KNN(K-Nearest Neighbor)算法是机器学习算法中最基础、最简单的算法之一。它既能用于分类,也能用于回归。KNN通过测量不同特征值之间的距离来进行分类。
KNN算法的思想非常简单:对于任意n维输入向量,分别对应于特征空间中的一个点,输出为该特征向量所对应的类别标签或预测值。
KNN算法是一种非常特别的机器学习算法,因为它没有一般意义上的学习过程。它的工作原理是利用训练数据对特征向量空间进行划分,并将划分结果作为最终算法模型。存在一个样本数据集合,也称作训练样本集,并且样本集中的每个数据都存在标签,即我们知道样本集中每一数据与所属分类的对应关系。
输入没有标签的数据后,将这个没有标签的数据的每个特征与样本集中的数据对应的特征进行比较,然后提取样本中特征最相近的数据(最近邻)的分类标签。
一般而言,我们只选择样本数据集中前k个最相似的数据,这就是KNN算法中K的由来,通常k是不大于20的整数。最后,选择k个最相似数据中出现次数最多的类别,作为新数据的分类。
其中,在分类问题中,KNN用来预测种类。一般使用“投票法”,选择这k个样本中出现最多的类别来作为测试样本的类别。
在回归问题中,KNN预测一个值。使用“平均法”,将k个样本的实值输出的平均值作为测试样本的输出。
import pandas as pd
from numpy import *
import operator
import matplotlib
import matplotlib.pyplot as plt
## 本文数据集为3列,标签集为一列
#此类KNN适用与标签混乱,并且标签只有一列的情况,用于算出它的错误率
#分离出数据和标签,3列的数据集,1列的标签的情况
def file2matrix(filename):
fr = open(filename)
arrayOLines = fr.readlines()
# 得到文件行数
numberOfLines = len(arrayOLines)
# print(numberOfLines)
# 创建返回的NumPy矩阵
returnMat = zeros((numberOfLines, 3))
classLabelVector = []
index = 0
# 解析文件数据到列表
for line in arrayOLines:
line.strip()
# 截取掉所有的回车字符
line = line.strip()
# 数据分割成一个元素列表
listFromLine = line.split('\t')
# 下面的3要根据训练的列数确定
returnMat[index, :] = listFromLine[0:3]
# classLabelVector.append(int(listFromLine[-1]))
classLabelVector.append(int(listFromLine[-1]))
index += 1
return returnMat, classLabelVector
# 归一化数据
def autoNorm(dataSet):
minVals = dataSet.min(0)
maxVals = dataSet.max(0)
ranges = maxVals - minVals
normDataSet = zeros(shape(dataSet))
m = dataSet.shape[0]
normDataSet = dataSet - tile(minVals, (m, 1))
# 特征值相除
normDataSet = normDataSet / tile(ranges, (m, 1))
return normDataSet, ranges, minVals
# KNN进行分类,这个算法只支持标签为列表的情况
def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
# 距离计算
diffMat = tile(inX, (dataSetSize, 1)) - dataSet
sqDiffMat = diffMat ** 2
sqDistances = sqDiffMat.sum(axis=1)# 按照x轴相加
distances = sqDistances ** 0.5
sortedDistIndicies = distances.argsort()
classCount = {}
# 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
# 排序
sortedClassCount = sorted(classCount.items(),
key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]
if __name__ == '__main__':
hoRatio = 0.10
datingDataMat, datingLabels = file2matrix('data/datingTestSet2.txt')
normMat, ranges, minVals = autoNorm(datingDataMat)
m = normMat.shape[0]
numTestVecs = int(m * hoRatio)
errorCount = 0.0
for i in range(numTestVecs):
classifierResult = classify0(normMat[i, :], normMat[numTestVecs:m,:],
datingLabels[numTestVecs:m], 3)
print("the classifier came back with: {}, the real answer is {}"
.format(classifierResult, datingLabels[i]))
if (classifierResult != datingLabels[i]): errorCount += 1.0
print("the total error rate is: {}".format(errorCount / float(numTestVecs)))
数据集
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
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42878 11.464879 1.481589 3
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26080 11.318785 1.510979 3
19119 3.574709 1.531514 2
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51136 1.828412 1.013702 1
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42776 11.309897 0.086291 3
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15436 0.241714 0.715577 2
