使用python实现knn算法_Python

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 ** 0.5

									def 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

使用python实现knn算法

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持服务器之家。

原文链接：http://blog.csdn.net/chenge_j/article/details/72110652

使用python实现knn算法

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