K-means聚类分析与python实现

# coding:utf-8 import numpy as np import pylab as pl import random as rd import imageio #计算平面两点的欧氏距离 step=0 color=['.r','.g','.b','.y']#颜色种类 dcolor=['*r','*g','*b','*y']#颜色种类 frames = [] def distance(a, b): return (a[0]- b[0]) ** 2 + (a[1] - b[1]) ** 2 #K均值算法 def k_means(x, y, k_count): count = len(x) #点的个数 #随机选择K个点 k = rd.sample(range(count), k_count) k_point = [[x[i], [y[i]]] for i in k] #保证有序 k_point.sort() global frames global step while True: km = [[] for i in range(k_count)] #存储每个簇的索引 #遍历所有点 for i in range(count): cp = [x[i], y[i]] #当前点 #计算cp点到所有质心的距离 _sse = [distance(k_point[j], cp) for j in range(k_count)] #cp点到那个质心最近 min_index = _sse.index(min(_sse)) #把cp点并入第i簇 km[min_index].append(i) #更换质心 step+=1 k_new = [] for i in range(k_count): _x = sum([x[j] for j in km[i]]) / len(km[i]) _y = sum([y[j] for j in km[i]]) / len(km[i]) k_new.append([_x, _y]) k_new.sort() #排序 #使用Matplotlab画图 pl.figure() pl.title("N=%d,k=%d iteration:%d"%(count,k_count,step)) for j in range(k_count): pl.plot([x[i] for i in km[j]], [y[i] for i in km[j]], color[j%4]) pl.plot(k_point[j][0], k_point[j][1], dcolor[j%4]) pl.savefig("1.jpg") frames.append(imageio.imread('1.jpg')) if (k_new != k_point):#一直循环直到聚类中心没有变化 k_point = k_new else: return km #计算SSE # def calc_sse(x, y, k_count): # count = len(x) #点的个数 # k = rd.sample(range(count), k_count) #随机选择K个点 # k_point = [[x[i], [y[i]]] for i in k] # k_point.sort() #保证有序 # #centroid # sse = [[] for i in range(k_count)] # while True: # ka = [[] for i in range(k_count)] #存储每个簇的索引 # sse = [[] for i in range(k_count)] # #遍历所有点 # for i in range(count): # cp = [x[i], y[i]] #当前点 # #计算cp点到所有质心的距离 # _sse = [distance(k_point[j], cp) for j in range(k_count)] # #cp点到那个质心最近 # min_index = _sse.index(min(_sse)) # #把cp点并入第i簇 # ka[min_index].append(i) # sse[min_index].append(min(_sse)) # #更换质心 # k_new = [] # for i in range(k_count): # _x = sum([x[j] for j in ka[i]]) / len(ka[i]) # _y = sum([y[j] for j in ka[i]]) / len(ka[i]) # k_new.append([_x, _y]) # k_new.sort() #排序 # #更换质心 # if (k_new != k_point): # k_point = k_new # else: # break # s =0 # for i in range(k_count): # s += sum(sse[i]) # return s x, y = np.loadtxt('2.csv', delimiter=',', unpack=True) k_count = 4 km = k_means(x, y, k_count) print step imageio.mimsave('k-means.gif', frames, 'GIF', duration = 0.5)