算法描述
K邻近算法采用测量不同特征值之间的距离方法进行分类
工作原理
存在一个样本数据集合，也称作训练样本集，并且样本集中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。输入没有标签的新数据后，将新数据的每个特征与样本集中数据对应的特征进行比较
然后算法提取样本集中最相似的数据（最邻近）的分类标签。一般来说，我们只选择样本数据集中前K个最相似的数据，这就是K-邻近算法中K的出处，通常K是不大于20的整数。最后，选择K个最相似的数据中出现次数最多
的分类，作为新数据的分类
算法的类别
该算法属于监督学习，用于分类，因而其目标变量是分散的
优点
对异常数据值不敏感，精度高，无数据输入设定
缺点
空间计算复杂度高
算法的一般流程
收集数据
准备数据
分析数据
训练算法
测试算法
使用算法
KNN算法实现代码
from numpy import * import operator from os import listdir import matplotlib import matplotlib.pyplot as plt def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] diffMat = tile(inX, (dataSetSize,1)) - dataSet sqDiffMat = diffMat**2 sqDistances = sqDiffMat.sum(axis=1) distances = sqDistances**0.5 sortedDistIndicies = distances.argsort() classCount={} for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] #对类计数 classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True) #选取出现次数最多的 return sortedClassCount[0][0] def createDataSet(): group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]]) labels = ['A','A','B','B'] return group, labels def file2matrix(filename): fr = open(filename) numberOfLines = len(fr.readlines()) #get the number of lines in the file returnMat = zeros((numberOfLines,3)) #prepare matrix to return classLabelVector = [] #prepare labels return fr = open(filename) index = 0 for line in fr.readlines(): line = line.strip() listFromLine = line.split(' ') returnMat[index,:] = listFromLine[0:3] classLabelVector.append(int(listFromLine[-1])) index += 1 return returnMat,classLabelVector 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)) #element wise divide return normDataSet, ranges, minVals def datingClassTest(): hoRatio = 0.50 #hold out 10% datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') #load data setfrom file 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 rate is: %f" % (errorCount/float(numTestVecs)) print errorCount 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 def handwritingClassTest(): hwLabels = [] trainingFileList = listdir('trainingDigits') #load the training set m = len(trainingFileList) trainingMat = zeros((m,1024)) for i in range(m): #读取图片文件名 0_1.txt fileNameStr = trainingFileList[i] fileStr = fileNameStr.split('.')[0] #take off .txt classNumStr = int(fileStr.split('_')[0]) #标记标签 hwLabels.append(classNumStr) trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr) #测试模型 testFileList = listdir('testDigits') #iterate through the test set errorCount = 0.0 mTest = len(testFileList) for i in range(mTest): fileNameStr = testFileList[i] fileStr = fileNameStr.split('.')[0] #take off .txt 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/float(mTest)) if __name__ == "__main__": # datingDataMat,datingLabels = file2matrix('datingTestSet2.txt') # ranges= autoNorm(datingDataMat) # fig = plt.figure() # ax = fig.add_subplot(111) # ax.scatter(datingDataMat[:,1],datingDataMat[:,2],s = 15.0*array(datingLabels),c = array(datingLabels)) # ax.scatter(datingDataMat[:,1],datingDataMat[:,2],c = 'r') # dataMatX = array([[1,2,3],[4,5,6],[7,8,9]]) # dataMatY = array([[2,4,6],[8,10,12],[14,16,18]]) # big = array([11,21,31]) # ax.scatter(dataMatX[:,1],dataMatY[:,1],s = 15.0*big,c = big) # plt.show() # print array(datingLabels) # print datingDataMat handwritingClassTest()