一、题目
https://www.kaggle.com/c/digit-recognizer
二、kNN算法
请参考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
五、参考
https://blog.csdn.net/u012162613/article/details/41929171
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