机器学习 | K近邻算法

jiezi

6 年前

由于近期学业繁重 QAQ，所以我就不说废话了，直接上代码~
使用 K 近邻算法改进约会网站
from numpy import *
import operator
import matplotlib
import matplotlib.pyplot as plt

# 将文件转成 numpy 数组的函数
def file2matrix(filename):
#打开文件
fr=open(filename)
#将文件内容使用数组表示
arrayOLines=fr.readlines()
#print(‘arrayOLines:’)
#print(arrayOLines)
#数组的长度表示文件的行数
numberOfLine=len(arrayOLines)
#print(‘numberOfLine:’)
#print(numberOfLine)
#创建返回的 NumPy 矩阵，内容全为 0
returnMat=zeros((numberOfLine,3))
#print(‘returnMat:’)
#print(returnMat)
classLabelVector=[]
index=0
for line in arrayOLines:
line=line.strip()
listFromLine=line.split(‘\t’)
#print(‘listFromLine:’)
#print(listFromLine)
returnMat[index,:]=listFromLine[0:3]
#print(‘returnMat:’)
#print(+returnMat)
classLabelVector.append(int(listFromLine[-1]))
#print(‘classLabelVector:’)
#print(classLabelVector)
index+=1
return returnMat,classLabelVector

# 根据数组绘图的函数
def myDraw(datingDataMat,datingLabels):
#建立一个画布
fig=plt.figure()
#在画布中建立图表
#fig.add_subplot() 函数
#画布分割成 1 行 1 列
ax=fig.add_subplot(111)
ax.scatter(datingDataMat[:,0],datingDataMat[:,1],
15.0*array(datingLabels),15.0*array(datingLabels))
plt.show()

# 归一化特征值的函数
#返回的是归一化后的数组，取值范围，每一列的最小值归一化数据
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))
normDataSet=normDataSet/tile(ranges,(m,1))
return normDataSet,ranges,minVals

# 使用 k - 近邻算法进行分类
def classify0(inX,dataSet,labels,k):
dataSetSize=dataSet.shape[0]
#计算距离
diffMat=tile(inX,(dataSetSize,1))-dataSet
sqDiffMat=diffMat**2
distances=sqDiffMat.sum(axis=1)
sortedDisIndices=distances.argsort()
classCount={}
#选择距离最小的 k 个点
for i in range(k):
voteIlabel=labels[sortedDisIndices[i]]
classCount[voteIlabel]=classCount.get(voteIlabel,0)+1
#排序
sortedClassCount=sorted(classCount.items(),
key=operator.itemgetter(1),reverse=True)
#返回发生频率最高的元素标签
return sortedClassCount[0][0]

# 将数据分为训练集与测试集
#对分类器分类效果进行测试
def datingClassTest():
#测试数据占比
hoRatio=0.10
datingDataMat,datingLabels=file2matrix(‘datingTestSet2.txt’)
normMat,ranges,minVals=autoNorm(datingDataMat)
m=normMat.shape[0]
#m 为行数 1000
#print(‘m:’)
#print(m)
numTestVecs=int(m*hoRatio)
#选取其中的 100 个进行测试
#print(‘numTestVecs:’)
#print(numTestVecs)
errorCount=0.0
#print(‘normMat[numTestVecs:m,:]:’)
#print(normMat[numTestVecs:m,:])
#print(‘datingLabels[numTestVecs:m]:’)
#print(datingLabels[numTestVecs:m])
for i in range(numTestVecs):
#print(‘i:’)
#print(i)
classifierResult=classify0(normMat[i,:],normMat[numTestVecs:m,:],
datingLabels[numTestVecs:m],3)
print(“the classifierResult came back with: %d,the real answer is: %d”
%(classifierResult,datingLabels[i]))
if(classifierResult!=datingLabels[i]):
errorCount+=1.0
print(“the total error rate is: %f”%(errorCount/float(numTestVecs)))
myDraw(datingDataMat,datingLabels)

