python KMeans算法实例(调用python库以及自己实现Kmeans)
python KMeans算法实例(调用python库以及自己实现Kmeans)
varyshare 发表于11个月前
python KMeans算法实例(调用python库以及自己实现Kmeans)
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调用库使用KMeans算法对各省份消费水平进行分类

全国各省消费数据如下,本文写程序中利用loadData()函数从data.csv文件读取。

北京,2959.19,730.79,749.41,513.34,467.87,1141.82,478.42,457.64
天津,2459.77,495.47,697.33,302.87,284.19,735.97,570.84,305.08
河北,1495.63,515.90,362.37,285.32,272.95,540.58,364.91,188.63
山西,1406.33,477.77,290.15,208.57,201.50,414.72,281.84,212.10
内蒙古,1303.97,524.29,254.83,192.17,249.81,463.09,287.87,192.96
辽宁,1730.84,553.90,246.91,279.81,239.18,445.20,330.24,163.86
吉林,1561.86,492.42,200.49,218.36,220.69,459.62,360.48,147.76
黑龙江,1410.11,510.71,211.88,277.11,224.65,376.82,317.61,152.85
上海,3712.31,550.74,893.37,346.93,527.00,1034.98,720.33,462.03
江苏,2207.58,449.37,572.40,211.92,302.09,585.23,429.77,252.54
浙江,2629.16,557.32,689.73,435.69,514.66,795.87,575.76,323.36
安徽,1844.78,430.29,271.28,126.33,250.56,513.18,314.00,151.39
福建,2709.46,428.11,334.12,160.77,405.14,461.67,535.13,232.29
江西,1563.78,303.65,233.81,107.90,209.70,393.99,509.39,160.12
山东,1675.75,613.32,550.71,219.79,272.59,599.43,371.62,211.84
河南,1427.65,431.79,288.55,208.14,217.00,337.76,421.31,165.32
湖北,1783.43,511.88,282.84,201.01,237.60,617.74,523.52,182.52
湖南,1942.23,512.27,401.39,206.06,321.29,697.22,492.60,226.45
广东,3055.17,353.23,564.56,356.27,811.88,873.06,1082.82,420.81
广西,2033.87,300.82,338.65,157.78,329.06,621.74,587.02,218.27
海南,2057.86,186.44,202.72,171.79,329.65,477.17,312.93,279.19
重庆,2303.29,589.99,516.21,236.55,403.92,730.05,438.41,225.80
四川,1974.28,507.76,344.79,203.21,240.24,575.10,430.36,223.46
贵州,1673.82,437.75,461.61,153.32,254.66,445.59,346.11,191.48
云南,2194.25,537.01,369.07,249.54,290.84,561.91,407.70,330.95
西藏,2646.61,839.70,204.44,209.11,379.30,371.04,269.59,389.33
陕西,1472.95,390.89,447.95,259.51,230.61,490.90,469.10,191.34
甘肃,1525.57,472.98,328.90,219.86,206.65,449.69,249.66,228.19
青海,1654.69,437.77,258.78,303.00,244.93,479.53,288.56,236.51
宁夏,1375.46,480.89,273.84,317.32,251.08,424.75,228.73,195.93
新疆,1608.82,536.05,432.46,235.82,250.28,541.30,344.85,214.40

 实现步骤

  1. 加载数据
  2. 调用fit_predict()方法对数据归类
  3. 获取各省份所属类的标记
  4. 将同一个标记的省放在同一个列表
  5. 输出分类结果

需要注意的是:

  • 由于data.csv数据源文件有中文而且是utf-8编码所以打开文件编码也要是utf-8,然后python程序设置成utf-8,将分类结果(省份名称)写入到文件中也需要是utf-8编码
  • Numpy.average(arr,axis)要根据需求设置axis,axis=1表示按行计算,axis=2表示按列计算,不写就是将所有数求平均
# coding:utf-8
import numpy as np
from sklearn.cluster import KMeans


def loadData(filePath):
    """
    读取文件数据并返回消费数据和对应省份名称
    :param filePath: 数据文件路径
    :return: 各省消费数据,省份名称
    """
    file = open(filePath, 'r+', encoding='utf-8')  # 注意读文件的编码
    lines = file.readlines()
    fileData = []
    fileCityName = []
    for line in lines:
        items = line.strip().split(',')
        fileCityName.append(items[0])
        fileData.append([float(items[i]) for i in range(1, len(items))])
    file.close()
    return fileData, fileCityName


def saveData(filePath, data):
    """
    保存输出结果到指定路径下
    :param filePath: 保存结果的目的文件路径
    :param data: 结果数据
    :return:
    """
    file = open(filePath, 'w+',encoding='utf-8') # 注意编码
    file.write(str(data))
    file.close()


data, cityName = loadData('data.csv')
km = KMeans(n_clusters=3) # 将省份分3类
label = km.fit_predict(data) # 获取各省份所属的类编号
avgExpenses = np.average(km.cluster_centers_, axis=1)  # axis 1按行 2按列 求平均

