Add a wrapper to this function so that it computers multiple runs (a user-specified value) and computes an average accuracy over the multiple runs rather than a single accuracy?

Code:

importcsv

importrandom

importmath

importoperator

importurllib.request

defgetLines(filename):

lines=[]

if(filename.startswith(('http','ftp','sftp'))):

#Skipdownloadingitandopendirectlyonline:

response=urllib.request.urlopen(filename)

lines=csv.reader(response.read().decode('utf-8').splitlines())

else:

#TutorialsPointIDErequires'r',not'rb'

withopen(filename,'r')ascsvfile:

#csvreaderisanobjectthatisessentiallyalistoflists

csvreader=csv.reader(csvfile)

forlineincsvreader:

lines.append(line)

returnlines

defloadDatasetFinal(filename,split,trainingSet=[],testSet=[]):

lines=getLines(filename)

#forrowinlines:

#print(','.join(row))

dataset=list(lines)

forxinrange(len(dataset)-1):

foryinrange(4):#legend=('Sepallength','Sepalwidth','Petallength','Petalwidth')

dataset[x][y]=float(dataset[x][y])

ifrandom.random()

trainingSet.append(dataset[x])

else:

testSet.append(dataset[x])

defeuclideanDistance(instance1,instance2,length):

distance=0

forxinrange(length):

distance+=pow((instance1[x]-instance2[x]),2)

returnmath.sqrt(distance)

defgetNeighbors(trainingSet,testInstance,k):

distances=[]

length=len(testInstance)-1

forxinrange(len(trainingSet)):

dist=euclideanDistance(testInstance,trainingSet[x],length)

distances.append((trainingSet[x],dist))

distances.sort(key=operator.itemgetter(1))

neighbors=[]

forxinrange(k):

neighbors.append(distances[x][0])

returnneighbors

defgetResponse(neighbors):

classVotes={}

forxinrange(len(neighbors)):

response=neighbors[x][-1]

ifresponseinclassVotes:

classVotes[response]+=1

else:

classVotes[response]=1

sortedVotes=sorted(classVotes.items(),key=operator.itemgetter(1),reverse=True)

returnsortedVotes[0][0]

defgetAccuracy(testSet,predictions):

correct=0

forxinrange(len(testSet)):

iftestSet[x][-1]==predictions[x]:

correct+=1

return(correct/float(len(testSet)))*100.0

defmain():

#setourparameters

k=3

split=0.67

filename='iris.data'

filename='https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data'

#preparedata

trainingSet=[]

testSet=[]

loadDatasetFinal(filename,split,trainingSet,testSet)

print('Trainset:'+repr(len(trainingSet)))

print('Testset:'+repr(len(testSet)))

#generatepredictions

predictions=[]

forxinrange(len(testSet)):

neighbors=getNeighbors(trainingSet,testSet[x],k)

result=getResponse(neighbors)

predictions.append(result)

print('>predicted='+repr(result)+',actual='+repr(testSet[x][-1]))

accuracy=getAccuracy(testSet,predictions)

print('Accuracy:'+repr(accuracy)+'%')

main()