本文主要利用k-近邻分类器实现手写识别系统,训练数据集大约2000个样本,每个数字大约有200个样本,每个样本保存在一个txt文件中,手写体图像本身是32X32的二值图像,如下图所示:
首先,我们需要将图像格式化处理为一个向量,把一个32X32的二进制图像矩阵通过img2vector()函数转换为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
手写数字识别系统的测试代码:
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命令提示符中输入kNN.handwritingClassTest(),测试该函数的输出结果:
注:本文的相关代码均来源于Peter Harringtor的《机器学习实战》
时间: 2024-10-09 22:11:30