687918 0.660836 1 31676 10.037847 0.962245 3 74038 12.404762 1.112080 1 44738 10.237305 0.633422 3 17410 4.745392 0.662520 2 5688 4.639461 1.569431 2 36642 3.149310 0.639669 1 29956 13.406875 1.639194 3 60350 6.068668 0.881241 1 23758 9.477022 0.899002 3 25780 3.897620 0.560201 2 11342 5.463615 1.203677 2 36109 3.369267 1.575043 1 14292 5.234562 0.825954 2 11160 0.000000 0.722170 2 23762 12.979069 0.504068 3 39567 5.376564 0.557476 1 25647 13.527910 1.586732 3 14814 2.196889 0.784587 2 73590 10.691748 0.007509 1 35187 1.659242 0.447066 1 49459 8.369667 0.656697 3 31657 13.157197 0.143248 3 6259 8.199667 0.908508 2 33101 4.441669 0.439381 3 27107 9.846492 0.644523 3 17824 0.019540 0.977949 2 43536 8.253774 0.748700 3 67705 6.038620 1.509646 1 35283 6.091587 1.694641 3 71308 8.986820 1.225165 1 31054 11.508473 1.624296 3 52387 8.807734 0.713922 3 40328 0.000000 0.816676 1 34844 8.889202 1.665414 3 11607 3.178117 0.542752 2 64306 7.013795 0.139909 1 32721 9.605014 0.065254 3 33170 1.230540 1.331674 1 37192 10.412811 0.890803 3 13089 0.000000 0.567161 2 66491 9.699991 0.122011 1 15941 0.000000 0.061191 2 4272 4.455293 0.272135 2 48812 3.020977 1.502803 1 28818 8.099278 0.216317 3 35394 1.157764 1.603217 1 71791 10.105396 0.121067 1 40668 11.230148 0.408603 3 39580 9.070058 0.011379 3 11786 0.566460 0.478837 2 19251 0.000000 0.487300 2 56594 8.956369 1.193484 3 54495 1.523057 0.620528 1 11844 2.749006 0.169855 2 45465 9.235393 0.188350 3 31033 10.555573 0.403927 3 16633 6.956372 1.519308 2 13887 0.636281 1.273984 2 52603 3.574737 0.075163 1 72000 9.032486 1.461809 1 68497 5.958993 0.023012 1 35135 2.435300 1.211744 1 26397 10.539731 1.638248 3 7313 7.646702 0.056513 2 91273 20.919349 0.644571 1 24743 1.424726 0.838447 1 31690 6.748663 0.890223 3 15432 2.289167 0.114881 2 58394 5.548377 0.402238 1 33962 6.057227 0.432666 1 31442 10.828595 0.559955 3 31044 11.318160 0.271094 3 29938 13.265311 0.633903 3 9875 0.000000 1.496715 2 51542 6.517133 0.402519 3 11878 4.934374 1.520028 2 69241 10.151738 0.896433 1 37776 2.425781 1.559467 1 68997 9.778962 1.195498 1 67416 12.219950 0.657677 1 59225 7.394151 0.954434 1 29138 8.518535 0.742546 3 5962 2.798700 0.662632 2 10847 0.637930 0.617373 2 70527 10.750490 0.097415 1 9610 0.625382 0.140969 2 64734 10.027968 0.282787 1 25941 9.817347 0.364197 3 2763 0.646828 1.266069 2 55601 3.347111 0.914294 1 31128 11.816892 0.193798 3 5181 0.000000 1.480198 2 69982 10.945666 0.993219 1 52440 10.244706 0.280539 3 57350 2.579801 1.149172 1 57869 2.630410 0.098869 1 56557 11.746200 1.695517 3 42342 8.104232 1.326277 3 15560 12.409743 0.790295 3 34826 12.167844 1.328086 3 8569 3.198408 0.299287 2 77623 16.055513 0.541052 1 78184 7.138659 0.158481 1 7036 4.831041 0.761419 2 69616 10.082890 1.373611 1 21546 10.066867 0.788470 3 36715 8.129538 0.329913 3 20522 3.012463 1.138108 2 42349 3.720391 0.845974 1 9037 0.773493 1.148256 2 26728 10.962941 1.037324 3 587 0.177621 0.162614 2 48915 3.085853 0.967899 1 9824 8.426781 