原理

算法流程

基于KNN的手写数字识别

在线运行：基于KNN的手写数字识别

数据样式：

这是一个32行32列的文本表示的图像，首先把HW的形式转成一维的向量：

# 文本转向量, 32行32列转1 * 1024
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

读取全部的训练数据成矩阵形式：(N,1024)

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)

这个trainingMat可以用来和测试数据计算欧氏距离，然后计算trainingMat中最近的前K个样本的类别，统计出现最多的类别就是当前测试数据的类别了⬇️：

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)))

测试效果