前沿拓展：

kb3194798

微軟10月12日面向Win10 Mobile和W顯然是本次累積更新的一個漏洞，只是略有些無厘頭了。根據(jù)不同用戶的反映，無論自己電腦C盤的容量是多少，更新KB3194798后清理磁盤，Disk Cleanup都顯示產(chǎn)生了3.99 TB的可刪除文件。有用戶舉例稱，自己有三臺電腦，每臺電腦的硬盤容量都不同，但實際最后的結(jié)果都是現(xiàn)實KB31片通職始且勞高94798產(chǎn)生了3.99 TB的垃圾文件。
　　不過，雖然Disk Clea學(xué)己責(zé)律沙曲款評春括收nup回報的可刪除

1.1 工作原理

給定一個訓(xùn)練數(shù)據(jù)集，對新的輸入實例，在訓(xùn)練數(shù)據(jù)集中找到與該實例最鄰近的 k （k <= 20）個實例，這 k 個實例的多數(shù)屬于某個類，

就把該輸入實例分為這個類。

https://www.cnblogs.com/ybjourney/p/4702562.html給出的例子很形象，這里借用一下。

如下圖，綠色圓要被決定賦予哪個類，是紅色三角形還是藍色四方形？如果K=3，由于紅色三角形所占比例為2/3，綠色圓將被賦予紅色三角形那個類，

如果K=5，由于藍色四方形比例為3/5，因此綠色圓被賦予藍色四方形類。

由此也說明了KNN算法的結(jié)果很大程度取決于K的選擇。

1.2 歐氏距離公式

計算兩個向量點xA和xB之間的距離

1.3 分類決策規(guī)則（如多數(shù)表為指示函數(shù)，即當

時為 1，否則為0。

1.4 算法流程

對未知類別屬性的數(shù)據(jù)集中的每個點依次執(zhí)行以下**作：

1. 計算已知類別數(shù)據(jù)集中的點與當前點之間的距離；

2. 按照距離遞增次序排序；

3. 選取與當前點距離最小的 k 個點；

4. 確定前 k 個點所在類別的出現(xiàn)頻率；

5. 返回前 k 個點出現(xiàn)頻率最高的類別作為當前點的預(yù)測分類；

2. 代碼實現(xiàn)

python3.6

每個方法的作用，以及每行代碼的作用，同樣我都做了詳細的注解。

希望大家最好自己能實現(xiàn)一下，特別是在運算時 list，array，matrix之間的關(guān)系以及運用場景，

只有在你自己實現(xiàn)時，才能理清這三者的作用以及關(guān)系。

2.1 輸入數(shù)據(jù)

