knn算法实例（python）

import csv
import random
import math
import operator

def loadDataset(filename,split,trainingSet=[],testSet=[]):
# 注意这儿加上'b'模式会出错，因为csv文件与普通文件不一样
with open(filename, 'r') 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:
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)

# test for function euclideanDistance
# data1 = [2, 2, 2, 'a']
# data2 = [4, 4, 4, 'b']
# distance = euclideanDistance(data1, data2, 3)
# print(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))
# print(distances)
distances.sort(key=operator.itemgetter(1))
# print(distances)
neighbors = []
for x in range(k):
neighbors.append(distances[x][0])
return neighbors

# test for function getNeighbors
# trainSet = [[2, 2, 2, 'a'], [4, 4, 4, 'b'],[4.5, 4.5, 4.5, 'c']]
# testInstance = [5, 5, 5]
# k = 1
# neighbors = getNeighbors(trainSet, testInstance, 1)
# print(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
# py3.+使用 items() 与2.+的 iteritems()  不同
sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)
return sortedVotes[0][0]

# test for function getResponse
# neighbors = [[1, 1, 1, 'a'], [2, 2, 2, 'a'], [3, 3, 3, 'b']]
# response = getResponse(neighbors)
# print(response)

def getAccuracy(testSet, predictions):
correct = 0
for x in range(len(testSet)):
if testSet[x][-1] == predictions[x]:

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correct += 1
return (correct/float(len(testSet)))*100.0

def main():
# prepare data
trainingSet = []
testSet = []
loadDataset('f:/iris.csv', 0.66, trainingSet, testSet)
print("Train" + repr(len(trainingSet)))
print("Test" + repr(len(testSet)))
# print(trainingSet)

# generate predictions
predictions = []
k = 3
for x in range(len(testSet)):
neighbors = getNeighbors(trainingSet, testSet[x], k)
# print(neighbors)
result = getResponse(neighbors)
predictions.append(result)
print('> predicted=' + repr(result) + ', actual=' + repr(testSet[x][-1]))
accuraty = getAccuracy(testSet, predictions)
print('Accuracy: ' + repr(accuraty) + '%')

main()