本文实例讲述了Python实现的knn算法。分享给大家供大家参考，具体如下：

代码参考机器学习实战那本书：

有兴趣你们可以去了解下

具体代码：

# -*- coding:utf-8 -*-

#! python2

'''''

@author:zhoumeixu

createdate:2015年8月27日

'''

#np.zeros((4,2))

#np.zeros(8).reshape(4,2)

#x=np.array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) np.zeros_like(x)

# 最值和排序：最值有np.max(),np.min() 他们都有axis和out(输出)参数,

# 而通过np.argmax(), np.argmin()可以得到取得最大或最小值时的 下标。

# 排序通过np.sort(), 而np.argsort()得到的是排序后的数据原来位置的下标

# 简单实现knn算法的基本思路

import numpy as np

import operator #运算符操作包

from _ctypes import Array

from statsmodels.sandbox.regression.kernridgeregress_class import plt_closeall

def createDataSet():

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

labels=['A','A','B','B']

return group ,labels

group,labels=createDataSet()

def classify0(inx,dataSet,labels,k):

dataSetSize=dataSet.shape[0]

diffMat=np.tile(inx,(dataSetSize,1))-dataSet

sqDiffMat=diffMat**2

sqDistances=sqDiffMat.sum(axis=1)

distances=sqDistances**0.5 #计算距离 python中会自动广播的形式

sortedDistIndicies=distances.argsort() #排序，得到原来数据的在原来所在的下标

classCount={}

for i in range(k):

voteIlabel=labels[sortedDistIndicies[i]] # 计算距离最近的值所在label标签

classCount[voteIlabel]=classCount.get(voteIlabel,0)+1 # 计算距离最近的值所在label标签，对前k哥最近数据进行累加

sortedClassCount=sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True) #排序得到距离k个最近的数所在的标签

return sortedClassCount[0][0]

if __name__=='__main__':

print(classify0([0,0],group,labels,4))

# 利用knn算法改进约会网站的配对效果

def file2matrix(filename):

fr=open(filename)

arrayOLines=fr.readlines()

numberOfLines=len(arrayOLines)

returnMat=np.zeros((numberOfLines,3))

classLabelVector=[]

index=0

for line in arrayOLines:

line=line.strip()

listFromLine=line.split('\t')

returnMat[index,:]=listFromLine[0:3]

classLabelVector.append(int(listFromLine[-1]))

index+=1

return returnMat ,classLabelVector #生成训练数据的array和目标array

path=u'D:\\Users\\zhoumeixu204\\Desktop\\python语言机器学习\\机器学习实战代码 python\\机器学习实战代码\\machinelearninginaction\\Ch02\\'

datingDataMat,datingLabels=file2matrix(path+'datingTestSet2.txt')

import matplotlib

import matplotlib.pyplot as plt

fig=plt.figure()

ax=fig.add_subplot(111)

ax.scatter(datingDataMat[:,1],datingDataMat[:,2])

plt.show()

ax.scatter(datingDataMat[:,1],datingDataMat[:,2],15.0*np.array(datingLabels),15*np.array(datingDataMat[:,2]))

plt.show() #生成训练数据的array和目标array

def autoNorm(dataset):

minVals=dataset.min(0)

maxVals=dataset.max(0)

ranges=maxVals-minVals

normeDataSet=np.zeros(np.shape(dataset))

m=dataset.shape[0]

normDataSet=dataset-np.tile(minVals,(m,1))

normDataSet=normDataSet/np.tile(ranges,(m,1))

return normDataSet ,ranges,minVals

normMat,ranges,minVals=autoNorm(datingDataMat)

def datingClassTest():

hoRatio=0.1

datingDataMat,datingLabels=file2matrix(path+'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 :%d,the real answer is :%d"\

%(classifierResult,datingLabels[i])

if classifierResult!=datingLabels[i]:

errorCount+=1.0

print "the total error rare is :%f"%(errorCount/float(numTestVecs)) #利用knn算法测试错误率

if __name__=='__main__':

datingClassTest()

#利用构建好的模型进行预测

def classifyPerson():

resultList=['not at all','in same doses','in large d oses']

percentTats=float(raw_input("percentage if time spent playin cideo games:"))

ffMiles=float(raw_input("frequnet fliter miles earned per year:"))

iceCream=float(raw_input("liters of ice cream consumed per year:"))

datingDataMat,datingLabels=file2matrix(path+'datingTestSet2.txt')

normMat,ranges,minVals=autoNorm(datingDataMat)

inArr=np.array([ffMiles,percentTats,iceCream])

classifierResult=classify0((inArr-minVals)/ranges,normMat,datingLabels,3)

print("you will probably like the person:",resultList[classifierResult-1])

if __name__!='__main__':

classifyPerson()

#利用knn算法进行手写识别系统验证

path=u'D:\\Users\\zhoumeixu204\\Desktop\\python语言机器学习\\机器学习实战代码 python\\机器学习实战代码\\machinelearninginaction\\Ch02\\'

def img2vector(filename):

returnVect=np.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

testVector=img2vector(path+'testDigits\\0_13.txt')

print(testVector[0,0:31])

import os

def handwritingClassTest():

hwLabels=[]

trainingFileList=os.listdir(path+'trainingDigits')

m=len(trainingFileList)

trainingMat=np.zeros((m,1024))

for i in range(m):

fileNameStr=trainingFileList[i]

fileStr=fileNameStr.split('.')[0]

classNumStr=int(fileStr.split('_')[0])

hwLabels.append(classNumStr)

trainingMat[i,:]=img2vector(path+'trainingDigits\\'+fileNameStr)

testFileList=os.listdir(path+'testDigits')

errorCount=0.0

mTest=len(testFileList)

for j in range(mTest):

fileNameStr=testFileList[j]

fileStr=fileNameStr.split('.')[0]

classNumStr=int(fileNameStr.split('_')[0])

classNumStr=int(fileStr.split('_')[0])

vectorUnderTest=img2vector(path+'testDigits\\'+fileNameStr)

classifierResult=classify0(vectorUnderTest,trainingMat,hwLabels,3)

print("the classifier canme 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("\n the total error rate is :%f"%(errorCount/float(mTest)))

if __name__=='__main__':

handwritingClassTest()

运行结果如下图：

注：这里使用到了statsmodels模块，可以点击此处本站下载statsmodels安装模块，再进入statsmodels模块所在目录位置，使用：

pip install statsmodels-0.9.0-cp27-none-win32.whl

进行statsmodels模块的安装

同理，出现ImportError: No module named pandas错误提示时，点击此处本站下载pandas模块，再使用

pip install pandas-0.23.1-cp27-none-win32.whl

进行pandas模块的安装

希望本文所述对大家Python程序设计有所帮助。