学习笔记——《机器学习实战》KNN算法实现约会网站测试，手写数字识别，代码，注释，错误修改

2024年5月13日 82次阅读

# -*- coding: cp936 -*-
”’
Created on Nov 27, 2016
KNN Code for Machine Learning in Action Ch. 2
@author: Miaotong Jiang
”’

from numpy import *

from os import listdir

import operator

mydict={‘largeDoses’:3, ‘smallDoses’:2, ‘didntLike’:1}

def createDataSet():#创建训练数据集

group=array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])

labels=[‘A’,’A’,’B’,’B’]

return group,labels

def classify0(inx,dataset,labels,k):#得到欧式距离下最近邻决策规则的测试数据的预测分类

datasetsize=dataset.shape[0]

diffs=tile(inx,(datasetsize,1))-dataset

sq=diffs**2

ss=sq.sum(axis=1)#( >>> sum([[1,5],[0,3]],axis=1) array([6, 3]) >>> sum([[1,5],[0,3]],axis=0) array([1, 8]) )

s=ss**0.5

sortedsindices=s.argsort()#返回数组值从小到大的索引值

classCount={}

for i in range(k):

votellabel=labels[sortedsindices[i]]

classCount[votellabel]=classCount.get(votellabel,0)+1

sortedclasscount=sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)#sorted(可迭代类型，cmp（用于比较的函数，由key决定，有默认值，）， key（此处根据第二个域排序），reverse反转)

print classCount#输出是列表[‘B’,’2′,’A’,’1′]

return sortedclasscount[0][0]

def file2matrix(filename):#解析训练数据文件，将其转化成训练数据输入矩阵和输出矩阵

fr=open(filename)

arrayolines=fr.readlines()

numberoflines=len(arrayolines)#得到文件行数

returnmat=zeros((numberoflines,3))#创建零填充的矩阵

classlabelvector=[]

index=0

for line in arrayolines:

line=line.strip()#截取掉所有回车符

listfromline=line.split(‘\t’)#利用\t将数据分割成一个元素列表

returnmat[index,:]=listfromline[0:3]#选取前三个元素储存到矩阵中

classlabelvector.append(int(mydict[listfromline[-1]]))#把最后一列存到classlabelvector里

index+=1

return returnmat,classlabelvector

def autonorm(dataset):#将矩阵具体的特征值（即测试数据矩阵的每个元素归一化）

minvals=dataset.min(0)

maxvals=dataset.max(0)

ranges=maxvals-minvals

normdataset=zeros(shape(dataset))#shape() 输入参数：类似数组（比如列表，元组）等，或是数组返回：一个整型数字的元组，元组中的每个元素表示相应的数组每一维的长度

m=dataset.shape[0]#行数

normdataset=dataset-tile(minvals,(m,1))

normdataset=normdataset/tile(ranges,(m,1))

return normdataset,ranges,minvals

def datingclasstest():#错误率测试函数

horatio=0.05#测试数据的比例

datingdatamat,datinglabels=file2matrix(‘datingtestset.txt’)

normmat,ranges,minvals=autonorm(datingdatamat)

m=normmat.shape[0]

numtestvecs=int(m*horatio)

errorcount=0.0

for i in range(numtestvecs):#%还用在python的格式化输出，比如：a = ‘test’ ;print ‘it is a %s’ %(a) ;打印的结果就是 it is a test

classifierresult=classify0(normmat[i,:],normmat[numtestvecs:m,:],datinglabels[numtestvecs:m],3)

print”the classifier 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))

def classifyperson():#海伦约会网站预测函数

resultlist=[‘not at all’,’in small doses’,’in large doses’]

percenttats=float(raw_input(“percentage of time spent playing video games?”))

ffmiles=float(raw_input(“frequent flier miles earned per year?”))

icecream=float(raw_input(“liters of ice cream consumed per year”))

datingdatamat,datinglabels=file2matrix(‘datingtestset.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,”

return resultlist[classifierresult-1]#需要给出返回值

def img2vector(filename):#图像转换为向量

returnvector=zeros((1,1024))

fr=open(filename)

for i in range(32):

linestr=fr.readline()

for j in range(32):

returnvector[0,32*i+j]=int(linestr[j])

return returnvector

def handwritingclasstest():#手写数字识别系统测试

hwlabels=[]

trainingfilelist=listdir(‘trainingdigits’)#获取目录内容

m=len(trainingfilelist)

trainingmat=zeros((m,1024))#每行是一个图像

for i in range(m):

filenamestr=trainingfilelist[i]

filestr=filenamestr.split(‘.’)[0]#去掉.txt

classnumstr=int(filestr.split(‘_’)[0])#去掉_及之后的内容

hwlabels.append(classnumstr)#从文件名解析分类数字

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”the total number of errors is:%d”%errorcount

print”the total error rate is:%f”%(errorcount/mtest)

def handwritingclasspredict():#手写识别系统预测

vectorundertest=img2vector(‘testSet.txt’)