Kaggle-Digit Recognizer kNN解决方案

左手的ㄟ右手 2022-05-28 08:35 145阅读 0赞

# 一、题目 #

[https://www.kaggle.com/c/digit-recognizer][https_www.kaggle.com_c_digit-recognizer]

# 二、kNN算法 #

请参考[https://www.jianshu.com/p/dddd1c348553][https_www.jianshu.com_p_dddd1c348553]

# 三、代码 #

编程语言使用python 3.6

from numpy import *
    import operator
    import csv
    import pdb
    
    def toInt(array):
        array=mat(array)
        m,n=shape(array)
        newArray=zeros((m,n))
        for i in range(m):
            for j in range(n):
                    newArray[i,j]=int(array[i,j])
        return newArray
    
    def nomalizing(array):
        m,n=shape(array)
        for i in range(m):
            for j in range(n):
                if array[i,j]!=0:
                    array[i,j]=1
        return array
    
    def loadTrainData():
        l=[]
        with open('train.csv') as file:
             lines=csv.reader(file)
             for line in lines:
                 l.append(line) #42001*785
        l.remove(l[0])  # remove row 0, get 42000*785
        l=array(l)      # transfer list to array
        label=l[:,0]    # 从0行0列取到最后一行0列, 显示为1行42000列，而不是42000行1列
        data=l[:,1:]    # from row 0 to the last row, from col 1 to the last col, 42000*784
        return nomalizing(toInt(data)),toInt(label)  #data 42000*784 label 1*42000 
    
    def loadTestData():
        l=[]
        with open('test.csv') as file:
             lines=csv.reader(file)
             for line in lines:
                 l.append(line)
         #28001*784
        l.remove(l[0]) #28000*784
        data=array(l)
        return nomalizing(toInt(data))  # data 28000*784
    
    def loadTestResult():
        l=[]
        with open('knn_benchmark.csv') as file:
             lines=csv.reader(file)
             for line in lines:
                 l.append(line)
         #28001*2
        l.remove(l[0]) #28000*2
        label=array(l)  
        return toInt(label[:,1])  # label 1*28000
    
    #inX:1*n dataSet:m*n labels:m*1 
    def classify(inX, dataSet, labels, k): 
        inX=mat(inX)
        dataSet=mat(dataSet)
        labels=mat(labels)
        dataSetSize = dataSet.shape[0]                  
        diffMat = tile(inX, (dataSetSize,1)) - dataSet   
        sqDiffMat = array(diffMat)**2
        sqDistances = sqDiffMat.sum(axis=1)                  
        distances = sqDistances**0.5
        sortedDistIndicies = distances.argsort()            
        classCount={}                                      
        for i in range(k):
            voteIlabel = labels[sortedDistIndicies[i],0]
            classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1
    
        sortedClassCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)
        return sortedClassCount[0][0]
    
    def saveResult(result):
        with open('result.csv', 'w', newline = '') as myFile:    
            myWriter=csv.writer(myFile)
            for i in result:
                tmp=[]
                tmp.append(i)
                myWriter.writerow(tmp)
    
    
    def Test():
        trainData,trainLabel=loadTrainData()
        testData=loadTestData()
        testLabel=loadTestResult()
        m,n=shape(testData)
        errorCount=0
        resultList=[]
        for i in range(m):
            print ("classify: ",i)
    
            classifierResult = classify(testData[i], trainData[0:20000], trainLabel.transpose()[0:20000], 5)
            resultList.append(classifierResult)
            print ("the classifier came back with: %d, the real answer is: %d" % (classifierResult, testLabel[0,i]))
            if (classifierResult != testLabel[0,i]): 
                errorCount += 1.0
            print ("\nthe total number of errors is: %d" % errorCount)
            print ("\nthe total error rate is: %f" % (errorCount/float(m)))
        saveResult(resultList)

运行程序：  
打开cmd窗口，进入上述代码knn.py所在的目录，进入python环境  
执行命令

import knn
    knn.Test()

![execute.png][]

运行结果：

![result.png][]

# 四、Github代码下载 #

[下载地址][Link 1]

# 五、参考 #

[https://blog.csdn.net/u012162613/article/details/41929171][https_blog.csdn.net_u012162613_article_details_41929171]

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> ![feicuisenlin\_12x12.jpg][feicuisenlin_12x12.jpg]

[https_www.kaggle.com_c_digit-recognizer]: https://www.kaggle.com/c/digit-recognizer
[https_www.jianshu.com_p_dddd1c348553]: https://www.jianshu.com/p/dddd1c348553
[execute.png]: /images/20220528/b8de4e434a994405936aa29454eae3a3.png
[result.png]: /images/20220528/81aa4acc77eb492f9f9987f8f854da2b.png
[Link 1]: https://github.com/zhenghaishu/Kaggle/tree/master/Digit%20Recognizer
[https_blog.csdn.net_u012162613_article_details_41929171]: https://blog.csdn.net/u012162613/article/details/41929171
[feicuisenlin_12x12.jpg]: /images/20220528/c2a49bd0f81c4723a4dad24fe5625a7e.png