0.202558 2 4135 1.825927 1.128347 2 9666 2.185155 1.010173 2 59333 7.184595 1.261338 1 36198 0.000000 0.116525 1 34909 8.901752 1.033527 3 47516 2.451497 1.358795 1 55807 3.213631 0.432044 1 14036 3.974739 0.723929 2 42856 9.601306 0.619232 3 64007 8.363897 0.445341 1 59428 6.381484 1.365019 1 13730 0.000000 1.403914 2 41740 9.609836 1.438105 3 63546 9.904741 0.985862 1 30417 7.185807 1.489102 3 69636 5.466703 1.216571 1 64660 0.000000 0.915898 1 14883 4.575443 0.535671 2 7965 3.277076 1.010868 2 68620 10.246623 1.239634 1 8738 2.341735 1.060235 2 7544 3.201046 0.498843 2 6377 6.066013 0.120927 2 36842 8.829379 0.895657 3 81046 15.833048 1.568245 1 67736 13.516711 1.220153 1 32492 0.664284 1.116755 1 39299 6.325139 0.605109 3 77289 8.677499 0.344373 1 33835 8.188005 0.964896 3 71890 9.414263 0.384030 1 32054 9.196547 1.138253 3 38579 10.202968 0.452363 3 55984 2.119439 1.481661 1 72694 13.635078 0.858314 1 42299 0.083443 0.701669 1 26635 9.149096 1.051446 3 8579 1.933803 1.374388 2 37302 14.115544 0.676198 3 22878 8.933736 0.943352 3 4364 2.661254 0.946117 2 4985 0.988432 1.305027 2 37068 2.063741 1.125946 1 41137 2.220590 0.690754 1 67759 6.424849 0.806641 1 11831 1.156153 1.613674 2 34502 3.032720 0.601847 1 4088 3.076828 0.952089 2 15199 0.000000 0.318105 2 17309 7.750480 0.554015 3 42816 10.958135 1.482500 3 43751 10.222018 0.488678 3 58335 2.367988 0.435741 1 75039 7.686054 1.381455 1 42878 11.464879 1.481589 3 42770 11.075735 0.089726 3 8848 3.543989 0.345853 2 31340 8.123889 1.282880 3 41413 4.331769 0.754467 3 12731 0.120865 1.211961 2 22447 6.116109 0.701523 3 33564 7.474534 0.505790 3 48907 8.819454 0.649292 3 8762 6.802144 0.615284 2 46696 12.666325 0.931960 3 36851 8.636180 0.399333 3 67639 11.730991 1.289833 1 171 8.132449 0.039062 2 26674 10.296589 1.496144 3 8739 7.583906 1.005764 2 66668 9.777806 0.496377 1 68732 8.833546 0.513876 1 69995 4.907899 1.518036 1 82008 8.362736 1.285939 1 25054 9.084726 1.606312 3 33085 14.164141 0.560970 3 41379 9.080683 0.989920 3 39417 6.522767 0.038548 3 12556 3.690342 0.462281 2 39432 3.563706 0.242019 1 38010 1.065870 1.141569 1 69306 6.683796 1.456317 1 38000 1.712874 0.243945 1 46321 13.109929 1.280111 3 66293 11.327910 0.780977 1 22730 4.545711 1.233254 1 5952 3.367889 0.468104 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 0.000000 1.679729 2 26309 2.495011 1.459589 1 33484 11.516831 0.001156 3 45944 9.213215 0.797743 3 4249 5.332865 0.109288 2 6089 0.000000 1.689771 2 7513 0.000000 1.126053 2 27862 12.640062 1.690903 3 39038 2.693142 1.317518 1 19218 3.328969 0.268271 2 62911 7.193166 1.117456 1 77758 6.615512 1.521012 1 27940 8.000567 0.835341 3 2194 4.017541 0.512104 2 37072 13.245859 0.927465 3 15585 5.970616 0.813624 2 25577 11.668719 0.886902 3 8777 4.283237 1.272728 2 29016 10.742963 0.971401 3 21910 12.326672 1.592608 3 12916 0.000000 0.344622 2 10976 0.000000 0.922846 2 79065 10.602095 0.573686 1 36759 10.861859 1.155054 3 50011 1.229094 1.638690 1 1155 0.410392 1.313401 