机器学习实战笔记-K近邻算法3(手写识别系统)

1 准备数据：将图像转换为测试向量

这次数据集还是有两种，训练数据集和测试数据集，分别有2000个，900个。

我们将把一个32*32的二进制图像矩阵转换为1 x 1024的向量，这样前两节使用的分类器就可以处理数字图像信息了。

代码:

def img2vector(filename): 
    returnVect = zeros((1,1024)) 
    file = open(filename) 
    for i in range(32): 
        line = file.readline() 
        for j in range(32): 
            returnVect[0,i*32+j] = line[j] 
    return returnVect

效果截图:

测试算法

代码:

def handWritingTest(): 
    hwLabels = [] 
    trainingFileList = os.listdir(‘trainingDigits‘) 
    trainingFileLength = len(trainingFileList) 
    trainingMat = zeros((trainingFileLength,1024)) 
    for i in range(trainingFileLength): 
        fileNameStr = trainingFileList[i] 
        className = fileNameStr.split(‘_‘)[0] 
        hwLabels.append(int(className)) 
        fileVector = img2vector(‘trainingDigits/‘ + fileNameStr) 
        trainingMat[i,:] = fileVector 
    testFileList = os.listdir(‘testDigits‘) 
    testFileLength = len(testFileList) 
    errorCount = 0.0 
    for i in range(testFileLength): 
        fileNameStr = testFileList[i] 
        className = int(fileNameStr.split(‘_‘)[0]) 
        fileVector = img2vector(‘testDigits/‘ + fileNameStr) 
        testResult = classify0(fileVector,trainingMat,hwLabels,3) 
        print("the classifier came back with: %d, the real answer is: %d" % (testResult,className)) 
        if(testResult != className): 
            errorCount+=1.0 
    errorRate = errorCount/float(testFileLength) 
    print("the errorRate is : %f" % errorRate)

结果截图:

分别将k改为4,5:

可以发现错误率逐渐增高