datingTestSet2.txt ：約會網(wǎng)站數(shù)據(jù)（三種類型：不喜歡的人，魅力一般的人，極具魅力的人）

1 40920 8.326976 0.953952 3
2 14488 7.153469 1.673904 2
3 26052 1.441871 0.805124 1
4 75136 13.147394 0.428964 1
5 38344 1.669788 0.134296 1
6 72993 10.141740 1.032955 1
7 35948 6.830792 1.213192 3
8 42666 13.276369 0.543880 3
9 67497 8.631577 0.749278 1
10 35483 12.273169 1.508053 3
11 50242 3.723498 0.831917 1
12 63275 8.385879 1.669485 1
13 5569 4.875435 0.728658 2
14 51052 4.680098 0.625224 1
15 77372 15.299570 0.331351 1
16 43673 1.889461 0.191283 1
17 61364 7.516754 1.269164 1
18 69673 14.239195 0.261333 1
19 15669 0.000000 1.250185 2
20 28488 10.528555 1.304844 3
21 6487 3.540265 0.822483 2
22 37708 2.991551 0.833920 1
23 22620 5.297865 0.638306 2
24 28782 6.593803 0.187108 3
25 19739 2.816760 1.686209 2
26 36788 12.458258 0.649617 3
27 5741 0.000000 1.656418 2
28 28567 9.968648 0.731232 3
29 6808 1.364838 0.640103 2
30 41611 0.230453 1.151996 1
31 36661 11.865402 0.882810 3
32 43605 0.120460 1.352013 1
33 15360 8.545204 1.340429 3
34 63796 5.856649 0.160006 1
35 10743 9.665618 0.778626 2
36 70808 9.778763 1.084103 1
37 72011 4.932976 0.632026 1
38 5914 2.216246 0.587095 2
39 14851 14.305636 0.632317 3
40 33553 12.591889 0.686581 3
41 44952 3.424649 1.004504 1
42 17934 0.000000 0.147573 2
43 27738 8.533823 0.205324 3
44 29290 9.829528 0.238620 3
45 42330 11.492186 0.263499 3
46 36429 3.570968 0.832254 1
47 39623 1.771228 0.207612 1
48 32404 3.513921 0.991854 1
49 27268 4.398172 0.975024 1
50 5477 4.276823 1.174874 2
51 14254 5.946014 1.614244 2
52 68613 13.798970 0.724375 1
53 41539 10.393591 1.663724 3
54 7917 3.007577 0.297302 2
55 21331 1.031938 0.486174 2
56 8338 4.751212 0.064693 2
57 5176 3.692269 1.655113 2
58 18983 10.448091 0.267652 3
59 68837 10.585786 0.329557 1
60 13438 1.604501 0.069064 2
61 48849 3.679497 0.961466 1
62 12285 3.795146 0.696694 2
63 7826 2.531885 1.659173 2
64 5565 9.733340 0.977746 2
65 10346 6.093067 1.413798 2
66 1823 7.712960 1.054927 2
67 9744 11.470364 0.760461 3
68 16857 2.886529 0.934416 2
69 39336 10.054373 1.138351 3
70 65230 9.972470 0.881876 1
71 2463 2.335785 1.366145 2
72 27353 11.375155 1.528626 3
73 16191 0.000000 0.605619 2
74 12258 4.126787 0.357501 2
75 42377 6.319522 1.058602 1
76 25607 8.680527 0.086955 3
77 77450 14.856391 1.129823 1
78 58732 2.454285 0.222380 1
79 46426 7.292202 0.548607 3
80 32688 8.745137 0.857348 3
81 64890 8.579001 0.683048 1
82 8554 2.507302 0.869177 2
83 28861 11.415476 1.505466 3
84 42050 4.838540 1.680892 1
85 32193 10.339507 0.583646 3
86 64895 6.573742 1.151433 1
87 2355 6.539397 0.462065 2
88 0 2.209159 0.723567 2
89 70406 11.196378 0.836326 1
90 57399 4.229595 0.128253 1
91 41732 9.505944 0.005273 3
92 11429 8.652725 1.348934 3
93 75270 17.101108 0.490712 1
