本文实例为大家分享了python实现knn算法的具体代码，供大家参考，具体内容如下

knn算法描述

对需要分类的点依次执行以下操作：
1.计算已知类别数据集中每个点与该点之间的距离
2.按照距离递增顺序排序
3.选取与该点距离最近的k个点
4.确定前k个点所在类别出现的频率
5.返回前k个点出现频率最高的类别作为该点的预测分类

knn算法实现

数据处理

#从文件中读取数据，返回的数据和分类均为二维数组def loadDataSet(filename): dataSet = [] labels = [] fr = open(filename) for line in fr.readlines(): lineArr = line.strip().split(",") dataSet.append([float(lineArr[0]),float(lineArr[1])]) labels.append([float(lineArr[2])]) return dataSet , labels

knn算法

#计算两个向量之间的欧氏距离def calDist(X1 , X2): sum = 0 for x1 , x2 in zip(X1 , X2): sum += (x1 - x2) ** 2 return sum ** 0.5def knn(data , dataSet , labels , k): n = shape(dataSet)[0] for i in range(n): dist = calDist(data , dataSet[i]) #只记录两点之间的距离和已知点的类别 labels[i].append(dist) #按照距离递增排序 labels.sort(key=lambda x:x[1]) count = {} #统计每个类别出现的频率 for i in range(k): key = labels[i][0] if count.has_key(key): count[key] += 1 else : count[key] = 1 #按频率递减排序 sortCount = sorted(count.items(),key=lambda item:item[1],reverse=True) return sortCount[0][0]#返回频率最高的key，即label

结果测试

已知类别数据（来源于西瓜书+虚构）

0.697,0.460,1
0.774,0.376,1
0.720,0.330,1
0.634,0.264,1
0.608,0.318,1
0.556,0.215,1
0.403,0.237,1
0.481,0.149,1
0.437,0.211,1
0.525,0.186,1
0.666,0.091,0
0.639,0.161,0
0.657,0.198,0
0.593,0.042,0
0.719,0.103,0
0.671,0.196,0
0.703,0.121,0
0.614,0.116,0

绘图方法

def drawPoints(data , dataSet, labels): xcord1 = []; ycord1 = []; xcord2 = []; ycord2 = []; for i in range(shape(dataSet)[0]): if labels[i][0] == 0: xcord1.append(dataSet[i][0]) ycord1.append(dataSet[i][1]) if labels[i][0] == 1: xcord2.append(dataSet[i][0]) ycord2.append(dataSet[i][1]) fig = plt.figure() ax = fig.add_subplot(111) ax.scatter(xcord1, ycord1, s=30, c='blue', marker='s',label=0) ax.scatter(xcord2, ycord2, s=30, c='green',label=1) ax.scatter(data[0], data[1], s=30, c='red',label="testdata") plt.legend(loc='upper right') plt.show()

测试代码

dataSet , labels = loadDataSet('dataSet.txt')data = [0.6767,0.2122]drawPoints(data , dataSet, labels)newlabels = knn(data, dataSet , labels , 5)print newlabels

运行结果

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