===分三类的=====

======分四类的========

直接写文件名，那么你的那个txt文件应该是和py文件在同一个路径的

 

============code===========

import numpy as np

from sklearn.cluster import KMeans

def loadData(filePath):

    fr = open(filePath,'r+')

    lines = fr.readlines()

    retData = []

    retCityName = []

    for line in lines:

        items = line.strip().split(",")

        retCityName.append(items[0])

        retData.append([float(items[i]) for i in range(1,len(items))])

    for i in range(1,len(items)):

        return retData,retCityName

if __name__ == '__main__':

    data,cityName=loadData('city.txt')

    km = KMeans(n_clusters=3)

    label = km.fit_predict(data)

    expenses = np.sum(km.cluster_centers_,axis=1)

    #print(expense)

    CityCluster =[[],[],[]]

    for i in range(len(cityName)):

        CityCluster[label[i]].append(cityName[i])

    for i in range(len(CityCluster)):

        print("Expenses:%.2f"%expenses[i])

        print(CityCluster[i])

    =========

1. import numpy as np
2. from sklearn.cluster import KMeans
3.  
4.  
5. def loadData(filePath):
6.     fr = open(filePath,'r+')
7.     lines = fr.readlines()
8.     retData = []
9.     retCityName = []
10.     for line in lines:
11.         items = line.strip().split(",")
12.         retCityName.append(items[0])
13.         retData.append([float(items[i]) for i in range(1,len(items))])
14.     return retData,retCityName
15.  
16.      
17. if __name__ == '__main__':
18.     data,cityName = loadData('city.txt')
19.     km = KMeans(n_clusters=4)
20.     label = km.fit_predict(data)
21.     expenses = np.sum(km.cluster_centers_,axis=1)
22.     #print(expenses)
23.     CityCluster = [[],[],[],[]]
24.     for i in range(len(cityName)):
25.         CityCluster[label[i]].append(cityName[i])
26.     for i in range(len(CityCluster)):
27.         print("Expenses:%.2f" % expenses[i])
28.         print(CityCluster[i])