Python 实现K-means算法

# coding=utf-8
import os
import sys
from pylab import *
import random
import types
cny = {}
def genRandom():
    f = rand(3,2)
    print "random number:",f
    return f
def loaddata():
    vals = []
    f = open("D:/kmeans.txt","r")
    while True:
        line = f.readline();
        if not line.strip():break
        if len(line) == 0: break
        vals.append(line.strip("\n").split("\t"))       
    f.close()
    return vals
def euclid(list1,list2):
    res = 0.0
    #print len(list1)
    for index in range(0,len(list1)):
        res += (float(list1[index]) - float(list2[index]))**2
    return sqrt(res)


'''
根据随机的点聚成原始的分类中心
'''
def classfy(rn,val):
    cls = {}
    for d in range(0,len(rn)):
        cls[d+1] = "" 
    for xx in val:
        max = 0
        count = 0
        for v in rn:
            fv = euclid(v,xx)
            if(fv > max): 
                count+=1
                max = fv
        if type(cls[count])  is types.StringType:
            cls[count] += ",".join(xx)+":"
    vc = {}
    for kin in cls.keys():
        ds = cls[kin]   
        vc[kin] = ds.split(":")
    dx = kmeans(vc)
    return rn,vc
'''
t1 0.38,t2 0.6
'''


def canopy(vals,t1,t2):
    
    rnum = random.randint(0,len(vals)-1)
    center1 = vals[rnum]
    del vals[rnum]
    ccny = []
    #ccny = {}
    #cstrong = []
    #cweak = []
    for vl in vals:
        dis = euclid(vl,center1)
        if  dis <= t1:
            #cstrong.append(vl)
            ccny.append(vl)
            vals.remove(vl)
        elif dis > t1 and dis <=t2:
            ccny.append(vl)
            #cweak.append(vl)
            #vals.remove(vl)
    #ccny[0] = cstrong
    #ccny[1] = cweak
    if not len(ccny) == 0:
       cny[",".join(center1)] = ccny
    print "the remaning size of vals:",len(vals) 
    if len(vals) == 0:
        return cny
    else:
        return canopy(vals,t1,t2)
    
def kmeans(vc):
    res = []
    for fh in vc:
        size = 0
        resultx = 0.0
        resulty = 0.0
        for dv in vc[fh]:
           dvs = dv.split(",")
           size += 1
           if len(dvs) == 2:
             resultx += float(dvs[0])
             resulty += float(dvs[1])
        ww = []
        ww.append(resultx/size)
        ww.append(resulty/size)
        res.append(ww)
    return res 
    


val = loaddata()
print  canopy(val,0.38,0.6)

数据kmeans.txt：

0.3 0.25
0.9 0.6
0.5 0.9
0.2 0.15
0.3 1.0
0.85 0.55
0.95 0.4
0.2 0.3
0.12 0.1
0.1 0.9
0.8 0.5
0.13 0.3
0.95 0.75
0.4 0.9
0.25 0.8
0.8 0.65
0.2 0.85
0.1 0.3