k-means的简单实现

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', ' 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', '

dataSet = loadDataSet(“test.txt”)
k = 4
centroids,clusterAssment = KMeans(dataSet,k)

showCluster(dataSet,k,centroids,clusterAssment)