2 71600 14.552711 0.616162 1 30817 14.178043 0.616313 3 54559 14.136260 0.362388 1 29764 0.093534 1.207194 1 69100 10.929021 0.403110 1 47324 11.432919 0.825959 3 73199 9.134527 0.586846 1 44461 5.071432 1.421420 1 45617 11.460254 1.541749 3 28221 11.620039 1.103553 3 7091 4.022079 0.207307 2 6110 3.057842 1.631262 2 79016 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 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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
data = np.loadtxt("datingTestSet2.txt",delimiter=‘\t‘) #读取数据 hob_data = data[:,:-1] hob_labels = data[:,-1]
def dataNorm(data): #归一化操作 mn = np.mean(data,0) sigma = np.std(data,0,ddof=0) return (data - mn)/sigma,mn,sigma
#划分测试集和训练集数据 hoRatio = 0.4 #测试集比例 m = hob_labels.size m_test = int(hob_labels.size*hoRatio) hob_data_norm,mn,sigma = dataNorm(hob_data) #获取训练集数据 X = hob_data_norm[:m-m_test,:] y = hob_labels[:m-m_test] #获取测试集数据 X_test = hob_data_norm[m-m_test:m,:] y_test = hob_labels[m-m_test:m]
#进行测试 error_count = 0 for i in range(y_test.size): clf = KNNClassfy(X_test[i],X,y,3) if clf != y_test[i]: error_count = error_count + 1 print("{} --- {}".format(clf,y_test[i])) print("error rate is:",error_count/y_test.size)
#进行预测 resultList = [‘not at all‘,‘in small doses‘,‘in large doses‘] hb_1 = float(input("爱好一:")) hb_2 = float(input("爱好二:")) hb_3 = float(input("爱好三:")) preData = np.array([[hb_1,hb_2,hb_3]]) preData_norm = ((preData - mn)/sigma).flatten()
clf = KNNClassfy(preData_norm,hob_data_norm,hob_labels,3) print("喜欢程度:{}".format(resultList[int(clf)-1]))
from os import listdir import codecs #将每一个数字文件转换为矩阵向量 def image2Vector(filename): data = [] with codecs.open(filename,‘r‘) as fp: for i in range(32): linestr = fp.readline() #读取一行数据 for j in range(32): data.append(int(linestr[j])) #添加数据 fp.close() return np.array(data)
#获取数据集 def getDataSet(path): #读取数据 hwLabels = [] filelist = listdir(path) #获取所有文件目录 m = len(filelist) data = np.zeros((m,1024)) #先获取标签值 for i in range(m): filename = filelist[i] hwLabels.append(int(filename.split(‘_‘)[0])) #添加标签值 data[i,:] = image2Vector("%s/%s"%(path,filename)) return data,hwLabels
#获取训练集 data,labels = getDataSet("trainingDigits") #获取测试集 data_test,labels_test = getDataSet("testDigits") print(data_test.shape)
def KNNClassfy(preData,dataSet,labels,k): distance = np.sum(np.power(dataSet - preData,2),1) #注意：这里我们不进行开方，可以少算一次 sortDistIdx = np.argsort(distance,0) #小到大排序，获取索引 labels_idx = {} for i in range(k): #获取分类 idx = sortDistIdx[i] #获取索引 label = labels[idx] #获取标签 labels_idx[label] = labels_idx.get(label,0) labels_sort = sorted(labels_idx.items(),key=lambda x:x[1],reverse=True) return labels_sort[0][0] #获取最大可能分类
#进行测试 error_count = 0 for i in range(data_test.shape[0]): clf = KNNClassfy(data_test[i,:],data,labels,3) if clf != labels_test[i]: error_count += 1 print(error_count) print("{} {}".format(error_count,error_count/data_test.shape[0]))
标签：rate from create err turn 矩阵 EDA pre ini
原文地址：https://www.cnblogs.com/ssyfj/p/13045457.html
