本文主要利用k-近鄰分類器實現手寫識別系統，訓練數據集大約2000個樣本，每個數字大約有200個樣本，每個樣本保存在一個txt文件中，手寫體圖像本身是32X32的二值圖像，如下圖所示：
首先，我們需要將圖像格式化處理為一個向量，把一個32X32的二進制圖像矩陣通過img2vector()函數轉換為1X1024的向量：
~~~
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
~~~
手寫數字識別系統的測試代碼：
~~~
def handwritingClassTest():
hwLabels = []
trainingFileList = listdir('trainingDigits') #load the training set
m = len(trainingFileList)
trainingMat = 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('trainingDigits/%s' % fileNameStr)
testFileList = listdir('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('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))
~~~
在Python命令提示符中輸入kNN.handwritingClassTest()，測試該函數的輸出結果：
注：本文的相關代碼均來源于Peter Harringtor的《機器學習實戰》
