kaggle 训练赛（1）Digit Recognizer

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题目

识别手写数字

做法

开始做kaggle的第一套题，识别手写数字。每个数字是28*28的一个向量，朴素的跑了一个KNN，距离用的是欧几里得距离。最终成绩0.96

def knn(inX,num):    dataSet = trainMat    labels = labelList    k = 3    dataSetSize = dataSet.shape[0]    diffMat = tile(inX,(dataSetSize,1)) - dataSet    sqDiffMat = diffMat**2    sumDiffMat = sqDiffMat.sum(axis=1)    distances = sumDiffMat**0.5    sortedDistances = distances.argsort()    classCount = {}    for i in range(k):        vote = labels[sortedDistances[i]]        classCount[vote] = classCount.get(vote,0) + 1    # sortedClassCount = sorted(classCount,key=itemgetter('vote'))    max = 0    ans = ''    for k,v in classCount.items():        if(v>max):            ans = k            max = v    print(str(num+1) + ' = ' + ans)    outFile.write(str(num+1) + ',' + ans + '\n')    return

以后学到更多的知识再做优化。
在写法上，用上了Python的多线程来处理，节省了一定的时间

from multiprocessing.dummy import Pool    outFile = open("out2.csv",'w')    pool = Pool()    pool.starmap(knn,zip(testMat,range(n)))    pool.close()    pool.join()    outFile.close()

代码

from numpy import *import csvfrom multiprocessing.dummy import Pooldef knn_warp(args):    return knn(*args)def knn(inX,num):    dataSet = trainMat    labels = labelList    k = 3    dataSetSize = dataSet.shape[0]    diffMat = tile(inX,(dataSetSize,1)) - dataSet    sqDiffMat = diffMat**2    sumDiffMat = sqDiffMat.sum(axis=1)    distances = sumDiffMat**0.5    sortedDistances = distances.argsort()    classCount = {}    for i in range(k):        vote = labels[sortedDistances[i]]        classCount[vote] = classCount.get(vote,0) + 1    # sortedClassCount = sorted(classCount,key=itemgetter('vote'))    max = 0    ans = ''    for k,v in classCount.items():        if(v>max):            ans = k            max = v    print(str(num+1) + ' = ' + ans)    outFile.write(str(num+1) + ',' + ans + '\n')    returndef readTrain(row,i):    labelList[i] = row['label']    for x in range(0, 784):        trainMat[i, x] = int(row['pixel' + str(x)])    print(str(i))def readTest(row,i):    for x in range(0, 784):        testMat[i, x] = int(row['pixel' + str(x)])    print(str(i))global labelListglobal trainMatglobal outFileif __name__ == '__main__':    f = open('train.csv')    m = len(f.readlines())    m = m - 1    labelList = list(range(m))    trainMat = zeros((m,784))    f.close()    with open('train.csv') as f:        f_csv = csv.DictReader(f)        pool = Pool()        pool.starmap(readTrain, zip(f_csv, range(m)))        pool.close()        pool.join()    f = open('test.csv')    n = len(f.readlines())    n = n - 1    testMat = zeros((n,784))    f.close()    with open('test.csv') as f:        f_csv = csv.DictReader(f)        pool = Pool()        pool.starmap(readTest, zip(f_csv, range(n)))        pool.close()        pool.join()    outFile = open("out2.csv",'w')    pool = Pool()    pool.starmap(knn,zip(testMat,range(n)))    pool.close()    pool.join()    outFile.close()

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