K近邻算法讲解、python实现、k值的确定(python实现，具体代码讲解请看博主博客）

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 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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 2.68