14402 10.482619 1.694972 2
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6390 0.000000 0.189456 2
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43432 1.508394 0.652651 1
38334 9.359918 0.052262 3
34068 10.052333 0.550423 3
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22239 11.265971 0.724054 3
28725 10.383830 0.254836 3
57071 3.878569 1.377983 1
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9896 0.000000 0.924198 2
65821 4.106727 1.085669 1
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71289 7.796874 0.052336 1
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15092 0.000000 1.370549 2
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10141 8.240793 0.099735 2
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55783 3.612548 1.551911 1
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11992 0.000000 1.606849 2
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43757 7.882601 1.332446 3
# 导入jar包
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
class KNN:
"""KNN 回归算法
使用鸢尾花的四个特征进行训练 花瓣长度、宽度 花萼长度、宽度
算法的目标是 根据鸢尾花的三个特征,预测最后一个特征的 度量 (预测值)
"""
def __init__(self, k):
"""初始化方法
k:int
设置k的值,找出相邻数据的个数
"""
self.k = k
def fit(self, X, y):
"""根据参数传递过来的X,对样本数据进行预测
-------
返回一个数组类型,预测结果
"""
self.X = np.asarray(X)
self.y = np.asarray(y)
result = []
def predict(self, V):
V = np.asarray(V)
result = []
for v in V:
# 计算距离 测试几种某一个数据到训练集中每一个点的距离
# 数学模型就是 计算空间中某一点(含 x,y,z坐标) 到空间中一个含有多个点的集合中()
# axis=1是向着行的方向计算,axis=0是按照着列的方向计算
dis = np.sqrt(np.sum((v - self.X) ** 2, axis=1))
index = dis.argsort()
index = index[:self.k]
# 计算训练的平均值作为预测结果
disavg = np.mean(self.y[index])
# 将disavg的最大下标的数据添加到result数组中
result.append(disavg.argmax())
result.append(np.mean(self.y[index]))
return np.asarray(result)
def predict2(self, V):
""" 加权重 """
V = np.asarray(V)
result = []
for v in V:
# 计算距离 测试几种某一个数据到训练集中没一个点的距离
# 数学模型就是 计算空间中某一点(含 x,y,z坐标) 到空间中一个含有多个点的集合中()
dis = np.sqrt(np.sum((v - self.X) ** 2, axis=1))
index = dis.argsort()
index = index[:self.k]
# 求k个距离的倒数和
he = np.sum(1 / (dis[index] + 0.0001))
# 计算权重(倒数/倒数和)
weight = (1 / (dis[index] + 0.0001)) / he
# 计算训练的平均值作为预测结果
# disavg = np.mean(self.y[index])
# 将disavg的最大下标的数据添加到result数组中
# result.append(disavg.argmax())
# 将前面K的计算结果(计算结果要与该店所占的权重比相乘)求均值后放入result数组中
result.append(np.mean(self.y[index] * weight))
return np.asarray(result)
if __name__ == '__main__':
# 获取数据
data = pd.read_csv("data/iris_training.csv")
data1=pd.read_csv("data/iris_test.csv")
# 随机去前10条数据
data.sample(10)
# 删除两列
data.drop(["virginica"],axis=1,inplace=True)
data1.drop(["virginica"],axis=1,inplace=True)
print(data)
# 去重,排除线性相关
# data.drop_duplicates(inplace=True)
# print(len(data))
#这条命令因该是随机打乱样本的
X = data.sample(len(data),random_state=0)
print(len(X))
Y= data1.sample(len(data1), random_state=0)
print(len(Y))
train_X = X.iloc[:120, :-1]
train_y = X.iloc[:120, -1]
test_X = Y.iloc[:30, :-1]
test_y = Y.iloc[:30, -1]
print(train_X)
print(train_y)
print(test_X)
print(test_y)
# 创建KNN对象
knn = KNN(k = 3)
knn.fit(train_X,train_y)
result = knn.predict(test_X)
print(result)
print(result.shape)
print(test_y.values)
print(test_y.shape)
