Python机器学习实战笔记之KNN算法

<pre name="code" class="plain"><pre name="code" class="plain">1、k-近邻算法
测量不同特征值之间的距离方法进行分类
优 点 ：精度高、对异常值不敏感、无数据输入假定。
缺点：计算复杂度高、空间复杂度高。
适用数据范围：数值型和标称型。

(常用欧氏距离)
1收集数据2准备数据3分析数据4训练算法5测试算法6使用算法

Python中识别中文
文件开头添加
#coding:utf-8

k近邻法与kd树
为了提高k近邻搜索的效率，可以考虑使用特殊的结构存储训练数据，以减少计算距离的次数。具体方法有很多，这里介绍kd树方法
参考 http://blog.csdn.net/qll125596718/article/details/8426458[/code] 
Python版实现 http://blog.csdn.net/q383700092/article/details/51757762 R语言版调用函数 http://blog.csdn.net/q383700092/article/details/51759313 MapReduce简化实现版 http://blog.csdn.net/q383700092/article/details/51780865 spark版后续添加
<span style="font-size: 13.3333px;">from numpy import * # 科学计算包</span>
import operator  # 运算符模块from os import listdirdef createDataSet():group = array([[1.0,1.1],[1.0,1.0],[0,0],[0,0.1]])labels = ['A','A','B','B']return group, labels#KNN算法核心 inx需要分类的向量，训练样本dataSet标签向量labels 近邻的数目#调用格式KNN.classify0([0,0], group, labels, 3)def classify0(inX, dataSet, labels, k):dataSetSize = dataSet.shape[0]  #向量大小ndiffMat = tile(inX, (dataSetSize,1)) - dataSet  #分类向量1重复n次减去训练样本sqDiffMat = diffMat**2  #**代表幂计算 2次方sqDistances = sqDiffMat.sum(axis=1)  #计算每行的和distances = sqDistances**0.5  #每个数开根号sortedDistIndicies = distances.argsort()  #升序排序后的数据原来位置的下标classCount={}for i in range(k):voteIlabel = labels[sortedDistIndicies[i]] #將排序后的labers輸出(从多到少的标号选出3个)classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 #get(k,d)如果k不在classCount为d#将classCount按照第二字段排序sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)#返回最近3个值的里最近的那个值的标签return sortedClassCount[0][0]#将文本记录到转换NumPy的解析程序#datingDataMat,datingLabels=KNN.file2matrix('G:/python/pythonwork/datingTestSet2.txt')def file2matrix(filename):fr = open(filename)numberOfLines = len(fr.readlines())         #get the number of lines in the filereturnMat = zeros((numberOfLines,3))        #prepare matrix to returnclassLabelVector = []                       #prepare labels returnfr = open(filename)index = 0for line in fr.readlines():line = line.strip()  #去除首尾空格listFromLine = line.split('\t')returnMat[index,:] = listFromLine[0:3]classLabelVector.append(int(listFromLine[-1]))  #索引值-1表示列表中的最后一列元素index += 1return returnMat,classLabelVector#归一化#normMat, ranges, minVals=KNN.autoNorm(datingDataMat)def autoNorm(dataSet):minVals = dataSet.min(0)maxVals = dataSet.max(0)ranges = maxVals - minValsnormDataSet = zeros(shape(dataSet))m = dataSet.shape[0]normDataSet = dataSet - tile(minVals, (m,1))normDataSet = normDataSet/tile(ranges, (m,1))   #element wise dividereturn normDataSet, ranges, minVals#分类器结果 KNN.datingClassTest()def datingClassTest():hoRatio = 0.50      #hold out 10%datingDataMat,datingLabels = file2matrix('G:/python/pythonwork/datingTestSet2.txt')       #load data setfrom filenormMat, ranges, minVals = autoNorm(datingDataMat)m = normMat.shape[0]numTestVecs = int(m*hoRatio)errorCount = 0.0for 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 += 1.0print "the total error rate is: %f" % (errorCount/float(numTestVecs))print "error: %f,total:  %d" % (errorCount,numTestVecs)
数字识别
#将图像转换为向量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#构建手写识别系统def handwritingClassTest():hwLabels = []trainingFileList = listdir('G:/python/pythonwork/trainingDigits')           #load the training setm = len(trainingFileList)trainingMat = zeros((m,1024))for i in range(m):fileNameStr = trainingFileList[i]fileStr = fileNameStr.split('.')[0]     #take off .txtclassNumStr = int(fileStr.split('_')[0])hwLabels.append(classNumStr)trainingMat[i,:] = img2vector('G:/python/pythonwork/trainingDigits/%s' % fileNameStr)testFileList = listdir('G:/python/pythonwork/testDigits')        #iterate through the test seterrorCount = 0.0mTest = len(testFileList)for i in range(mTest):fileNameStr = testFileList[i]fileStr = fileNameStr.split('.')[0]     #take off .txtclassNumStr = int(fileStr.split('_')[0])vectorUnderTest = img2vector('G:/python/pythonwork/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 += 1.0print "\nthe total number of errors is: %d" % errorCountprint "\nthe total error rate is: %f" % (errorCount/float(mTest))
k近邻法与kd树为了提高k近邻搜索的效率，可以考虑使用特殊的结构存储训练数据，以减少计算距离的次数。具体方法有很多，这里介绍kd树方法参考 http://blog.csdn.net/qll125596718/article/details/8426458