94 5459 7.871839 0.717662 2
95 73520 8.262131 1.361646 1
96 40279 9.015635 1.658555 3
97 21540 9.215351 0.806762 3
98 17694 6.375007 0.033678 2
99 22329 2.262014 1.022169 1
100 46570 5.677110 0.709469 1
101 42403 11.293017 0.207976 3
102 33654 6.590043 1.353117 1
103 9171 4.711960 0.194167 2
104 28122 8.768099 1.108041 3
105 34095 11.502519 0.545097 3
106 1774 4.682812 0.578112 2
107 40131 12.446578 0.300754 3
108 13994 12.908384 1.657722 3
109 77064 12.601108 0.974527 1
110 11210 3.929456 0.025466 2
111 6122 9.751503 1.182050 3
112 15341 3.043767 0.888168 2
113 44373 4.391522 0.807100 1
114 28454 11.695276 0.679015 3
115 63771 7.879742 0.154263 1
116 9217 5.613163 0.933632 2
117 69076 9.140172 0.851300 1
118 24489 4.258644 0.206892 1
119 16871 6.799831 1.221171 2
120 39776 8.752758 0.484418 3
121 5901 1.123033 1.180352 2
122 40987 10.833248 1.585426 3
123 7479 3.051618 0.026781 2
124 38768 5.308409 0.030683 3
125 4933 1.841792 0.028099 2
126 32311 2.261978 1.605603 1
127 26501 11.573696 1.061347 3
128 37433 8.038764 1.083910 3
129 23503 10.734007 0.103715 3
130 68607 9.661909 0.350772 1
131 27742 9.005850 0.548737 3
132 11303 0.000000 0.539131 2
133 0 5.757140 1.062373 2
134 32729 9.164656 1.624565 3
135 24619 1.318340 1.436243 1
136 42414 14.075597 0.695934 3
137 20210 10.107550 1.308398 3
138 33225 7.960293 1.219760 3
139 54483 6.317292 0.018209 1
140 18475 12.664194 0.595653 3
141 33926 2.906644 0.581657 1
142 43865 2.388241 0.913938 1
143 26547 6.024471 0.486215 3
144 44404 7.226764 1.255329 3
145 16674 4.183997 1.275290 2
146 8123 11.850211 1.096981 3
147 42747 11.661797 1.167935 3
148 56054 3.574967 0.494666 1
149 10933 0.000000 0.107475 2
150 18121 7.937657 0.904799 3
151 11272 3.365027 1.014085 2
152 16297 0.000000 0.367491 2
153 28168 13.860672 1.293270 3
154 40963 10.306714 1.211594 3
155 31685 7.228002 0.670670 3
156 55164 4.508740 1.036192 1
157 17595 0.366328 0.163652 2
158 1862 3.299444 0.575152 2
159 57087 0.573287 0.607915 1
160 63082 9.183738 0.012280 1
161 51213 7.842646 1.060636 3
162 6487 4.750964 0.558240 2
163 4805 11.438702 1.556334 3
164 30302 8.243063 1.122768 3
165 68680 7.949017 0.271865 1
166 17591 7.875477 0.227085 2
167 74391 9.569087 0.364856 1
168 37217 7.750103 0.869094 3
169 42814 0.000000 1.515293 1
170 14738 3.396030 0.633977 2
171 19896 11.916091 0.025294 3
172 14673 0.460758 0.689586 2
173 32011 13.087566 0.476002 3
174 58736 4.589016 1.672600 1
175 54744 8.397217 1.534103 1
176 29482 5.562772 1.689388 1
177 27698 10.905159 0.619091 3
178 11443 1.311441 1.169887 2
179 56117 10.647170 0.980141 3
180 39514 0.000000 0.481918 1
181 26627 8.503025 0.830861 3
182 16525 0.436880 1.395314 2
183 24368 6.127867 1.102179 1
184 22160 12.112492 0.359680 3
185 6030 1.264968 1.141582 2
186 6468 6.067568 1.327047 2
