本文共 2713 字,大约阅读时间需要 9 分钟。
from numpy import * '''numpy科学计算包,在抽象和处理矩阵运算上具有优势'''import operator'''导入数据,创建数据集和标签'''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'''实施kNN算法,构造分类器''''''inX:进行分类的数据;dataSet:训练样本集;labels:标签向量;k:KNN算法的参数k,用来选择最近邻居'''def classify0(inX, dataSet, labels, k): dataSetSize = dataSet.shape[0] '''读取矩阵dataSet第一维度的长度即行数''' diffMat = tile(inX, (dataSetSize,1)) - dataSet '''将待进行分类的数据复制dataSetSize份,diffMat得到了目标与训练数值之间的差值''' sqDiffMat = diffMat**2 '''各个元素分别平方''' sqDistances = sqDiffMat.sum(axis=1) '''每一行元素之和''' distances = sqDistances**0.5'''开方求距离''' sortedDistIndicies = distances.argsort() '''将distance进行排序:由小到大''' classCount={} '''选择距离最小的K个点''' for i in range(k): voteIlabel = labels[sortedDistIndicies[i]] classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1 '''统计最后出现结果的不同标签值的数量,出现一次便加1,以此类推''' sortedclassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1),reverse=True)'''按照从大到小的次序排序,最后返回发生频率最高的元素标签'''return sortedclassCount[0][0]def file2matrix(filename): fr = open(filename) arrayOLines = fr.readlines() '''按行读取文件''' numberOfLines = len(arrayOLines) '''文件的函数即为返回矩阵的行数''' returnMat = zeros((numberOfLines,3)) '''创建行数为numberOfLines,列数为3的0矩阵''' classLabelVector = [] index = 0 for line in arrayOLines: line = line.strip() listFromLine = line.split('\t') returnMat[index,:] = [0:3] classLabelVector.append(int (listFromLine[-1])) '''将列表的最后一列存储在classLabelVector中,此处明确的通知解释器列表所存储的元素值为整型,否则Python语言会将这些元素当做字符串来处理''' index += 1 return returnMat,classLabelVector'''归一化特征值'''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'''分类器针对约会网站的测试代码''' 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
转载地址:http://pcaci.baihongyu.com/