为了判断未知实例的类别，以所有已知类别的实例作为参照

选择参数K

计算未知实例与所有已知实例的距离

选择最近K个已知实例

根据少数服从多数的投票法则(majority-voting)，让未知实例归类为K个最邻近样本中最多数的类别 

关于K

关于距离的衡量方法:

2.2.1 Euclidean Distance 定义

        

其他距离衡量：余弦值（cos）, 相关度 （correlation）, 曼哈顿距离 （Manhattan distance）

 

import csv
import random
import math
import operatordef loadDataset(filename, split, trainingSet = [], testSet = []):'''导入数据:param filename::param split: 将数据总集以split为界限 分成训练集和测试集:param trainingSet::param testSet::return:'''with open(filename, 'rt') as csvfile:       # 以逗号为分隔符lines = csv.reader(csvfile)             # 读取所有行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):'''计算euclideanDistance:param instance1::param instance2::param length: 维度:return:'''distance = 0for x in range(length):distance += pow((instance1[x]-instance2[x]), 2)return math.sqrt(distance)def getNeighbors(trainingSet, testInstance, k):'''返回最近的k个邻居:param trainingSet: 训练集:param testInstance: 一个测试实例:param k: 参数k:return:'''distances = []length = len(testInstance)-1for x in range(len(trainingSet)):#testinstancedist = euclideanDistance(testInstance, trainingSet[x], length)distances.append((trainingSet[x], dist))#distances.append(dist)distances.sort(key=operator.itemgetter(1))neighbors = []for x in range(k):neighbors.append(distances[x][0])return neighborsdef getResponse(neighbors):'''以距离排序，返回最近的几个点:param neighbors::return:'''classVotes = {}for x in range(len(neighbors)):response = neighbors[x][-1]if response in classVotes:classVotes[response] += 1else:classVotes[response] = 1sortedVotes = sorted(classVotes.items(), key=operator.itemgetter(1), reverse=True)  # python3 里的.items()返回的是列表，.iteritems()返回的是一个迭代器return sortedVotes[0][0]def getAccuracy(testSet, predictions):'''预测值和实际值的准确率:param testSet::param predictions::return:'''correct = 0for x in range(len(testSet)):if testSet[x][-1] == predictions[x]:correct += 1return (correct/float(len(testSet)))*100.0def main():#prepare datatrainingSet = []testSet = []split = 0.67loadDataset(r'irisdata.txt', split, trainingSet, testSet)print('Train set: ' + repr(len(trainingSet)))print('Test set: ' + repr(len(testSet)))#generate predictionspredictions = []k = 3for x in range(len(testSet)):# trainingsettrainingSet[x]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)+ '%')if __name__ == '__main__':main()

简单

易于理解

容易实现

通过对K的选择可具备丢噪音数据的健壮性          

需要大量空间储存所有已知实例

算法复杂度高（需要比较所有已知实例与要分类的实例）

当其样本分布不平衡时，比如其中一类样本过大（实例数量过多）占主导的时候，新的未知实例容易被归类为这个主导样本，因为这类样本实例的数量过大，但这个新的未知实例实际并木接近目标样本