187 22945 8.010964 1.681648 3
188 18520 3.791084 0.304072 2
189 34914 11.773195 1.262621 3
190 6121 8.339588 1.443357 2
191 38063 2.563092 1.464013 1
192 23410 5.954216 0.953782 1
193 35073 9.288374 0.767318 3
194 52914 3.976796 1.043109 1
195 16801 8.585227 1.455708 3
196 9533 1.271946 0.796506 2
197 16721 0.000000 0.242778 2
198 5832 0.000000 0.089749 2
199 44591 11.521298 0.300860 3
200 10143 1.139447 0.415373 2
201 21609 5.699090 1.391892 2
202 23817 2.449378 1.322560 1
203 15640 0.000000 1.228380 2
204 8847 3.168365 0.053993 2
205 50939 10.428610 1.126257 3
206 28521 2.943070 1.446816 1
207 32901 10.441348 0.975283 3
208 42850 12.478764 1.628726 3
209 13499 5.856902 0.363883 2
210 40345 2.476420 0.096075 1
211 43547 1.826637 0.811457 1
212 70758 4.324451 0.328235 1
213 19780 1.376085 1.178359 2
214 44484 5.342462 0.394527 1
215 54462 11.835521 0.693301 3
216 20085 12.423687 1.424264 3
217 42291 12.161273 0.071131 3
218 47550 8.148360 1.649194 3
219 11938 1.531067 1.549756 2
220 40699 3.200912 0.309679 1
221 70908 8.862691 0.530506 1
222 73989 6.370551 0.369350 1
223 11872 2.468841 0.145060 2
224 48463 11.054212 0.141508 3
225 15987 2.037080 0.715243 2
226 70036 13.364030 0.549972 1
227 32967 10.249135 0.192735 3
228 63249 10.464252 1.669767 1
229 42795 9.424574 0.013725 3
230 14459 4.458902 0.268444 2
231 19973 0.000000 0.575976 2
232 5494 9.686082 1.029808 3
233 67902 13.649402 1.052618 1
234 25621 13.181148 0.273014 3
235 27545 3.877472 0.401600 1
236 58656 1.413952 0.451380 1
237 7327 4.248986 1.430249 2
238 64555 8.779183 0.845947 1
239 8998 4.156252 0.097109 2
240 11752 5.580018 0.158401 2
241 76319 15.040440 1.366898 1
242 27665 12.793870 1.307323 3
243 67417 3.254877 0.669546 1
244 21808 10.725607 0.588588 3
245 15326 8.256473 0.765891 2
246 20057 8.033892 1.618562 3
247 79341 10.702532 0.204792 1
248 15636 5.062996 1.132555 2
249 35602 10.772286 0.668721 3
250 28544 1.892354 0.837028 1
251 57663 1.019966 0.372320 1
252 78727 15.546043 0.729742 1
253 68255 11.638205 0.409125 1
254 14964 3.427886 0.975616 2
255 21835 11.246174 1.475586 3
256 7487 0.000000 0.645045 2
257 8700 0.000000 1.424017 2
258 26226 8.242553 0.279069 3
259 65899 8.700060 0.101807 1
260 6543 0.812344 0.260334 2
261 46556 2.448235 1.176829 1
262 71038 13.230078 0.616147 1
263 47657 0.236133 0.340840 1
264 19600 11.155826 0.335131 3
265 37422 11.029636 0.505769 3
266 1363 2.901181 1.646633 2
267 26535 3.924594 1.143120 1
268 47707 2.524806 1.292848 1
269 38055 3.527474 1.449158 1
270 6286 3.384281 0.889268 2
271 10747 0.000000 1.107592 2
272 44883 11.898890 0.406441 3
273 56823 3.529892 1.375844 1
274 68086 11.442677 0.696919 1
275 70242 10.308145 0.422722 1
276 11409 8.540529 0.727373 2
277 67671 7.156949 1.691682 1
