Kaggle-Digit Recognizer kNN解决方案

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)

import knn
knn.Test()