KNN算法实战——手写数字识别

KNN算法简介

KNN算法的简介可参考：K-近邻算法（KNN）

手写数字识别

kNN算法主要被应用于文本分类、相似推荐，本文将描述一个分类的例子。

何为手写识别？可参考维基百科介绍：手写识别

数据下载：手写识别数据

数据说明：每个手写数字已经事先处理成32*32的二进制文本，存储格式为txt文件。分为训练样本和测试样本：“trainingDigits”、“testDigits”。

编程实现步骤：

将每个图片（即txt文本，以下提到图片都指txt文本）转化为一个向量，即32*32的数组转化为1*1024的数组，这个1*1024的数组用机器学习的术语来说就是特征向量;

训练样本中有10*10个图片，可以合并成一个100*1024的矩阵，每一行对应一个图片;

测试样本中有10*5个图片，我们要让程序自动判断每个图片所表示的数字。同样的，对于测试图片，将其转化为1*1024的向量，然后计算它与训练样本中各个图片的“距离”（这里两个向量的距离采用欧式距离），然后对距离排序，选出较小的前k个，因为这k个样本来自训练集，是已知其代表的数字的，所以被测试图片所代表的数字就可以确定为这k个中出现次数最多的那个数字。

代码如下：

from numpy import *
import operator
from os import listdir

def classify0(inX, dataSet, labels, k):
dataSetSize = dataSet.shape[0]
diffMat = tile(inX, (dataSetSize,1)) - dataSet
sqDiffMat = diffMat**2
sqDistances = sqDiffMat.sum(axis=1)
distances = sqDistances**0.5
sortedDistIndicies = distances.argsort()
classCount={}
for i in range(k):
voteIlabel = labels[sortedDistIndicies[i]]
classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedClassCount[0][0]

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

def handwritingClassTest():
hwLabels = []
trainingFileList = listdir('trainingDigits')
m = len(trainingFileList)
trainingMat = zeros((m,1024))
for i in range(m):
fileNameStr = trainingFileList[i]
fileStr = fileNameStr.split('.')[0]
classNumStr = int(fileStr.split('_')[0])
hwLabels.append(classNumStr)
trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)
testFileList = listdir('testDigits')
errorCount = 0.0
mTest = len(testFileList)
for i in range(mTest):
fileNameStr = testFileList[i]
fileStr = fileNameStr.split('.')[0]
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))

handwritingClassTest()

输出结果：

the classifier came back with: 4, the real answer is: 4
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 8, the real answer is: 8
the classifier came back with: 8, the real answer is: 8
the classifier came back with: 5, the real answer is: 5
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 6, the real answer is: 6
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 7, the real answer is: 7
the classifier came back with: 0, the real answer is: 0
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 7, the real answer is: 7
the classifier came back with: 8, the real answer is: 8
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 7, the real answer is: 7
the classifier came back with: 4, the real answer is: 4
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 0, the real answer is: 0
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 8, the real answer is: 8
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 0, the real answer is: 0
the classifier came back with: 6, the real answer is: 6
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 6, the real answer is: 6
the classifier came back with: 8, the real answer is: 8
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 4, the real answer is: 4
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 4, the real answer is: 4
the classifier came back with: 0, the real answer is: 0
the classifier came back with: 4, the real answer is: 4
the classifier came back with: 5, the real answer is: 5
the classifier came back with: 3, the real answer is: 3
the classifier came back with: 7, the real answer is: 7
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 6, the real answer is: 6
the classifier came back with: 1, the real answer is: 1
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 0, the real answer is: 0
the classifier came back with: 2, the real answer is: 2
the classifier came back with: 7, the real answer is: 7
the classifier came back with: 9, the real answer is: 9
the classifier came back with: 5, the real answer is: 5
the classifier came back with: 5, the real answer is: 5
the classifier came back with: 6, the real answer is: 6
the classifier came back with: 5, the real answer is: 5

the total number of errors is: 0

the total error rate is: 0.000000

因为用的训练集和测试集都比较小，所以凑巧没有识别错误的情况。