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《机器学习实战》之k-近邻算法(手写识别系统)

时间:2018-02-14 00:17:06      阅读:23      评论:0      收藏:0      [点我收藏+]

标签:ref   append   error   机器   http   k-近邻算法   9.png   ems   with   

这个玩意和改进约会网站的那个差不多,它是提前把所有数字转换成了32*32像素大小的黑白图,然后转换成字符图(用0,1表示),将所有1024个像素点用一维矩阵保存下来,这样就可以通过knn计算欧几里得距离来得到最接近的答案。

 1 import os
 2 import operator
 3 from numpy import *
 4 
 5 def classify0(inX, dataSet, labels, k):
 6     dataSetSize = dataSet.shape[0]
 7     diffMat = tile(inX, (dataSetSize,1)) - dataSet  #统一矩阵,实现加减
 8     sqDiffMat = diffMat**2
 9     sqDistances = sqDiffMat.sum(axis=1)  #进行累加,axis=0是按列,axis=1是按行
10     distances = sqDistances**0.5  #开根号
11     sortedDistIndicies = distances.argsort()  #按升序进行排序,返回原下标
12     classCount = {}
13     for i in range(k):
14         voteIlabel = labels[sortedDistIndicies[i]]
15         classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1  #get是字典中的方法,前面是要获得的值,后面是若该值不存在时的默认值
16     sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
17     return sortedClassCount[0][0]
18 
19 
20 def img2vector(filename):
21     f = open(filename)
22     returnVect = zeros((1,1024))
23     for i in range(32):
24         line = f.readline()
25         for j in range(32):
26             returnVect[0,i*32+j] = int(line[j])
27     return returnVect
28 
29 
30 def handwritingClassTest():
31     fileList = os.listdir(trainingDigits)
32     m = len(fileList)
33     traingMat = zeros((m, 1024))
34     hwlabels = []
35     for i in range(m):
36         fileName = fileList[i]
37         prefix = fileName.split(.)[0]
38         number = int(prefix.split(_)[0])
39         hwlabels.append(number)
40         traingMat[i,:] = img2vector(trainingDigits/%s %fileName)
41     testFileList = os.listdir(testDigits)
42     m = len(testFileList)
43     errorNum = 0.0
44     for i in range(m):
45         testFileName = testFileList[i]
46         prefix = testFileList[i].split(.)[0]
47         realNumber = int(prefix.split(_)[0])
48         testMat = img2vector(testDigits/%s %testFileName)
49         testResult = classify0(testMat, traingMat, hwlabels, 3)
50         if testResult != realNumber:
51             errorNum += 1
52         print(The classifier came back with: %d, the real answer is: %d %(testResult, realNumber))
53     print(错误率为%f %(errorNum/float(m)))
54 
55 if __name__ == __main__:
56     handwritingClassTest()

技术分享图片

 

《机器学习实战》之k-近邻算法(手写识别系统)

标签:ref   append   error   机器   http   k-近邻算法   9.png   ems   with   

原文地址:https://www.cnblogs.com/zyb993963526/p/8447681.html

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