import kMeans

from numpy import *

# 导入txt数据

datMat = mat(kMeans.loadDataSet('data2.txt'))

# datMat矩阵的第2-4列分别对应半长轴、偏心率和轨道倾角

datMat[0,2:5]

# 计算距离

delta_v = kMeans.distdeltaV(datMat[0,2:5],datMat[1,2:5])

# 随机生成k个质心

centroids = kMeans.randCent(datMat[:,2:5], 4)

# k-均值聚类

myCentroids, clustAssing = kMeans.kMeans(datMat[:,2:5], 5, kMeans.distdeltaV)

# 二分 k-均值聚类

centList, myNewAssments = kMeans.biKmeans(datMat[:,2:5], 5, kMeans.distdeltaV)

# 画图

kMeans.showCluster_SRQ(datMat[:,2:5],myNewAssments)