# 玩视频游戏所消耗的时间百分比
#每年获得的飞行常客里程数
#每周消费的冰淇淋公升数
#预测函数
def classifyPerson():
resultList=[‘not at all’,’in small doses’,’in large deses’]
percentTats=float(input(“ 玩视频游戏所消耗的时间百分比?”))
ffMiles=float(input(“ 每年获得的飞行常客里程数?”))
iceCream=float(input(“ 每周消费的冰淇淋公升数?”))
datingDataMat,datingLabels=file2matrix(‘datingTestSet2.txt’)
normMat,ranges,minVals=autoNorm(datingDataMat)
inArr=array([ffMiles,percentTats,iceCream])
classifierResult=classify0((inArr-minVals)/ranges,normMat,datingLabels,3)
print(“You will probably like this person: “,resultList[classifierResult-1])

def main():
classifyPerson()

if __name__==’__main__’:
main()
datingTestSet2.txt 数据预览
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
…

使用 K 近邻算法实现手写识别
from numpy import *
import operator
from os import listdir

# 将二维 32X32 的图像，
#转换成一个 1X1024 的向量
#方便使用之前的分类器
def img2vector(filename):
returnVect=zeros((1,1024))
fr=open(filename)
for i in range(32):
lineStr=fr.readline()
for j in range(32):
returnVect[0,32*i+j]=int(lineStr[j])
return returnVect

# 手写数字识别系统
def handwritingClassTest():
#标签列表
hwLabels=[]
#获取目录内容
trainingFileList=listdir(‘trainingDigits’)
m=len(trainingFileList)
#以文件夹中的文件个数为行数
#将每个文件中的内容转换成一个 1X1024 的向量
#矩阵的每一行代表一个文件中的所有内容
trainingMat=zeros((m,1024))
#从文件名解析分类数字
#7_200.txt 表示数字 7 的第 200 个实例
for i in range(m):
#获取文件名
fileNameStr=trainingFileList[i]
fileStr=fileNameStr.split(‘.’)[0]
classNumStr=int(fileStr.split(‘_’)[0])
hwLabels.append(classNumStr)
trainingMat[i,:]=img2vector(‘trainingDigits/%s’%fileNameStr)
testFileList=listdir(‘testDigits’)
errorCount=0.0
mTest=len(testFileList)
for i in range(mTest):
fileNameStr=testFileList[i]
fileStr=fileNameStr.split(‘.’)[0]
classNumStr=int(fileStr.split(‘_’)[0])
vectorUnderTest=img2vector(‘testDigits/%s’%fileNameStr)
classifierResult=classify0(vectorUnderTest,trainingMat,hwLabels,3)
print(“the classifier came back with: %d,the real answer is: %d”
%(classifierResult,classNumStr))
if(classifierResult!=classNumStr):
errorCount+=1.0
print(“\nthe total number of errors is: %d”%errorCount)
print(“\nthe total error rate is: %f”%(errorCount/float(mTest)))

def main():
#testVector=img2vector(‘./MLiA_SourceCode/machinelearninginaction/Ch02/digits/testDigits/0_13.txt’)
#print(‘testVector:’)
#print(testVector[0,0:31])
handwritingClassTest()

if __name__==’__main__’:
main()

0_0.txt 数据预览
00000000000001111000000000000000
00000000000011111110000000000000
00000000001111111111000000000000
00000001111111111111100000000000
00000001111111011111100000000000
00000011111110000011110000000000
00000011111110000000111000000000
00000011111110000000111100000000
00000011111110000000011100000000
00000011111110000000011100000000
00000011111100000000011110000000
00000011111100000000001110000000
00000011111100000000001110000000
00000001111110000000000111000000
00000001111110000000000111000000
00000001111110000000000111000000
00000001111110000000000111000000
00000011111110000000001111000000
00000011110110000000001111000000
00000011110000000000011110000000
00000001111000000000001111000000
00000001111000000000011111000000
00000001111000000000111110000000
00000001111000000001111100000000
00000000111000000111111000000000
00000000111100011111110000000000
00000000111111111111110000000000
00000000011111111111110000000000
00000000011111111111100000000000
00000000001111111110000000000000
00000000000111110000000000000000
00000000000011000000000000000000