k-近邻算法

基于python3实现的《机器学习实战》ch2 k-邻近算法

1.kNN算法伪代码如下：

对未知类别属性的数据集中的每个点依次执行以下操作：
1）计算已知类别数据集中的点与当前点之间的距离；
2）按照距离递增次序排序；
3）选取与当前点距离最近的k个点；
4）确定前k个点所在类别的出现频率；
5）返回前k个点出现频率最高的类别作为当前点的预测分类。

2.程序2-1 k邻近算法

def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    #采用欧式距离公式计算距离
    diffMat = tile(inX, (dataSetSize,1)) - dataSet
    sqDiffMat = diffMat**2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances**0.5
    
    sortedDistIndicies = distances.argsort()     
    classCount={} 
    
    #统计当前点与已知数据集距离最近的k个点的类别         
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

上面的代码是基于python2，在python3下运行会出现以下错误：
AttributeError: ‘dict’ object has no attribute 'iteritems’
将代码中的classCount.iteritems()改为classCount.items()即可

3.使用kNN改进约会网站的配对结果

数据集下载链接：链接: https://pan.baidu.com/s/1WcOfYaC_lNSpOHU-NyQ-mA 提取码: kiwj
数据集共有1000行，前3列分别表示：每年获得的飞行常客里程数、玩视频游戏所消耗的时间百分比、每周消耗的冰淇淋公升数，最后一列表示所属的类别，其中1表示：‘not at all’, 2表示：‘in small doses’, 3表示：‘in large doses’

#程序2-2 将文本记录转换为Numpy的解析程序 
def file2matrix(filename):
    fr = open(filename)
    arrayOlines = fr.readlines()
    numberOfLines = len(arrayOlines)
    returnMat = zeros((numberOfLines,3))
    classLabelVector = []
    index = 0
    for line in arrayOlines:
        line = line.strip() #截掉回车字符
        listFromLine = line.split('\t')
        returnMat[index, :] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat,classLabelVector
  
#特征归一化
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))
    return normDataSet, ranges, minVals

#测试代码
def datingClassTest():
    hoRatio = 0.50
    datingDataMat, datingLabels = file2matrix("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 rate is: %f" % (errorCount / float(numTestVecs)))
 
#约会网站的预测函数       
def classifyPerson():
    resultList = ['not at all', 'in small doses', 'in large doses']
    percentTats = float(input("percentage of time spent playing video games?"))
    ffMiles = float(input("frequent flier miles earned per year?"))
    iceCram = float(input("liters of ice cream consumed per year?"))
    datingDataMat, datingLabels = file2matrix("datingTestSet2.txt")
    normMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = array([ffMiles, percentTats, iceCram])
    classifierResult = classify0((inArr - minVals) / ranges, normMat, datingLabels, 3)
    print("You will probably like this person:", resultList[classifierResult - 1])
    

4.手写识别系统

数据集：链接: https://pan.baidu.com/s/1D_lzQV-0bg1suq_oCnMk-g 提取码: jdgj
数据集中的文件名如0_75.txt，1_47.txt等，其中_之前表示该数据表示的数字。

#图像转向量
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

def handWritingClassTest():
    hwLabels = []
    trainingFileList = listdir('trainingDigits')
    m = len(trainingFileList)
    trainingMat = zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0]) #从文件名中解析其所属的分类
        hwLabels.append(classNumStr)
        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("\nthe total number of error is: %d" % errorCount)
    print("\nthe total error rate is: %f" % (errorCount / float(mTest)))