Python实现C-均值聚类

*不保证正确性，仅作记录

# -*- coding: utf-8 -*-
#############################
# Author : 水娃 #
# Date : 2020-12-04-01.21.51#
#############################
#导入库
import numpy as np
import matplotlib.pyplot as plt
#计算两个样本之间的距离
def calculateDistance(posa,posb):
return np.sqrt(np.sum(np.power(posa-posb,2)))
#随机生成P个二维坐标
def getRandomData(P):
return np.array([[np.random.rand()*100 for j in range(0,2)] for i in range(0,P)])
#获取初始类心
def getInitCenters(data,mp,P,C):
#先把刚开始距离最远的两个点加入类心
maxdis=0.0
st1=-1
st2=-1
for i in range(0,P):
for j in range(0,P):
if mp[i][j]>maxdis:
st1=i
st2=j
maxdis=mp[i][j]
currentCenters=[st1,st2]
#算出其余类心
for i in range(0,C-2):
currentCandidate=-1
currentDistance=0;
for j in range (0,P):
if j in currentCenters:
continue
flag=True
maxDistance=200
for k in currentCenters:
if mp[j][k]<currentDistance:
flag=False
break
maxDistance=min(maxDistance,mp[j][k])
if flag==False:
continue
if maxDistance>currentDistance:
currentCandidate=j
currentDistance=maxDistance
currentCenters.append(currentCandidate)
return currentCenters
#获取初始聚类结果
def getOriginalCluster(data,centers,mp,P,C):
#按照最近距离原则将所有点聚类
result=[[] for i in range(0,C)]
for i in range(0,P):
distance=mp[i][centers[0]]
index=0
for j in range(0,C):
currentCenter=centers[j]
if mp[i][currentCenter]<distance:
distance=mp[i][currentCenter]
index=j
result[index].append(i)
return result
#C均值聚类
def Cmeans(data,P,C):
#获取距离列表，初始类心和初始聚类结果
mp=[[calculateDistance(data[i],data[j])for j in range(0,P)]for i in range(0,P)]
centers=getInitCenters(data,mp,P,C)
cluster=getOriginalCluster(data,centers,mp,P,C)
#将类心从索引转换成坐标
positionCenters=[[]for i in range(0,C)]
for i in range(0,C):
positionCenters[i].append(data[centers[i]][0])
positionCenters[i].append(data[centers[i]][1])
centers=positionCenters
#开始迭代聚类
haschanged=True
times=0
while haschanged:
times+=1
print(times)
tmpCluster=[[]for i in range(0,C)]
haschanged=False
#更新类心
for i in range(0,C):
totalx=0.0
totaly=0.0
for j in cluster[i]:
totalx+=data[j][0]
totaly+=data[j][1]
centers[i][0]=totalx/(1.0*len(cluster[i]))
centers[i][1]=totaly/(1.0*len(cluster[i]))
#更新聚类结果
for i in range(0,C):
for j in cluster[i]:
currentDistance=calculateDistance(centers[i],data[j])
currentIndex=i;
for k in range(0,C):
if calculateDistance(centers[k],data[j])<currentDistance:
currentDistance=calculateDistance(centers[k],data[j])
currentIndex=k
if currentIndex!=i:
haschanged=True
tmpCluster[currentIndex].append(j)
cluster=tmpCluster
return centers,cluster
#设定类的数目C和点数P
C=10
P=200
#获取随机数据
data=getRandomData(P)
#进行聚类获得结果
result,cluster=Cmeans(data,P,C)
#根据测试数据和结果点集绘制图形
for i in range(0,C):
pointx=[]
pointy=[]
for k in cluster[i]:
pointx.append(data[k][0])
pointy.append(data[k][1])
plt.scatter(pointx,pointy)
resultx=[result[i][0]for i in range(0,C)]
resulty=[result[i][1]for i in range(0,C)]
plt.scatter(resultx,resulty,marker='s',color='k')
plt.show()

本文地址：https://blog.csdn.net/qq_43807662/article/details/110633215