# print(np.mean(result-test_y)**2)
# display(result)
# display(test_y.values)
# np.mean((result-test_y)**2)
#
# matplotlib 不支持中文,需要配置一下,设置一个中文字体
mpl.rcParams["font.family"] = "SimHei"
# 能够显示 中文 ,正常显示 "-"
mpl.rcParams["axes.unicode_minus"] = False
plt.figure(figsize=(10,10))
plt.plot(result,"ro-",label="预测值")
plt.plot(test_y.values,"bo--",label="真实值")
plt.title("KNN回归算法预测展示")
plt.xlabel("序号")
plt.ylabel("度量值")
plt.legend()
plt.show()
数据集
iris_training.csv
1.00 0.00 1.00 0.00 0.26 0.00 0.00 0.00 0.00 0.00 0.00
9.00 0.50 9.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
139.00 0.07 40.00 0.00 0.38 0.10 0.00 0.01 0.01 0.01 0.01
17.00 0.07 13.00 0.00 0.32 0.01 0.00 0.06 0.00 0.00 0.00
16.00 0.43 15.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.06
5.00 0.20 5.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
36.00 0.15 22.00 0.00 0.33 0.03 0.00 0.00 0.00 0.00 0.03
36.00 0.15 22.00 0.00 0.33 0.03 0.00 0.00 0.00 0.00 0.03
36.00 0.15 22.00 0.00 0.33 0.03 0.00 0.00 0.00 0.00 0.03
36.00 0.15 22.00 0.00 0.33 0.03 0.00 0.00 0.00 0.00 0.03
18.00 0.18 17.00 0.00 0.32 0.01 0.00 0.00 0.06 0.00 0.00
13.00 0.09 11.00 0.00 0.33 0.01 0.00 0.00 0.00 0.00 0.00
11.00 0.09 11.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
3.00 0.00 3.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
35.00 0.18 31.00 0.00 0.35 0.05 0.00 0.00 0.00 0.00 0.00
31.00 0.13 24.00 0.00 0.34 0.03 0.00 0.00 0.00 0.00 0.00
16.00 0.04 11.00 0.00 0.31 0.01 0.00 0.06 0.00 0.00 0.00
6.00 0.13 6.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
6.00 0.13 6.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.00
7.00 0.05 5.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00
141.00 0.40 58.00 0.00 0.38 0.19 0.00 0.00 0.00 0.00 0.00
22.00 0.06 17.00 0.00 0.33 0.02 0.00 0.00 0.00 0.00 0.00
14.00 0.21 13.00 0.00 0.31 0.01 0.00 0.00 0.00 0.00 0.00
125.00 0.06 38.00 0.00 0.36 0.08 0.00 0.00 0.00 0.00 0.01
29.00 0.10 21.00 0.00 0.33 0.01 0.00 0.00 0.00 0.00 0.00
2.00 0.00 2.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00
4.00 0.17 4.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00
6.00 0.07 6.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
29.00 0.08 19.00 0.00 0.32 0.01 0.00 0.00 0.03 0.00 0.00
48.00 0.24 38.00 0.00 0.36 0.07 0.00 0.00 0.00 0.00 0.00
157.00 0.05 41.00 0.00 0.36 0.10 0.00 0.00 0.00 0.00 0.00
52.00 0.06 19.00 0.00 0.35 0.03 0.00 0.00 0.00 0.00 0.02
30.00 0.10 23.00 0.00 0.34 0.02 0.00 0.00 0.03 0.00 0.00
5.00 0.10 5.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
100.00 0.03 33.00 0.00 0.36 0.06 0.00 0.00 0.00 0.00 0.00
54.00 0.10 30.00 0.00 0.35 0.04 0.00 0.00 0.00 0.00 0.00
10.00 0.27 10.00 0.00 0.31 0.01 0.00 0.00 0.00 0.00 0.00
15.00 0.09 12.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.07
4.00 0.00 4.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
32.00 0.16 26.00 0.00 0.33 0.03 0.00 0.00 0.00 0.00 0.00
25.00 0.50 23.00 0.00 0.32 0.02 0.00 0.00 0.00 0.00 0.00
118.00 0.08 40.00 0.00 0.37 0.09 0.00 0.00 0.01 0.00 0.00
14.00 0.02 12.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
35.00 0.06 19.00 0.00 0.32 0.01 0.00 0.00 0.00 0.03 0.00
38.00 0.18 25.00 0.00 0.35 0.03 0.00 0.00 0.00 0.00 0.00