278 61238 0.720675 0.847574 1
279 17774 0.229405 1.038603 2
280 53376 3.399331 0.077501 1
281 30930 6.157239 0.580133 1
282 28987 1.239698 0.719989 1
283 13655 6.036854 0.016548 2
284 7227 5.258665 0.933722 2
285 40409 12.393001 1.571281 3
286 13605 9.627613 0.935842 2
287 26400 11.130453 0.597610 3
288 13491 8.842595 0.349768 3
289 30232 10.690010 1.456595 3
290 43253 5.714718 1.674780 3
291 55536 3.052505 1.335804 1
292 8807 0.000000 0.059025 2
293 25783 9.945307 1.287952 3
294 22812 2.719723 1.142148 1
295 77826 11.154055 1.608486 1
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297 31676 10.037847 0.962245 3
298 74038 12.404762 1.112080 1
299 44738 10.237305 0.633422 3
300 17410 4.745392 0.662520 2
301 5688 4.639461 1.569431 2
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305 23758 9.477022 0.899002 3
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307 11342 5.463615 1.203677 2
308 36109 3.369267 1.575043 1
309 14292 5.234562 0.825954 2
310 11160 0.000000 0.722170 2
311 23762 12.979069 0.504068 3
312 39567 5.376564 0.557476 1
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314 14814 2.196889 0.784587 2
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346 11786 0.566460 0.478837 2
347 19251 0.000000 0.487300 2
348 56594 8.956369 1.193484 3
349 54495 1.523057 0.620528 1
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351 45465 9.235393 0.188350 3
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397 8569 3.198408 0.299287 2
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431 8738 2.341735 1.060235 2
432 7544 3.201046 0.498843 2
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436 67736 13.516711 1.220153 1
437 32492 0.664284 1.116755 1
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439 77289 8.677499 0.344373 1
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441 71890 9.414263 0.384030 1
442 32054 9.196547 1.138253 3
443 38579 10.202968 0.452363 3
444 55984 2.119439 1.481661 1
445 72694 13.635078 0.858314 1
446 42299 0.083443 0.701669 1
447 26635 9.149096 1.051446 3
448 8579 1.933803 1.374388 2
449 37302 14.115544 0.676198 3
450 22878 8.933736 0.943352 3
451 4364 2.661254 0.946117 2
452 4985 0.988432 1.305027 2
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984 9493 6.724021 0.563044 2
985 2371 4.289375 0.012563 2
986 13963 0.000000 1.437030 2
987 2299 3.733617 0.698269 2
988 5262 2.002589 1.380184 2
989 4659 2.502627 0.184223 2
990 17582 6.382129 0.876581 2
991 27750 8.546741 0.128706 3
992 9868 2.694977 0.432818 2
993 18333 3.951256 0.333300 2
994 3780 9.856183 0.329181 2
995 18190 2.068962 0.429927 2
996 11145 3.410627 0.631838 2
997 68846 9.974715 0.669787 1
998 26575 10.650102 0.866627 3
999 48111 9.134528 0.728045 3
1000 43757 7.882601 1.332446 32.2 KNN算法實現(xiàn)