# 根据label将相同分类省份名放置一起
CityCluster = [[], [], []]
for i in range(len(cityName)):
    CityCluster[label[i]].append(cityName[i])

resultStr = '' # 保存分类结果
# 输出分类结果
for i in range(len(CityCluster)):
    print("平均消费%0.2f" % (avgExpenses[i]))
    print(CityCluster[i])
    # 将同分类省份用,拼接
    resultStr = resultStr + ','.join(CityCluster[i]) + '\n'

# 保存分类结果
saveData('result.csv',resultStr)

 

 

自己实现Kmeans算法

kmeans步骤

  1. 加载数据,设置要分成k类
  2. 生成k个随机簇中心
  3. 遍历所有点找到离该点最近的簇中心
  4. 然后对所有簇进行求平均然后更新当前簇中心为平均值
from numpy import *


def loadDataSet(fileName):
    dataMat = []
    fr = open(fileName)
    for line in fr.readlines():
        curLine = line.strip().split('\t')
        arrLine = list(map(float, curLine))
        dataMat.append(arrLine)
    fr.close()
    return mat(dataMat)


def distEclud(vecA, vecB):
    return sqrt(sum(power(vecA - vecB, 2)))  # sqrt((x1-x2)^2+...)


def randCent(dataSet, k):
    n = shape(dataSet)[1]
    centroids = mat(zeros((k, n)))  # 用于保存随机生成的k个簇中心
    for j in range(n):  # 按列依次生成随机数
        minJ = min(dataSet[:, j])  # 获取每列最小值
        rangeJ = float(max(dataSet[:, j]) - minJ)
        centroids[:, j] = minJ + rangeJ * random.rand(k, 1)  # 生成k行1列的随机数
    return centroids


def kMeans(dataSet, k, distMeans=distEclud, createCent=randCent):
    m = shape(dataSet)[0]
    clusterAssment = mat(zeros((m, 2))) # 各行所属簇编号 离最近簇中心距离
    centroids = createCent(dataSet, k)  # 保存k个簇中心
    clusterChanged = True
    while clusterChanged:
        clusterChanged = False

        # 对每个点去找最近的簇中心
        for i in range(m):
            minDist = inf
            minIndex = -1
            # 从所有簇中心找离当前点最近的簇中心
            for j in range(k):
                distJI = distMeans(centroids[j, :], dataSet[i, :])
                if distJI < minDist:
                    minDist = distJI
                    minIndex = j
                if clusterAssment[i, 0] != minIndex:
                    clusterChanged = True
                clusterAssment[i, :] = minIndex, minDist

        for cent in range(k):
            # 获取cent簇所有点
            lines = nonzero(clusterAssment[:, 0].A == cent)[0] # 找簇等于cent的点对应的行号
            ptsInClust = dataSet[lines]

            # 对当前簇所有点求平均
            centroids[cent, :] = mean(ptsInClust, axis=0)
        return centroids, clusterAssment


myMat = loadDataSet("data.txt")
clusterNum = 3
centroids, clusterAssment = kMeans(myMat, clusterNum)
print('簇中心\n',centroids)
for label in range(clusterNum):
    print(label,"\n",myMat[nonzero(clusterAssment[:,0]==label)[0]])

data.txt数据

3.792121	5.135768
-4.786473	3.358547
2.624081	-3.260715
-4.009299	-2.978115
2.493525	1.963710
-2.513661	2.642162
1.864375	-3.176309
-3.171184	-3.572452
2.894220	2.489128
-2.562539	2.884438
3.491078	-3.947487
-2.565729	-2.012114
3.332948	3.983102
-1.616805	3.573188
2.280615	-2.559444
-2.651229	-3.103198
2.321395	3.154987
-1.685703	2.939697
3.031012	-3.620252
-4.599622	-2.185829
4.196223	1.126677
-2.133863	3.093686
4.668892	-2.562705
-2.793241	-2.149706
2.884105	3.043438
-2.967647	2.848696
4.479332	-1.764772
-4.905566	-2.911070

输出结果:

簇中心
 [[-3.06875436  0.17342357]
 [ 2.99334217 -3.18781867]
 [ 3.29923363  2.39150475]]
0 
 [[-4.786473  3.358547]
 [-4.009299 -2.978115]
 [-2.513661  2.642162]
 [-3.171184 -3.572452]
 [-2.562539  2.884438]
 [-2.565729 -2.012114]
 [-1.616805  3.573188]
 [-2.651229 -3.103198]
 [-1.685703  2.939697]
 [-4.599622 -2.185829]
 [-2.133863  3.093686]
 [-2.793241 -2.149706]
 [-2.967647  2.848696]
 [-4.905566 -2.91107 ]]
1 
 [[ 2.624081 -3.260715]
 [ 1.864375 -3.176309]
 [ 3.491078 -3.947487]
 [ 2.280615 -2.559444]
 [ 3.031012 -3.620252]
 [ 4.668892 -2.562705]]
2 
 [[ 3.792121  5.135768]
 [ 2.493525  1.96371 ]
 [ 2.89422   2.489128]
 [ 3.332948  3.983102]
 [ 2.321395  3.154987]
 [ 4.196223  1.126677]
 [ 2.884105  3.043438]
 [ 4.479332 -1.764772]]

 

标签: kmeans python
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