91 lines
2.5 KiB
Python
91 lines
2.5 KiB
Python
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# -*- coding: UTF-8 -*-
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import numpy as np
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import operator
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from os import listdir
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from sklearn.svm import SVC
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"""
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Author:
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Jack Cui
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Blog:
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http://blog.csdn.net/c406495762
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Zhihu:
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https://www.zhihu.com/people/Jack--Cui/
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Modify:
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2017-10-04
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"""
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def img2vector(filename):
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"""
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将32x32的二进制图像转换为1x1024向量。
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Parameters:
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filename - 文件名
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Returns:
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returnVect - 返回的二进制图像的1x1024向量
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"""
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#创建1x1024零向量
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returnVect = np.zeros((1, 1024))
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#打开文件
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fr = open(filename)
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#按行读取
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for i in range(32):
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#读一行数据
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lineStr = fr.readline()
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#每一行的前32个元素依次添加到returnVect中
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for j in range(32):
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returnVect[0, 32*i+j] = int(lineStr[j])
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#返回转换后的1x1024向量
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return returnVect
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def handwritingClassTest():
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"""
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手写数字分类测试
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Parameters:
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无
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Returns:
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无
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"""
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#测试集的Labels
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hwLabels = []
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#返回trainingDigits目录下的文件名
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trainingFileList = listdir('trainingDigits')
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#返回文件夹下文件的个数
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m = len(trainingFileList)
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#初始化训练的Mat矩阵,测试集
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trainingMat = np.zeros((m, 1024))
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#从文件名中解析出训练集的类别
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for i in range(m):
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#获得文件的名字
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fileNameStr = trainingFileList[i]
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#获得分类的数字
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classNumber = int(fileNameStr.split('_')[0])
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#将获得的类别添加到hwLabels中
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hwLabels.append(classNumber)
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#将每一个文件的1x1024数据存储到trainingMat矩阵中
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trainingMat[i,:] = img2vector('trainingDigits/%s' % (fileNameStr))
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clf = SVC(C=200,kernel='rbf')
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clf.fit(trainingMat,hwLabels)
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#返回testDigits目录下的文件列表
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testFileList = listdir('testDigits')
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#错误检测计数
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errorCount = 0.0
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#测试数据的数量
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mTest = len(testFileList)
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#从文件中解析出测试集的类别并进行分类测试
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for i in range(mTest):
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#获得文件的名字
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fileNameStr = testFileList[i]
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#获得分类的数字
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classNumber = int(fileNameStr.split('_')[0])
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#获得测试集的1x1024向量,用于训练
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vectorUnderTest = img2vector('testDigits/%s' % (fileNameStr))
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#获得预测结果
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# classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
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classifierResult = clf.predict(vectorUnderTest)
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print("分类返回结果为%d\t真实结果为%d" % (classifierResult, classNumber))
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if(classifierResult != classNumber):
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errorCount += 1.0
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print("总共错了%d个数据\n错误率为%f%%" % (errorCount, errorCount/mTest * 100))
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if __name__ == '__main__':
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handwritingClassTest()
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