2.3示例：手寫識別系統 2.3.1準備資料：將影象轉換為測試向量

def img2vector(filename):
    returnVect = np.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

在python命令列中輸入下列命令測試img2vector函式

在測試函式時容易出現如下圖中的錯誤
檔案在多級目錄下，檔名由數字組成，程式不可執行，此時應記得對檔名進行轉義 2.3.2 測試演算法：使用k-近鄰演算法識別手寫數字 將以下手寫數字識別系統的測試程式碼寫入kNN.py檔案之前，必須將from os import listdir 寫入檔案的起始部分，該函式可以列出給定目錄的檔名

def handwritingClassTest():
    hwLabels = []
    trainingFileList = listdir('F:\\MachineLearninginaction\\Ch02\\trainingDigits')           #load the training set
    m = len(trainingFileList)
    trainingMat = np.zeros((m,1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]     #take off .txt
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i,:] = img2vector('F:\\MachineLearninginaction\\Ch02\\trainingDigits\\%s' % fileNameStr)
    testFileList = listdir('F:\\MachineLearninginaction\\Ch02\\testDigits')        #iterate through the test set
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]     #take off .txt
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('F:\\MachineLearninginaction\\Ch02\\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 errors is: %d" % errorCount
    print "\nthe total error rate is: %f" % (errorCount/float(mTest))
 

此時同樣要注意對檔名進行轉義 在python命令提示符中輸入kNN.handwritingClassTest（），測試該函式輸出結果。輸出結果如下圖所示：