myKNN.py

1 # -*- coding: utf-8 -*-
2 """
3 Created on Mon Sep 17 15:58:58 2018
4 KNN(K-Nearest Neighbor) K-近鄰算法
5 @author: weixw
6 """
7
8 import numpy as np
9 import operator
10 #輸入：行測試數(shù)據(jù)集，訓(xùn)練數(shù)據(jù)集，標簽數(shù)據(jù)集，用于選擇最近鄰居的數(shù)目
11 #功能：根據(jù)歐氏距離公式，找到與未知類別的測試數(shù)據(jù)距離最小的 k 個點，
12 # 以這 k 個點出現(xiàn)頻率最高的類別座位測試數(shù)據(jù)的預(yù)測分類。
13 # 歐氏距離公式：測試數(shù)據(jù)與訓(xùn)練數(shù)據(jù)對應(yīng)位置作差，平方和，第二開方
14 #輸出：測試數(shù)據(jù)預(yù)測分類結(jié)果
15 def classify(testDataSet, trainingDataSet, labelList, k):
16 #訓(xùn)練數(shù)據(jù)集行數(shù)
17 trainingDataSetSize = trainingDataSet.shape[0]
18 #np.tile(testDataSet, (trainingDataSetSize,1)沿X軸**1倍（相當于沒有**），再沿Y軸**trainingDataSetSize倍，維數(shù)：1000*3
19 #歐氏距離公式實現(xiàn)
20 #1 測試數(shù)據(jù) – 訓(xùn)練數(shù)據(jù)
21 diffMat = np.mat(np.tile(testDataSet, (trainingDataSetSize, 1)) – trainingDataSet)
22 #2 差平方（需要將matrix轉(zhuǎn)化為數(shù)組，否則報錯）
23 sqDiffMat = diffMat.A**2
24 #3 按行求和 axis = 0(默認按列) axis = 1(按行)
25 sqDistances = sqDiffMat.sum(axis = 1)
26 #4 開方
27 distances = sqDistances**0.5
28 #agrsort():從小到大排序，返回歐氏距離最小值對應(yīng)的索引列表
29 sortedDistIndicies = distances.argsort()
30 #預(yù)測分類計數(shù)
31 predictClassCount = {}
32 #多數(shù)表決方式，選擇 k 個歐氏距離最小值
33 for i in range(k):
34 #找到索引對應(yīng)的標簽值
35 voteLabel = labelList[sortedDistIndicies[i]]
36 #預(yù)測標簽值字典，存儲索引標簽值預(yù)測次數(shù)
37 predictClassCount[voteLabel] = predictClassCount.get(voteLabel, 0) + 1
38 #對象按值逆向（由大到?。┡判?br /> 39 # sorted(iterable[, cmp[, key[, reverse]]])
40 # itemgetter(1) 取第一項結(jié)果
41 sortedPredictClassCount = sorted(predictClassCount.items(), key = operator.itemgetter(1), reverse = True)
42 return sortedPredictClassCount[0][0]
43
44
45
46 #輸入：數(shù)據(jù)文件
47 #功能：加載文件，文件最后一列是標簽數(shù)據(jù)，分離特征數(shù)據(jù)集與標簽數(shù)據(jù)集
48 # 自動檢測多少列特征數(shù)據(jù)并分離
49 #輸出：特征數(shù)據(jù)集矩陣，標簽數(shù)據(jù)集矩陣
50 def loadDataSet(fileName):
51 #特征數(shù)據(jù)列長度
52 numberFeat = len(open(fileName).readline().split('t')) – 1
53 dataSet = []; labelSet = []
54 fr = open(fileName)
55 for line in fr.readlines():
56 lineArr = []
57 #去除收尾空格，第二分割每一列
58 curLine = line.strip().split('t')
59 #保存每一列特征數(shù)據(jù)
60 for i in range(numberFeat):
61 lineArr.append(float(curLine[i]))
62 dataSet.append(lineArr)
63 labelSet.append(float(curLine[-1]))
64 return np.mat(dataSet), labelSet
65
66 #輸入：原始特征數(shù)據(jù)集
67 #功能：數(shù)據(jù)歸一化，使每類數(shù)據(jù)都在同一范圍內(nèi) (0, 1) 變化
68 # 歸一化公式：newValue = (ol**alue – min)/(max – min)
69 #輸出：歸一化后特征數(shù)據(jù)集，范圍數(shù)組大小（分母）,列最小值數(shù)組
70 def autoNorm(dataMat):
71 #min(axis) 無參數(shù)：所有值中最小值；axis = 0：每列最小值；axis = 1：每行最小值
72 #求出每列最小值
73 minVal**at = dataMat.min(0)
74 #求出每列最大值
75 maxVal**at = dataMat.max(0)
76 #計算差值（對應(yīng)位置相減）
77 range**at = maxVal**at – minVal**at
78 #歸一化特征數(shù)據(jù)集初始化，維數(shù)：1000*3
79 normDataMat = np.zeros(np.shape(dataMat))
80 #原始數(shù)據(jù)集行數(shù)目
81 m = dataMat.shape[0]
82 #歸一化公式分子實現(xiàn)
83 #np.tile(minVals, (m,1)沿X軸**1倍（相當于沒有**），再沿Y軸**m倍，維數(shù)：1000*3
84 normDataMat = dataMat – np.tile(minVal**at, (m, 1))
85 #歸一化公式實現(xiàn)，求得歸一化結(jié)果
86 normDataMat = normDataMat/np.tile(range**at, (m, 1))
87 return normDataMat, range**at, minVal**at
