K-means的python实现

import numpy as np
import matplotlib.pyplot as plt

# 加载数据
def loadDataSet(fileName):
data = np.loadtxt(fileName,delimiter='\t')
return data

# 欧氏距离计算
def distEclud(x,y):
return np.sqrt(np.sum((x-y)**2))  # 计算欧氏距离

# 为给定数据集构建一个包含K个随机质心的集合
def randCent(dataSet,k):
m,n = dataSet.shape
centroids = np.zeros((k,n))
for i in range(k):
index = int(np.random.uniform(0,m)) #
centroids[i,:] = dataSet[index,:]
return centroids

# k均值聚类
def KMeans(dataSet,k):

m = np.shape(dataSet)[0]  #行的数目
# 第一列存样本属于哪一簇
# 第二列存样本的到簇的中心点的误差
clusterAssment = np.mat(np.zeros((m,2)))
clusterChange = True

# 第1步 初始化centroids
centroids = randCent(dataSet,k)
while clusterChange:
clusterChange = False

# 遍历所有的样本（行数）
for i in range(m):
minDist = 100000.0
minIndex = -1

# 遍历所有的质心
#第2步 找出最近的质心
for j in range(k):
# 计算该样本到质心的欧式距离
distance = distEclud(centroids[j,:],dataSet[i,:])
if distance < minDist:
minDist = distance
minIndex = j
# 第 3 步：更新每一行样本所属的簇
if clusterAssment[i,0] != minIndex:
clusterChange = True
clusterAssment[i,:] = minIndex,minDist**2
#第 4 步：更新质心
for j in range(k):
pointsInCluster = dataSet[np.nonzero(clusterAssment[:,0].A == j)[0]]  # 获取簇类所有的点
centroids[j,:] = np.mean(pointsInCluster,axis=0)   # 对矩阵的行求均值

print("Congratulations,cluster complete!")
return centroids,clusterAssment

def showCluster(dataSet,k,centroids,clusterAssment):
m,n = dataSet.shape
if n != 2:
print("数据不是二维的")
return 1

mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
if k > len(mark):
print("k值太大了")
return 1

# 绘制所有的样本
for i in range(m):
markIndex = int(clusterAssment[i,0])
plt.plot(dataSet[i,0],dataSet[i,1],mark[markIndex])

mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
# 绘制质心
for i in range(k):
plt.plot(centroids[i,0],centroids[i,1],mark[i])

plt.show()

# 测试代码
import numpy as np
import matplotlib.pyplot as plt

dataSet = loadDataSet("C:\\machinelearninginaction\\Ch10\\testSet.txt")
k = 4
centroids,clusterAssment = KMeans(dataSet,k)

showCluster(dataSet,k,centroids,clusterAssment)

Congratulations,cluster complete!