1主要步骤：

``````import csv
import random
import math
import operator

def loadDataset(filename, split, trainingSet=[], testSet=[]):
with open(filename, 'rb') as csvfile:
lines = csv.reader(csvfile)
dataset = list(lines)
for x in range(len(dataset)-1):
for y in range(4):
dataset[x][y] = float(dataset[x][y])
if random.random() < split:
#if random.randrange(len(trainingSet)) < split:
trainingSet.append(dataset[x])
else:
testSet.append(dataset[x])

def euclideanDistance(instance1, instance2, length):
distance = 0
for x in range(length):
distance += pow((instance1[x]-instance2[x]), 2)
return math.sqrt(distance)

def getNeighbors(trainingSet, testInstance, k):
distances = []
length = len(testInstance)-1
for x in range(len(trainingSet)):
dist = euclideanDistance(testInstance, trainingSet[x], length)
distances.append((trainingSet[x], dist))
distances.sort(key=operator.itemgetter(1))
neighbors = []
for x in range(k):
neighbors.append(distances[x][0])
return neighbors

def getResponse(neighbors):
classVotes = {}
for x in range(len(neighbors)):
response = neighbors[x][-1]
if response in classVotes:
classVotes[response] += 1
else:
classVotes[response] = 1
sortedVotes = sorted(classVotes.iteritems(), key=operator.itemgetter(1), reverse=True)
return sortedVotes[0][0]

def getAccuracy(testSet, predictions):
correct = 0
for x in range(len(testSet)):
#print 'test'
# test = testSet[x][-1]
# print test
# print 'pre'
# pre = predictions[x]
# print pre
print ('test: ' + repr(testSet[x][-1])) repr(testSet[x][-1])
print ('pre: ' + repr(predictions[x]))
# if testSet[z][-1] == predictions[z]:
#     correct += 1
return (correct/float(len(testSet)))*100.0

def main():
#prepare data
"""

:rtype: object
"""
trainingSet = []
testSet = []
split = 0.70
loadDataset(r'/home/zhoumiao/ML/02KNearestNeighbor/irisdata.txt', split, trainingSet, testSet)
print 'Train set: ' + repr(len(trainingSet))
print 'Test set: ' + repr(len(testSet))
#generate predictions
predictions = []
k = 3
correct = []
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
result = getResponse(neighbors)
predictions.append(result)
#print ('test: ' + repr(testSet))
print ('predictions: ' + repr(predictions))
print ('>predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))

if result == testSet[x][-1]:
correct.append(x)
# print "len:"
# print len(testSet)
# print "correct:"
# print len(correct)
accuracy = (len(correct)/float(len(testSet)))*100.0
print('Accuracy: ' + repr(accuracy) + '%')

if __name__ == '__main__':
main()``````