88
89 #輸入：特征數(shù)據(jù)集矩陣，標簽數(shù)據(jù)集列表，測試數(shù)據(jù)與訓(xùn)練數(shù)據(jù)比例，用于選擇最近鄰居的數(shù)目
90 #功能：求出測試特征數(shù)據(jù)集預(yù)測分類結(jié)果
91 # 1.解析文件
92 # 2.通過ratio確定測試數(shù)據(jù)集
93 # 3.歸一化
94 # 4.對每一行測試數(shù)據(jù)運用歐氏距離公式以及多數(shù)表決方式預(yù)測分類結(jié)果
95 # 5.求出整個測試數(shù)據(jù)集的預(yù)測分類結(jié)果
96 #輸出：測試數(shù)據(jù)預(yù)測分類結(jié)果
97 def dataClassify(dataMat, labelList, ratio, k):
98
99 #特征數(shù)據(jù)集歸一化
100 normDataMat, range**at, minVal**at = autoNorm(dataMat)
101 #歸一化特征數(shù)據(jù)集行數(shù)目
102 m = normDataMat.shape[0]
103 #測試數(shù)據(jù)集行數(shù)目（也就知道訓(xùn)練數(shù)據(jù)集行數(shù)）
104 testDataNum = int(m*ratio)
105 #預(yù)測分類錯誤計數(shù)
106 errorCount = 0.0
107 for i in range(testDataNum):
108 #求出測試數(shù)據(jù)集每行預(yù)測分類
109 classifierResult = classify(normDataMat[i, :], normDataMat[testDataNum:m, :], labelList[testDataNum:m], k)
110 print ("the classifier result is: %d, the real answer is: %d"% (classifierResult, labelList[i]))
111 #統(tǒng)計錯誤預(yù)測分類
112 if(classifierResult != labelList[i]):
113 errorCount += 1.0
114 print ("the total error count is %d"% errorCount)
115 print ("the total error rate is: %f"%(errorCount/float(testDataNum)))
116
117
118 #繪制散點圖
119 def drawScatter(filename):
120 import matplotlib.pyplot as plt
121 #加載文件，分離特征數(shù)據(jù)集和標簽數(shù)據(jù)集
122 dataMat, labelList = loadDataSet(filename)
123 #矩陣轉(zhuǎn)化為數(shù)組
124 dataArr = dataMat.A
125 #創(chuàng)建一副圖畫
126 plt.figure()
127 #保存標簽類型相同的索引值（觀察標簽數(shù)據(jù)集，有3種不同類型）
128 label_idx1 = []; label_idx2 = []; label_idx3 = []
129 #遍歷標簽數(shù)組，索引，值
130 for index, value in enumerate(labelList):
131 if(value == 1):
132 label_idx1.append(index)
133 elif(value == 2):
134 label_idx2.append(index)
135 else:
136 label_idx3.append(index)
137 #scatter(x,y,s,maker,color,label)
138 #x,y必須是數(shù)組類型，s表示形狀大小，maker:形狀
139 plt.scatter(dataArr[label_idx1, 1], dataArr[label_idx1, 2], marker = 'x', color = 'm', label = 'no like', s = 30)
140 plt.scatter(dataArr[label_idx2, 1], dataArr[label_idx2, 2], marker = '+', color = 'c', label = 'like', s = 50)
141 plt.scatter(dataArr[label_idx3, 1], dataArr[label_idx3, 2], marker = 'o', color = 'r', label = 'very like', s = 15)
142 plt.legend(loc = 'upper right')
143
144 2.3 測試代碼 1 # -*- coding: utf-8 -*-
2 """
3 Created on Tue Sep 18 14:07:14 2018
4 測試KNN算法
5 @author: weixw
6 """
7 import myKNN as mk
8 #前50%是測試數(shù)據(jù)，后50%作為訓(xùn)練數(shù)據(jù)
9 ratio = 0.5
10 #選擇鄰居數(shù)目
11 #errCount:31 errRate:6.2%
12 k = 4
13 #errCount:30 errRate:6.0%
14 #k = 8
15 #errCount:30 errRate:6.0%
16 #k = 12
17 #errCount:33 errRate:6.6%
18 #k = 16
19 #errCount:32 errRate:6.4%
20 #k = 20
21
22
23
24 fileName = 'datingTestSet2.txt'
25 #繪制數(shù)據(jù)散點圖
26 mk.drawScatter(fileName)
27 #加載文件，分離特征數(shù)據(jù)集和標簽數(shù)據(jù)集
28 dataMat, labelList = mk.loadDataSet(fileName)
29 #預(yù)測測試數(shù)據(jù)結(jié)果
30 mk.dataClassify(dataMat, labelList, ratio, k)2.4 運行結(jié)果

輸入數(shù)據(jù)的散點圖：

k = 4 ，ratio = 0.5（一半測試數(shù)據(jù)，一半訓(xùn)練數(shù)據(jù)）時分類結(jié)果：

在 k為不同值時運行結(jié)果：

可以看出，并不是 k越大，正確率越高，會產(chǎn)生過擬合。

3. 優(yōu)缺點優(yōu)點：

1. 簡單，易于理解，易于實現(xiàn)，無需訓(xùn)練；

2. 精度高，對異常值不敏感；

缺點：

計算復(fù)雜度高，空間復(fù)雜度高。

拓展知識：