import numpy as np
from sklearn.cluster import KMeans

if__name__=='__main__':
    data,cityName=loadData('city.txt')
    km=KMeans(n_clusters=3)#定義聚類中心的數目為3的KMeans方法
    label=km.fit_predict(data)#呼叫上面的方法進行
    expenses=np.sum(km.cluster_centers_,axis=1)
    #print(expenses)
    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])

def loadData(filePath):
    fr=open(filePath,'r+')#以讀寫形式讀取檔案
    lines=fr.readlines()
    retData=[]#儲存城市的各項消費資訊
 

    retCityName=[]#儲存城市名稱
    for line in lines:
        items=line.strip().split(",")
        retCityName.append(items[0])
        retData.append([float(items[i])
        for i in range(1,len(items))])
    return retData,retCityName#返回城市的名稱和各項消費資訊。