280.00 0.04 42.00 0.00 0.39 0.16 0.00 0.01 0.00 0.00 0.00
3.00 0.00 3.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
3.00 0.00 2.00 0.00 0.25 0.00 0.00 0.00 0.00 0.00 0.00
24.00 0.08 16.00 0.00 0.32 0.01 0.00 0.00 0.04 0.00 0.00
6.00 0.13 4.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
12.00 0.02 11.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
2.00 0.00 2.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
5.00 0.50 5.00 0.00 0.30 0.01 0.00 0.00 0.00 0.00 0.00
25.00 0.24 23.00 0.00 0.33 0.02 0.00 0.00 0.00 0.00 0.04
7.00 0.10 5.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
8.00 0.00 7.00 0.00 0.29 0.00 0.00 0.00 0.13 0.00 0.00
4.00 0.00 4.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
4.00 0.00 4.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
7.00 0.24 7.00 0.00 0.31 0.01 0.00 0.00 0.14 0.00 0.00
3.00 0.33 2.00 0.00 0.26 0.00 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00
113.00 0.07 42.00 0.00 0.37 0.10 0.00 0.00 0.00 0.00 0.00
29.00 0.05 22.00 0.00 0.33 0.02 0.00 0.00 0.00 0.00 0.00
28.00 0.09 19.00 0.00 0.33 0.01 0.00 0.00 0.04 0.00 0.00
9.00 0.08 6.00 0.00 0.33 0.01 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00
15.00 0.01 10.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
22.00 0.19 21.00 0.00 0.33 0.02 0.00 0.00 0.05 0.00 0.00
12.00 0.11 10.00 0.00 0.33 0.01 0.00 0.00 0.00 0.00 0.00
91.00 0.02 30.00 0.00 0.35 0.04 0.00 0.00 0.01 0.00 0.00
49.00 0.10 27.00 0.00 0.34 0.02 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.29 0.00 0.00 0.00 0.00 0.00 0.00
31.00 0.05 21.00 0.00 0.34 0.02 0.00 0.00 0.00 0.00 0.00
20.00 0.24 18.00 0.00 0.34 0.02 0.00 0.00 0.00 0.00 0.00
16.00 0.08 14.00 0.00 0.32 0.01 0.00 0.00 0.06 0.00 0.00
21.00 0.09 19.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.05
78.00 0.13 38.00 0.00 0.36 0.08 0.00 0.00 0.00 0.00 0.00
18.00 0.15 17.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
17.00 0.07 10.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
5.00 0.20 4.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.24 0.00 0.00 0.00 0.00 0.00 0.00
1.00 0.00 1.00 0.00 0.31 0.01 0.00 0.00 0.00 0.00 0.00
44.00 0.06 22.00 0.00 0.32 0.02 0.00 0.00 0.00 0.00 0.00
12.00 0.11 12.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
12.00 0.11 12.00 0.00 0.32 0.01 0.00 0.00 0.00 0.00 0.00
30.00 0.06 21.00 0.00 0.34 0.02 0.00 0.00 0.00 0.00 0.00
6.00 0.07 4.00 0.00 0.27 0.00 0.00 0.00 0.00 0.00 0.00
6.00 0.27 6.00 0.00 0.28 0.00 0.00 0.00 0.00 0.00 0.00
9.00 0.08 6.00 0.00 0.30 0.00 0.00 0.00 0.00 0.00 0.00
20.00 0.07 17.00 0.00 0.34 0.03 0.00 0.00 0.00 0.00 0.00
16.00 0.08 13.00 0.00 0.34 0.01 0.00 0.00 0.00 0.00 0.00
12.00 0.08 12.00 0.00 0.32 0.01 0.00 0.08 0.00 0.00 0.00
50.00 0.11 28.00 0.00 0.35 0.04 0.00 0.00 0.00 0.00 0.00
训练集
iris_test.csv
1
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-1
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-1
-1
-1
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-1
-1
-1
-1
-1
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-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
-1
实验结果
KNN回归权值
KNN平均值
参考链接:KNN回归代码来源