【机器学习五】KNN

代码如下，其中数据集trainingDigits可以从我的 网盘.上下载，提取码：cbun 永久有效

#手写识别 32x32
from numpy import *
import operator
import time
from os import listdir

def classify(inputPoint,dataSet,labels,k):
    dataSetSize = dataSet.shape[0]     #已知分类的数据集（训练集）的行数
    #先tile函数将输入点拓展成与训练集相同维数的矩阵，再计算欧氏距离
    diffMat = tile(inputPoint,(dataSetSize,1))-dataSet  #样本与训练集的差值矩阵
    sqDiffMat = diffMat ** 2                    #差值矩阵平方
    sqDistances = sqDiffMat.sum(axis=1)         #计算每一行上元素的和
    distances = sqDistances ** 0.5              #开方得到欧拉距离矩阵
    sortedDistIndicies = distances.argsort()    #按distances中元素进行升序排序后得到的对应下标的列表
    #选择距离最小的k个点
    classCount = {}
    for i in range(k):
        voteIlabel = labels[ sortedDistIndicies[i] ]
        classCount[voteIlabel] = classCount.get(voteIlabel,0)+1
    #按classCount字典的第2个元素（即类别出现的次数）从大到小排序
    sortedClassCount = sorted(classCount.items(), key = operator.itemgetter(1), reverse = True)
    return sortedClassCount[0][0]

def img2vector(filename):
    returnVect = []
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect.append(int(lineStr[j]))
    return returnVect
#从文件名中解析分类数字
def classnumCut(fileName): 
    fileStr = fileName.split('.')[0]  
    classNumStr = int(fileStr.split('_')[0]) 
    return classNumStr
#构建训练集数据向量，及对应分类标签向量
def trainingDataSet():
    hwLabels = []
    trainingFileList = listdir('trainingDigits')           #获取目录内容
    m = len(trainingFileList)
    trainingMat = zeros((m,1024))                          #m维向量的训练集
    for i in range(m):
        fileNameStr = trainingFileList[i]
        hwLabels.append(classnumCut(fileNameStr))
        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)
    return hwLabels,trainingMat

#测试函数
def handwritingTest():
    hwLabels,trainingMat = trainingDataSet()    #构建训练集
    testFileList = listdir('testDigits')        #获取测试集
    errorCount = 0.0                            #错误数
    mTest = len(testFileList)                   #测试集总样本数
    t1 = time.time()
    
    for i in range(mTest):
        fileNameStr = testFileList[i]
        classNumStr = classnumCut(fileNameStr)
        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
        #调用knn算法进行测试
        classifierResult = classify(vectorUnderTest, trainingMat, hwLabels, 3)
        if (classifierResult != classNumStr): errorCount += 1.0
    print("\nthe total number of tests is: %d" % mTest  )             #输出测试总样本数
    print("the total number of errors is: %d" % errorCount    )       #输出测试错误样本数
    print("the total error rate is: %f" % (errorCount/float(mTest)) ) #输出错误率
    t2 = time.time()
    print("Cost time: %.2fmin, %.4fs."%((t2-t1)//60,(t2-t1)%60) )    #测试耗时

if __name__ == "__main__":
    handwritingTest()
    

最后运行的结果：

the total number of tests is: 946
the total number of errors is: 10
the total error rate is: 0.010571
Cost time: 0.00min, 44.5615s.

在测试集上效果还是很好的。