import numpy as np import sklearn.datasets #加载原数据 import matplotlib.pyplot as plt import random #点到各点距离 def PointToData(point,dataset): a = np.multiply(dataset - point,dataset - point) # print('a',a) distence = np.sqrt(a[:,0]+a[:,1]) return distence #选择初始的k个中心簇 def startpoint(k,dataset): m, n = np.shape(dataset) index1 = random.randint(0,len(dataset) - 1) A = [] # 初始的k个中心簇 A_dit = [] # 初始所有点到中心簇的距离 A.append(dataset[index1]) sum_dis = np.zeros((m, 1)) flag_mat = np.ones((m,1)) flag_mat[index1] = 0 for i in range(0, k - 1): A_dit.append((PointToData(A[i], dataset)).reshape(-1,1) ) # print('A_dit[{}]:{}'.format(i,A_dit[i])) sum_dis =(sum_dis + A_dit[i]) * flag_mat # print('sum_dis[{}]:{}'.format(i,sum_dis)) Index = np.argmax(sum_dis) flag_mat[Index] = 0 # print('选的Index：',Index) A.append(dataset[Index]) return A #加载数据 Data = sklearn.datasets.load_iris() dataset = Data.data[:,0:2] # #小数据测试编码 # test = dataset[0:15,:] # testm,testn = np.shape(test) # print(test) #测试k # k = 4 #初始点测试函数 # Apoint = startpoint(k,test) # print('Apoint',Apoint) #距离函数测试 # d = PointToData(test[0,:],test) # print('d,d+d:',d,d+d) def classfy(dataset,Apoint): m,n = np.shape(dataset) dis_li = [] num = 0 for point in Apoint: distence = PointToData(point,dataset) dis_li.append(distence) if num == 0: dis_li_mat = dis_li[num] else: dis_li_mat = np.column_stack((dis_li_mat,dis_li[num])) num += 1 result = np.argmin(dis_li_mat,axis=1) # print('dis_li:',dis_li) # print('dis_li_mat: ', dis_li_mat) # print('classfy:',result) return result # label2 = classfy(test,Apoint) # print('label2:',label2) #求分类的新中心 def Center(dataset,label,k): i = 0 newpoint = [] for index in range(k): flag = (label==index) # print('flag,i:',flag,i) num = sum(flag) # print('num:',num,index) a = flag.reshape(-1,1) * dataset newpoint.append(np.sum(a,axis = 0)/num) i += 1 # print(newpoint) return newpoint # testcenter = center(test,label2,k) # print('testcenter:',testcenter) #K-means主体函数 def myK(k,dataset): Startpoint = startpoint(k,dataset) m,n = np.shape(Startpoint) centerpoint = Startpoint labelset = classfy(dataset,Startpoint) newcenter = Center(dataset,labelset,k) # print('外:cecnterpoint', centerpoint) # print('外:newcenter', newcenter) flag = 0 for i in range(k): for j in range(n): if centerpoint[i][j] != newcenter[i][j]: flag = 1 while flag: print('循环') # print('里:cecnterpoint', centerpoint) # print('里:newcenter', newcenter) flag = 0 for i in range(k): for j in range(n): if centerpoint[i][j] != newcenter[i][j]: flag = 1 # print('flag:',flag) centerpoint = newcenter[:] labelset = classfy(dataset,centerpoint) newcenter = Center(dataset, labelset, k) # print('final_resultlabel:',labelset) # print('cenerpoint:', centerpoint) return labelset,centerpoint #测试 k=5 final_label,centerpoint = myK(k,dataset) print('centerpoint:',centerpoint) mat_center = np.mat(centerpoint) #画图 # plt.scatter(test[:,0],test[:,1],40,10*(labelset+1)) plt.scatter(dataset[:, 0], dataset[:, 1],40,10*(final_label+1)) plt.show()
